ALBERT

Description: Since mineral supplements for horses commonly contain macro minerals, although the requirement for such is usually covered by roughage-based diets, the aim of this study was to investigate the effects of different dietary calcium levels on mineral serum concentrations and bone metabolism. The trial was conducted in 30 young warmblood stallions (2–3 years) that were divided into two groups for a five-month feeding trial. The groups were fed a hay- and oat-based diet and were either supplied with high (Ca-High) or moderate (Ca-Moderate) calcium excess. While in Ca-High calcium supply was about 2–2.5-fold of the requirement, in Ca-Moderate calcium requirements were slightly surpassed (1.5–1.6-fold). In order to monitor the effects of the different calcium supply, blood samples were taken during the trial and analysed for levels of macro and trace elements as well as concentrations of two bone markers. In Ca-Moderate a trend towards higher phosphorus serum levels compared to Ca-High was observed which was significant at the end of the trial (p = 0.0002). Furthermore, results showed no influence of the diet on bone markers. Results support the idea that forage-based rations for horses do not necessarily have to be supplemented with macro minerals but with trace elements.

Description: Transport infrastructure (TI) plays a crucial role in socioeconomic development. The increase of TI inequality, an all-pervading phenomenon in both developed and developing countries, has been an obstacle to sustainable economic growth. The relationship between TI inequality and economic growth has attracted considerable interest over the past three decades. However, the relationship remains obscure, and people find it impossible to utilize to develop economies. This study collected a panel of empirical data from 1982 to 2015 from China to calculate the Gini coefficient and conduct the Granger causality test. The data analysis results show that TI inequality is not always conducive to economic growth. A softening TI inequality helps address the issues of uneven economic growth across regions in the long term. The short-term effects of improving TI inequality at the national level are reflected in the network effect. In addition, the “social filters” facilitate the region to absorb the economic benefits brought by the improvement of TI inequality. These findings offer a way to address the increase of TI inequality and shed light on the ways to improve transport investment from the perspective of economic growth.

Description: This case series study aimed to evaluate the safety, feasibility, and positive outcome of the neurorehabilitation multimodal protocol (NRMP) in 16 chronic post-surgical IVDD Hansen type I dogs, with OFS 0/DPP− (n = 9) and OFS 1/DPP+ (n = 7). All were enrolled in the NRMP for a maximum of 90 days and were clinically discharged after achieving ambulation. The NRMP was based on locomotor training, functional electrical stimulation, transcutaneous electrical spinal cord stimulation, and 4-aminopyridine (4-AP) pharmacological management. In the Deep Pain Perception (DPP)+ dogs, 100% recovered ambulation within a mean period of 47 days, reaching OFS ≥11, which suggests that a longer period of time is needed for recovery. At follow-up, all dogs presented a positive evolution with voluntary micturition. Of the DPP− dogs admitted, all achieved a flexion/extension locomotor pattern within 30 days, and after starting the 4-AP, two dogs were discharged at outcome day 45, with 78% obtaining Spinal Reflex Locomotion (SRL) and automatic micturition within a mean period of 62 days. At follow-up, all dogs maintained their neurological status. After the NRMP, ambulatory status was achieved in 88% (14/16) of dogs, without concurrent events. Thus, an NRMP may be an important therapeutic option to reduce the need for euthanasia in the clinical setting.

Description: The threshing of frozen corn is accompanied by breakage and adherence, which influence the cleaning performance when the corn-cleaning mixture is separated and cleaned. In order to reduce the impurity ratio and loss ratio during frozen corn cleaning and provide theoretical support for frozen corn combine harvesting, this study employed a self-made air-screen cleaning system with adjustable parameters. The optimal process parameters of frozen corn cleaning were determined by using the response surface method (RSM). The influences of the fan speed (FS), vibrational frequency (VF), and screen opening (SO) on the cleaning performance were explored. The results showed that all three process parameters had significant effects on the impurity ratio (IR) and loss ratio (LR). The fan speed had the most significant impact. The cleaning performance was optimal when the fan speed was 102.7 rad/s, the vibration frequency was 6.42 Hz, and the screen opening was 21.9 mm, corresponding to a 0.80% impurity ratio and a 0.61% loss ratio. The predicted values of the regression models were consistent with the experimental results with a relative error of less than 5%. The reliability and accuracy of regression models were established and confirmed.

Description: The muskrat is a neozoon species that has occupied many countries of continental North Europe after its introduction from north America as fur animals. Due to its burrowing activity it damages river and canal banks and structures of flood control. For this reason, the eradication of this alien species is recommended. Muskrats are also of parasitological interest since they can act as suitable intermediate hosts for Echinococcus multilocularis. On the other hand, little is known on the other helminths that infect muskrats. A total of 130 muskrats of different age groups trapped in different habitats in the Barnim district of the Brandenburg state by a professional hunter were examined for parasites and seven trematodes (Echinostoma sp., Notocotylus noyeri, Plagiorchis elegans, Plagiorchis arvicolae, Psilosostoma simillimum, P. spiculigerum, Opisthorchis felineus and four larval cestode species (Hydatigera taeniaeformis, Taenia martis, Taenia polyacantha, Taenia crassiceps) were detected. Larval stages of E. multilocularis were not found. O. felineus was found for the first time in muskrats in Germany. All the named parasites were present in Europe prior to the introduction of muskrats. With a prevalence of 48.9%, Strobilocercus fasciolaris, the larval stage of the cat tapeworm, H. taeniaeformis, was the most frequent parasite found in adult muskrats.

Description: Water uptake is a seminal process in seed germination. Salt and polyethylene glycol (PEG) are known to retard seed germination rates and percentages, which is often attributed to osmotic effects. Here, we quantified water uptake in wheat seeds killed with a hot needle, finding evidence of three distinct water uptake pools. The fast pool was unaffected by salt, and likely represents cell walls and other apoplastic material. Water uptake into the medium and slow pools was slowed by salt addition, with the medium pool thought to be cellular, while the slow pool is presumably related to endosperm hydration. Salt caused a minor decrease in the water uptake rates and maximum seed water content, while PEG strongly suppressed both parameters. Seeds transferred between water and salt solutions followed the water uptake trajectories of the solution into which they were transferred. Seeds transferred from PEG to water achieved final seed water contents similar to water control seeds, while seeds transferred from water to PEG achieved significantly higher final water contents than PEG controls. This work confirms that salt and PEG have distinct effects on water uptake by wheat seeds.

Description: The south of Portugal is one of the regions that will be most affected by the impacts of climate change (CC), with an expected increase in water scarcity. Irrigated super-intensive olive orchards occupy a large area of the used agricultural surface in the Alentejo region, south of Portugal, making it necessary to adapt this crop to the effects of CC. This study assessed the impacts of CC and defined adaptation measures concerning irrigation management of the super-intensive olive orchard. To compute the crop irrigation requirement (CIR), the soil water balance model ISAREG was combined with climate data relative to the reference period 1971–2000 and to the representative concentration pathways RCP4.5 and RCP8.5 for the periods 2011–2040, 2041–2070, and 2071–2100. The growing degree-days (GDD) approach was used to estimate olive phenology for these CC scenarios. Unchanged irrigation management with an average CIR increase up to 16% in RCP4.5 and 31% in RCP8.5 is expected. By adopting higher levels of water deficit, water savings of up to 22% can be realized. The results showed that the anticipated CIR increase for the CC scenarios can be mitigated through regulated deficit irrigation strategies.

Description: 5-aminolevulinic acid (ALA), a key precursor in the biosynthesis of porphyrins, can improve plant tolerance to various environmental stresses. However, it is unclear whether ALA can improve tolerance in poplar. Here, we investigated the effects of ALA on poplars under drought and salt stresses. ALA pretreatment exhibited less morphological damage, reduced leaf malonaldehyde content (MDA) and electrolyte leakage (EL), and increased leaf relative water content (RWC), proline (PRO), superoxide dismutase (SOD), and peroxidase (POD) content under stresses. Furthermore, exogenous ALA mitigated the decrease in photosynthetic capacity, and restored the chlorophyll content (Chl), net CO2 assimilation rate, stomatal conductance (Gs), transpiration rate (Tr), maximal photochemical quantum yield of PSII (Fv/Fm), actual quantum yield of photosynthesis (YII), and electron transfer rate (ETR) of poplar under various stresses. qRT-PCR showed that ALA up-regulated the expression of antiporters and aquaporins genes, which are associated with Na+ exclusion in the leaf cells and the transport activity of aquaporins. In summary, ALA pretreatment significantly improved the stress tolerance of poplar, decreasing the degree of membrane lipid peroxidation and promoting the photosynthesis and antioxidant capacity of leaves. In addition, our results showed that ALA might mediate Na+ transporter and aquaporins activity, thereby increasing the salt tolerance of poplar.

Description: This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

Description: Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.

Description: A field experiment was conducted to investigate the effectiveness of different herbicides for controlling wild onion (Asphodelus tenuifolius) in cumin (Cuminum cyminum L.) during the rabi seasons (2018–2019 and 2019–2020) at Agricultural Research Station, Agriculture University, Jodhpur, Rajasthan. The experiment comprised eight herbicidal weed management treatments for wild onion applied to cumin in a three-replication randomized block design. Among the herbicidal weed management treatments, early post-emergence (8 DAS) application of oxyfluorfen 200 g/ha resulted in the lowest weed density and dry matter of Asphodilus tenuifolius, with maximum weed (Asphodilus tenuifolius) control efficiency at 40 days after sowing (DAS) during both experimental years. Likewise, the highest total efficiency of weed control was recorded with the application of oxyfluorfen 200 g/ha at 8 DAS. Oxyflourfen 200 g/ha used early post emergence (8 DAS) reduced the weed index more effectively than the other herbicides. It also recorded the highest number of branches/plant, plant height, umbels/plant, umbellates/umbel, seeds/umbellates, and seed yield. However, application of oxyflourfen @ 200 g/ha 8 DAS–early POE and pendimethalin 38.7 CS 500 g/ha + oxyfluorfen @ 150 g/ha 8 DAS–early POE were statistically similar in terms of plant growth, yield, and yield attributes. The net returns (366.49 USD/ha in 2018–2019 and 175.72 USD/ha in 2019–2020) and B:C ratio (1.70 and 1.33 in 2018–2019 and 2019–2020, respectively) were also superior, with oxyfluorfen 200 g/ha applied early post emergence.

Description: This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

Description: This study evaluated the effects of the duration of ZH supplementation and days on feed (DOF) on performance, carcass characteristics, and saleable meat yield of Nellore young bulls. The fixed effects included the duration (0, 20, 30, or 40 d before slaughter plus a 3 d ZH withdrawal period—8.33 mg of ZH/kg of DM) and DOF (90 and 117 d). Feed efficiency (G:F) linearly increased when the duration of ZH supplementation increased (p 〈 0.01). Nellore bulls fed ZH had greater HCW (p 〈 0.01), dressing percentage (p 〈 0.01) and Longissimus muscle area (LMA) (p 〈 0.01), but less 12th-rib fat (p = 0.04) than the control group. The hot carcass weight (HCW) (p 〈 0.01), and dressing percentage increased linearly (p 〈 0.01) with the increase of duration of ZH supplementation. The HCW, ossification, and 12th-rib fat increased with DOF (p 〈 0.01). The ZH supplemented group had most of the individual cuts of hindquarters and total saleable meat increased compared with the control. Zilpaterol hydrochloride was effective in improving hot carcass weight, hindquarter, and saleable meat yields of Nellore bulls when fed for at least 20 d before slaughter, independently of days on feed.

Description: Healthy, weaned, coccidial-free male rabbits from two breeds (New Zealand white (NZ) and V-line (VL)) were divided into 10 equal groups (5 groups each for NZ and VL) (3 replicates/group, 6 rabbits/replicate, 18 rabbits/group). All rabbits were inoculated with 5 × 104 Eimeria spp. oocysts (E. intestinalis (67%), E. magna (22%), and E. media (11%)) except for the rabbits in the first group (G1), which were inoculated with a sterile solution and served as a negative control. The remaining four groups were treated as follows: G2, no treatment/positive control, G3, treated with neem leaf extract, G4, treated with pomegranate peel extract (PPE), and G5, treated with a combination of neem leaf extract and PPE. For both breeds, our results showed that the use of neem leaf and/or pomegranate peel extract resulted in improved growth performance, with a significant improvement in relative feed conversion ratio (FCR) compared to the positive control groups, which recorded the worst values, as well as a significant (p ≤ 0.05) reduction in mean oocyst count compared to the positive control groups. We also observed downregulation of mRNA levels of IL-1βα, IL6, and TNF-α in the herbal treatment groups compared with the mRNA levels of these genes in the positive control groups. Herbal treatment with neem leaf and/or pomegranate peel extracts had positive effects on the NZ and VL rabbits experimentally infected with mixed Eimeria species, as evidenced by their healthy appearance, good appetite, no mortalities, an anticoccidial index 〉 120, and a significantly higher total return and net profit when compared to the positive control groups of both breeds. In NZ rabbits, the treatment with neem leaf extract alone (G3) or in combination with PPE (G5) recorded the most efficient economic anticoccidial activity.

Description: The short form of the leptin receptor (LRa) plays a key role in the transport of leptin to the central nervous system (CNS). Here, the resistin (RSTN)-mediated expression of LRa in the preoptic area (POA), ventromedial and dorsomedial nuclei (VMH/DMH),arcuate nucleus (ARC) and the anterior pituitary gland (AP)was analyzed considering the photoperiodic (experiment 1) and nutritional status (experiment 2) of ewes. In experiment 1, 30 sheep were fed normally and received one injection of saline or two doses of RSTN one hour prior to euthanasia. RSTN increased LRa expression mainly in the ARC and AP during long days (LD) and only in the AP during short days (SD). In experiment 2, an altered diet for 5 months created lean or fat sheep. Twenty sheep were divided into four groups: the lean and fat groups were given saline, while the lean-R and fat-R groups received RSTN one hour prior to euthanasia. Changes in adiposity influenced the effect of RSTN on LRa mRNA transcript levels in the POA, ARC and AP and without detection of LRa in the VMH/DMH. Overall, both photoperiodic and nutritional signals influence the effects of RSTN on leptin transport to the CNS and are involved in the adaptive/pathological phenomenon of leptin resistance in sheep.

Description: One important type of animal welfare concern is “natural living” (i.e., that animals are able to express natural behaviours that are important to them, and to engage with aspects of the natural world that they find important). The aims of this narrative review were to describe the behavioural development of calves (Bos taurus) in natural settings and use this to identify characteristics of natural systems that may be important to consider relative to this natural living conception of animal welfare. At birth, calves are licked by their mothers and soon stand to suckle for colostrum, and during the milk-feeding period, calves spend much of their time lying down. In natural systems, calves perform a variety of social behaviours with herd-mates, and slowly transition from their mother’s milk to eating solid food, by gradually increasing time spent grazing and ruminating. In contrast, on most commercial dairy systems, dairy calves are removed from their mothers at birth, housed individually, fed restricted amounts of milk and weaned abruptly at a young age. The results of this review suggest that accommodating key natural behaviours, for example through the use of teat feeding of milk, social housing, and gradual weaning, can help address welfare concerns.

Description: The aim of the present research was to analyze the farmers’ intention towards participation in the management and conservation of wetlands through the lens of the extended theory of planned behavior (TPB). To do this, a cross-sectional survey of Iranian farmers was carried out. To select the samples, a multi-stage random sampling process with a proportional assignment was employed. The research instrument was a researcher-made questionnaire whose validity and reliability were verified using various quantitative and qualitative indicators. The results of the extended TPB using structural equation modeling showed that four variables, namely moral norms of participation in management and conservation (MNPMC), attitude towards participation in management and conservation (APMC), subjective norms towards participation in management and conservation (SNPMC), and self-concept about participation in management and conservation (SCPMC) had positive and significant impacts on intention towards participation in management and conservation (IPMC). The results also revealed that that entering MNPMC and SCPMC into TPB could increase its explanatory power. Also, the fit indicators supported the extended TPB. From a practical point of view, the present study provides justifications and insights for the use of MNPMC, APMC, SNPMC, and SCPMC in policies and programs intended to encourage farmers and local communities to participate in wetlands management and conservation.

Description: Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.

Description: A previous study revealed low survival rates for Japanese larch (Larix kaempferi) summer-planted seedlings grown in Hiko-V-120 containers. This study examines nursery practices that could potentially prevent deterioration of the seedling water balance after planting to improve the survival rate of this species, which has a low drought tolerance. During summer planting, we tested (1) drought hardening or high-potassium fertilization for two months before planting, (2) antitranspirant or topping treatment at planting, and (3) the use of the JFA-150 container with a larger capacity and lower growing density than the Hiko-V-120 container. Drought hardening increased seedling drought tolerance because of the low leaf:root ratio, due to lower leaf mass production, resulting in increased survival from 74% to 93% in Hiko-V-120 containers. When JFA-150 containers were used, the leaf:root ratio was lower because of higher root mass, resulting in an increase in survival to 87%, with the highest survival of 97% when combined with drought hardening. The application of antitranspirant increased survival to over 90%, whereas topping did not, probably because of severer competition from weeds. High-potassium fertilization did not affect seedling traits or survival. For better survival of summer-planted container-grown Japanese larch seedlings, it is recommended that they be grown in containers providing sufficient cell volume and density for root growth while the seedlings are in the nursery and that irrigation be withheld for two months before planting. In addition, to obtain higher survival, an antitranspirant can be applied at planting at a cost.

Description: Contagious agalactia is associated with mastitis, keratoconjunctivitis, arthritis, pneumonia, and septicemia in small ruminants in countries with large dairy industries worldwide. The causative agents belong to four (sub)species of the Mycoplasma genus that have remained essentially susceptible to antimicrobials, including to the widely-used tetracycline family. However, some clinical isolates have been detected that show increased minimum inhibitory concentrations of tetracyclines, although they do not harbor the mutation in the 16SrRNA gene usually associated with resistance. The present work aimed to assess whether efflux pumps, infrequently described in mycoplasmas, could participate in the observed moderate loss of susceptibility. General efflux mechanisms were measured (i) using the fluorescence property of ethidium bromide when accumulated intracellularly and intercalated in the mycoplasma genomes, its active extrusion resulting in a temperature-dependent decrease in fluorescence and (ii) monitoring the growth inhibition of mycoplasmas by subinhibitory concentrations of tetracycline with or without reserpine, a known inhibitor of efflux in other bacteria. Both methods revealed non-specific efflux phenomena in most of the isolates tested, although their efficacy was difficult to quantify. This property could contribute to the acquisition of mutations conferring resistance by maintaining intracellular concentrations of tetracyclines at subinhibitory levels.

Description: Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

Description: The characteristics of bridge pile-group foundation have a significant influence on the dynamic performance of the superstructure. Most of the existing analysis methods for the pile-group foundation impedance take the trait of strong specialty, which cannot be generalized in practical projects. Therefore, a project-oriented numerical solution algorithm is proposed to compute the dynamic impedance of bridge pile-group foundation. Based on the theory of viscous-spring artificial boundary, the derivation and solution of the impedance function are transferred to numerical modeling and harmonic analysis, which can be carried out through the finite element method. By taking a typical pile-group foundation as a case study, the results based on the algorithm are compared with those from existing literature. Moreover, an impact experiment of a real pile-group foundation was implemented, the results of which are also compared with those resulting from the proposed numerical algorithm. Both comparisons show that the proposed numerical algorithm satisfies engineering precision, thus showing good effectiveness in application.

Description: Urban greenery is widely acknowledged as a key element for creating livable urban environments and improving residents’ quality of life. However, only a few current studies on the subject of urban greenery focus on a human visual perspective and take street greenery into consideration. Street greenery is an indispensable component of urban vegetation to which residents have a higher frequency of access. Additionally, few studies focused on the disparity of the green view at a micro-level, such as at a county or community level. This study explored the spatial distribution of street greenery and its influential factors using the green view index (GVI) as the main evaluation indicator. Compared to other traditional indicators of greenery, such as the normalized difference vegetation index (NDVI) and accessibility, GVI is recognized as a human-oriented indicator to evaluate the quantity of greenery viewed by human eyes in daily life. The downtown area of Shanghai was chosen as the case study, as it reflects the common phenomenon of street greenery in many megacities globally. In addition, county/jiedao (the same administrative area as county in China) level was selected as the minimum geographical unit to evaluate the disparity of GVI and its influential factors to fill the knowledge gap. We analyzed 233,000 pieces of street-view images from Baidu Map and other correlated data. The results showed (1) the street greenery of 70% of the downtown area of Shanghai is less than the recommended comforFogre visual environment; (2) street greenery is spatially clustered in Huangpu district, Xuhui district, college town, and the Century Park of Shanghai; (3) street-greenery distribution is positively correlated with housing price and street network density, and negatively correlated with the ratio of society vulnerability; however, it is uncorrelated to population density. According to these findings, local municipalities could improve urban planning and design by introducing a more human-oriented green-space policy that improves social equity.

Description: Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.

Description: Climate change has inherent multidisciplinary characteristics, and predicting the future of a single field of work has a limit. Therefore, this study proposes a water-centric nexus approach for the agriculture and forest sectors for improving the response to climate change in the Korean Peninsula. Two spatial models, i.e., Environmental Policy Integrated Climate and Integrated Valuation of Ecosystem Services and Tradeoffs, were used to assess the extent of changes in agricultural water demand, forest water supply, and their balance at the watershed level in the current and future climatic conditions. Climate changed has increased the agricultural water demand and forest water supply significantly in all future scenarios and periods. Comparing the results with RCP8.5 2070s and the baseline, the agricultural water demand and forest water supply increased by 35% and 28%, respectively. Water balance assessment at the main watershed level in the Korean Peninsula revealed that although most scenarios of the future water supply increases offset the demand growth, a risk to water balance exists in case of a low forest ratio or smaller watershed. For instance, the western plains, which are the granary regions of South and North Korea, indicate a higher risk than other areas. These results show that the land-use balance can be an essential factor in a water-centric adaptation to climate change. Ultimately, the water-centric nexus approach can make synergies by overcoming increasing water demands attributable to climate change.

Description: Sex differences in a variety of cognitive traits have long been reported in various species, including dogs. However, only a few canine studies have taken the possible effect of reproductive hormones into account. The aim of this study was to investigate the effects of sex and reproductive status of pet dogs (N = 1032) on two cognitive traits: inhibitory control and social cognition. Inhibitory control was assessed using the cylinder test, and the dogs’ tendency to initiate social contact with a human during a problem-solving situation was assessed using the unsolvable task. Female dogs had a significantly higher success rate in the cylinder test compared to males, and they spent significantly more time in human-directed behavior during the unsolvable task. In contrast, males spent significantly more time in independent behavior during the unsolvable task. Reproductive status had no significant effect on the results of the cylinder test or the unsolvable task. Our results showed that female dogs asked for more help/used a more cooperative strategy during a problem-solving situation and had greater inhibitory control compared to males. According to our results, it seems likely that these sex differences were not influenced to a large extent by reproductive hormones.

Description: Heritabilities of workability (WT) traits—milking speed (MS) and temperament (MT)—as well as genetic and phenotypic correlations between these traits in the population of Polish Holstein-Friesian (PHF) cows were estimated. The estimation of genetic parameters was performed twice: first with the use of pedigree data; and second with the use of pedigree and genomic data. Phenotypic data from routinely conducted MS and MT evaluations for 1,045,511 cows born from 2004 to 2013 were available; the cows were evaluated from 2011 to 2015. The main dataset was reduced based on imposed restrictions (e.g., on age of calving, stage of lactation and day of first trial milking). The dataset prepared in this manner comprised 391,615 cows. It was then reduced to daughters of 10% randomly selected sires for computational reasons. Finally, for genetic parameter estimation, 13,280 records of cows were used. The linear observation model included additive random effects of animal, fixed effects of herd-year-season of calving subclass (HYS) and lactation phase, fixed regressions on cow age at calving and the percent of HF breed genes in the cow genotype. Heritabilities estimated based on pedigree data were 0.12 (±0.0067) for MS and 0.08 (±0.0063) for MT, the genetic correlation between MS and MT was estimated at 0.05 (±0.0002) and the phenotypic correlation coefficient was estimated at 0.14 (±0.0004). The inclusion of genomic information of sire bulls had no clear effect on the size of the estimated WT genetic parameters. The heritabilities of MS and MT were 0.11 (±0.0065) and 0.09 (±0.0012), respectively. The genetic and phenotypic correlation coefficients were 0.07 (±0.0003) and 0.12 (±0.0005), respectively. The sizes of the obtained heritabilities of WT and of the genetic and phenotypic correlation between these traits indicate the possibility of effective population improvement for both WT traits.

Description: Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.

Description: Motivation Accurate automatic annotation of protein function relies on both innovative models and robust data sets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. Results Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. Code and Data Availability The data and results for this paper can be found at https://doi.org/10.5281/zenodo.5153906. The code for this paper can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.

Description: This work aimed to (i) determine the reliability of a portable Bluetooth colourimeter for fruit colour measurements; (ii) characterise the changes in quantitative skin colour attributes in a nectarine cultivar in response to time from harvest; and (iii) determine the influence of row orientation and training system on nectarine skin colour. The skin colour attributes measured with the colourimeter, namely L*, a* and b*, were calibrated and validated against a reference spectrophotometer. C* and h° were obtained from a* and b*. Skin colour was measured in situ from 42 days before to 6 days after harvest on ‘Majestic Pearl’ nectarines subjected to different row orientations and training systems. Validation models showed high reliability of colour estimations. The trends of colour attributes over time were characterised by cubic regression models, with h° proving to be the best parameter to describe changes of colour over time, with a clear link to the maturation process. No significant effects of row orientation and training system on skin colour were observed at harvest. Overall, the device proved reliable for fruit colour detection. Results of this study highlight the potential of h° as a quantitative index to monitor ripening prior to harvest in ‘Majestic Pearl’ nectarines.

Description: Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Description: Cucumber, a widely cultivated vegetable, is mostly grown under greenhouse conditions. In recent years, the overuse of inorganic fertilizers for higher yield attainment adversely has affected human health and the environment. Therefore, a greenhouse experiment was designed to evaluate the effects of different nutrient sources (poultry manure (PM) and mineral fertilizer (MF)) on productivity-enhancing parameters of cucumber via univariate and multivariate analyses. Amounts of PM and MF (NPK15:15:15) were added to coco-peat per cubic meter by weight/volume (w/v) ratios as follows: T1 (control), 60 kg PM; T2, 30 kg PM + 3 kg MF; T3, 30 kg PM + 5 kg MF, and T4, 30 kg PM + 7 kg MF. The univariate analysis performed on the collected data illustrated the significant enhancement in growth and productivity for the integrated use of PM and MF. Multivariate analyses (correlation, clustering, and Principal Component Analysis) validated the results of univariate analysis by differentiating treatments into two groups. The three treatments obtained a distinguished group from T1 (Control) and did not show significant differences among each other, with a maximum yield increase by T2 (74.6%). According to these results, T2 could improve cucumber productivity under greenhouse conditions. It can be taken as recommendations for better quality and yield enhancement in future improvement programs and cucumber-related farming communities.

Description: Italian dry-cured ham production requires pigs to be slaughtered at 160 ± 16 kg at 9 months of age (control, C). The study explored three alternatives, based on different feeding conditions: (1) allowing pigs to express their growth potential by letting them reach 160 ± 16 kg slaughter weight (SW) at younger slaughter age (SA) (younger Age, YA); (2) allowing pigs to express their growth potential by maximizing their SW at 9 months SA (greater weight, GW); (3) increasing the SA required to reach 160 ± 16 kg SW (older age, OA). Pigs (336 C21 Goland, 95 kg initial body weight) were slaughtered on average at 257, 230, 257, and 273 d SA and 172.7, 172.3, 192.9, and 169.3 SW kg for the four treatments, respectively. C pigs had an average daily gain (ADG) of 715 g/d and feed efficiency (FE) of 0.265 (gain to feed). Compared to C, YA pigs had higher ADG (+32%), FE (+7.5%), and better ham adiposity; GW pigs had higher carcass weight (+12%), ADG (+25%), trimmed ham weight (+10.9%), and better ham adiposity. OA treatment affected ADG (−16.4%), FE (−16.6%), and trimmed ham weight (−3.6%). YA and GW could be promising alternatives to C as they improved FE and ham quality traits.

Description: Many factors govern the flow of deposited nitrogen (N) through forest ecosystems and into stream water. At the Fernow Experimental Forest in WV, stream water nitrate (NO3−) export from a long-term reference watershed (WS 4) increased in approximately 1980 and has remained elevated despite more recent reductions in chronic N deposition. Long-term changes in species composition may have altered forest N demand and the retention of deposited N. In particular, the abundance and importance value of Acer saccharum have increased since the 1950s, and this species is thought to have a low affinity for NO3−. We measured the relative uptake of NO3− and ammonium (NH4+) by six important temperate broadleaf tree species and estimated stand uptake of total N, NO3−, and NH4+. We then used records of stream water NO3− and stand composition to evaluate the potential impact of changes in species composition on NO3− export. Surprisingly, the tree species we examined all used both mineral N forms approximately equally. Overall, the total N taken up by the stand into aboveground tissues increased from 1959 through 2001 (30.9 to 35.2 kg N ha−1 yr−1). However, changes in species composition may have altered the net supply of NO3− in the soil since A. saccharum is associated with high nitrification rates. Increases in A. saccharum importance value could result in an increase of 3.9 kg NO3−-N ha−1 yr−1 produced via nitrification. Thus, shifting forest species composition resulted in partially offsetting changes in NO3− supply and demand, with a small net increase of 1.2 kg N ha−1 yr−1 in NO3− available for leaching. Given the persistence of high stream water NO3− export and relatively abrupt (~9 year) change in stream water NO3− concentration circa 1980, patterns of NO3− export appear to be driven by long-term deposition with a lag in the recovery of stream water NO3− after more recent declines in atmospheric N input.

Description: Biochar, a pyrogenic carbon, has been receiving incremental attention for potential contribution to soil health, agricultural productivity enhancement while mitigating climate change by sequestering carbon and reducing greenhouse gas (GHG) emissions. However, it is not well-known to us how far rice husk biochar (RHB) application rates could increase phosphorus (P) bioavailability and plant performance when co-applied with P and lime. Here, we present data of a pot experiment consisting of eleven treatments to evaluate RHB, lime, and phosphorus effect on soil phosphorus availability, CO2 emission, nutrient uptake, and yield performance of maize. Co-application of RHB (10 and 15 t ha−1) and lime (100% and 75%) was made with different rates of P (100%, 75%, and 50%). Our result revealed that, at harvest, the combined application of RHB, lime, and phosphorus fertilizer significantly increased soil pH, P availability and decreased Al and Fe toxicity relative to the control while increasing maize yield. The maximum soil pH increased by 36.75%, the highest available P increased by 158.75%, whilst, the exchangeable Al content reduced by 96.84% compared to the control treatment. However, the difference in biomass production and yield among different lime, RHB, and P were minimal, with the largest grain yield (15.50 t ha−1) was recorded in the T6 treatments (75% lime + 10 t ha−1 RHB + 100% Triple superphosphate). The increment in biomass and grain yield could have occurred due to lime and RHB mediated changes in soil properties, including enhancement of soil pH, availability of P, and other nutrients. This increased availability then increased nutrient uptake and biomass production. Our results suggest that the combined application of lime and RHB could bring favorable changes in soil properties while sacrificing some carbon from soils.

Description: The introduction of new species in forest management must be undertaken with a degree of care, to help prevent the spread of invasive species. However, new species with higher profitability are needed to increase forest products value and the resilience of rural populations. Paulownia tomentosa has an extremely fast growth. The objective and novelty of this work was to study the potential use of young Paulownia trees grown in Portugal by using heat treatment to improve its properties, thereby allowing higher value applications of the wood. The average chemical composition of untreated and heat-treated wood was determined. The extractive content was determined by successive Soxhlet extraction with dichloromethane (DCM), ethanol and water as solvents. The composition of lipophilic extracts was performed by injection in GC-MS with mass detection. Insoluble and soluble lignin, holocellulose and α-cellulose were also determined. Physical (density and water absorption and dimensional stability) and mechanical properties (bending strength and bending stiffness) and termite resistance was also determined. Results showed that extractive content increased in all solvents, lignin and α-cellulose also increased and hemicelluloses decreased. Compounds derived from the thermal degradation of lignin were found in heat-treated wood extractions. Dimensional stability improved but there was a decrease in mechanical properties. Resistance against termites was better for untreated wood than for heat-treated wood, possibly due to the thermal degradation of some toxic extractives.

Description: This paper describes the physical and tensile properties of corn bracts during a whole harvest period by using two corn cultivars, aiming to realize efficient peeling with minimum energy performance and decrease the incidence of ear damage. The value range and change rule of tensile properties were obtained by combining mechanical experiments and numerical statistics. Meanwhile, mathematical models were established for tensile properties depending on bract moisture content and bract thickness. The experimental results show that the tensile properties of leaf blade were affected by the orientation between pulling force and longitudinal vein, and that parallel orientation was greater than perpendicular. Further, the tensile properties of leaf sheaths depended on the angle between pulling force and natural growth direction of the bract in the following order: 0° 〉 90° 〉 180°. A larger pulling force angle can improve the probability of bract fracturing at the root of leaf sheaths, which helps reduce bract residue on the peduncle. In addition, the fracture mechanisms of leaf blades and sheaths were expressed from physiological and morphological perspectives. The experimental results are believed to be able to provide theoretical guidance by which to design and optimize corn-peeling devices.

Description: The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

Description: The physical-geographical features of the mountainous area of Montenegro cause difficulties in farmers’ life and work. The organization of the agricultural production faces a number of problems that limit the overall development of rural areas. Some agricultural crops, such as raspberry, have found optimal growth conditions and produce appropriate yields associated with good fruit quality in such mountainous area. The Willamette variety dominates the production and has broadly expanded, as some new varieties, most notably, Tulameen, Fertȍdi Zamatos, and Glen Ample. The aim of this paper was to examine the biological and production characteristics of two raspberry varieties—one floricane (Tulameen) and one primocane (Polka)—grown in two localities. We confirmed the general rule that at lower altitudes, these varieties (570 m a.s.l, Bijelo Polje) are characterized by earlier vegetation, flowering, and fruit ripening in comparison to plants at higher altitude (1040 m a.s.l, Mojkovac). The Tulameen variety started flowering on 15 May in Bijelo Polje (the flowering phenophase lasted for 29 days, until 12 June) and on 25 May in Mojkovac (the flowering phenophase lasted for 27 days, until 20 June). That is a delay of 11 days in relation to different locations. The Polka variety started flowering on 25 June in Bijelo Polje (57 days, until 20 August) and on 1 July in Mojkovac (flowering for 67 days, until 5 September). That is a delay of 7 days. The Tulameen variety started maturation on 14 June in Bijelo Polje (maturation lasted for 27 days, until 10 July) and on 22 June in Mojkovac (26 days, until 17 July), with a delay of 9 days. The Polka variety started maturation on 23 July in Bijelo Polje (the maturation phenophase lasted for 55 days, until 15 September) and on 5 August in Mojkovac (52 days, until 25 September), with a delay of 14 days. The results showed that the Polka variety had significantly higher total phenol content than the Tulameen variety (4.43 and 4.03 mg, respectively). In terms of locality, the Mojkovac raspberries had higher total phenol content than the Bijelo Polje raspberries. Polka raspberries also had a higher content of total flavonoids than Tulameen, whereas differences between localities in regard to the content of total flavonoids in the fruit were not significant. The content of condensed tannins and gallotannins in the raspberry fruit was similar in relation to both varieties and localities. Finally, the total antioxidant capacity was significantly higher in the Polka compared to the Tulameen variety, whereas the differences between localities were not statistically significant.

Description: In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.

Description: The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Description: The black soil of Northeast China has a strong adhesion ability because of its unique physical properties, and the soil often adheres to the surface of the grouser shoes of tracked vehicles during the operation. The adhesion performance depends largely on structure parameters of the grouser shoes. The grouser height, the grouser thickness, and the grouser splayed angle were selected as structure parameters. The adhesion force and adhesive soil mass were selected as indicators of adhesion performance. Therefore, the mathematical model between structure parameters and response indicators was established by the response surface method (RSM). The optimal parameters combination was that the grouser height was 20 mm, grouser thickness was 6.34 mm, and grouser splayed angle was 40.45°. The average data of verification experiments occurred when the adhesion force reached 1.11 kPa and adhesive soil mass reached 22.68 g. Compared with the average value of un-optimized experiment results, the adhesion force and adhesive soil mass reduced by 12.84% and 4.63%, respectively. The relative error of the predicted values and measured values was less than 5%, proving the reliability of the regression models. This study could provide a reference method for parameters optimization, and a new structure of the grouser shoes of tracked vehicles will be designed to reduce adhesion.

Description: Deciduous seasonal forests (DSFs) have a peculiar floristic composition, with species capable of surviving periods of high water deficit in a year. Our goal was to demonstrate that abiotic filters lead to the assembly of two DSF communities that have high floristic dissimilarity. For this, we characterized the environment of the areas and used the community-weighted mean (CWM), functional richness (FRic) and functional dispersion (FDis) indices for regional recognition of functional patterns. The local assessment of assembly rules was carried out using null models. We found differences in the FRic and FDis between the areas, which was attributed to the different floristic influences exercised on the communities. However, in both, the typical attributes of dry formations were dominant (CWM), indicating that, on the regional scale of study, the dry season acts as a filter in the composition of species in the communities. On a local scale, stochastic dispersion was identified as the most influential mechanism in the assembly of communities. We conclude that deterministic and stochastic processes act in the assembly of the studied communities, and the proportion of each of these depends on the scale, with abiotic filtration predominating on a regional scale and stochastic dispersion events on a local scale. Study Implications With the expansion of agriculture and climate change, tropical dry biomes, such as deciduous seasonal forests, are rapidly changing. In this study, we contribute to the recognition of functional standards and community assembly of this vegetation type to assist in management planning, restoration, and conservation. Understanding the different processes involved in building a community is crucial for anticipating how communities will behave under future environmental scenarios.

Description: Quantification of soil organic carbon (SOC) stocks in different land uses holds vital significance because of its implications regarding carbon sequestration as the largest terrestrial carbon pool. The current study was conducted to quantify and compare SOC stocks in forest land, agricultural land, and barren land-use categories in the subtropical ecosystems of the Himalayan foothills in Kashmir. Results showed the highest SOC value in the forest soil (75 ± 11.1 Mg C ha−1) followed by agricultural soil (58 ± 7.0 Mg C ha−1) and barren land (44 ± 6.5 Mg C ha−1). SOC stocks in different land uses showed broad variations with values as high as 106 ± 11.3 Mg C ha−1 (forest soils) and as low as 14.1 ± 7.1 Mg C ha−1 (barren land). The forest SOC values responded significantly to species composition with the highest values in Pinus roxburghii Sarg. pure stands as compared with broad leaved forests. Multivariate ordination analyses revealed that the variations in SOC stocks were significantly correlated with vegetation type, altitude, and soil bulk density. The study has identified anthropogenic disturbances as a major factor deteriorating local SOC stocks and recommends immediate soil restoration efforts to enhance the ecological significance of soils as vital regional carbon sinks. Study Implications The current study was designed to investigate the soil organic carbon (SOC) levels in different land uses of the Kashmir Himalayas, Pakistan. Results revealed forest soils as the major carbon sinks of the region, attributed to diverse species composition having a significant quantity of organic residues, followed by agricultural soils. Barren land-use class exhibited the lowest SOC values due to minimum vegetation cover, erosion, and degradation. Significant correlations of SOC stocks with geographical and edaphic variables were identified by multivariate ordination analysis in the investigated land-use classes. Results validate the significance of forests in enhancing SOC stocks, demanding forest restoration in the area by controlling the forest losses as well as afforestation. This article provides vital information required for better understanding of SOC stocks in different land-use classes in the subtropical ecosystems of the Kashmir region.

Description: Gram negative organisms are frequently isolated from Caretta caretta turtles, which can act as reservoir species for resistant microorganisms in the aquatic environment. C. caretta, which have no history of treatment with antimicrobials, are useful sentinel species for resistant microbes. In this culture-based study, commensal bacteria isolated from oral and cloacal samples of 98 healthy C. caretta were compared to clinical isolates from the wounds of 102 injured animals, in order to investigate the presence of AMR bacteria in free-living loggerheads from the Adriatic Sea. A total of 410 isolates were cultured. Escherichia coli and genera such as Serratia, Moraxella, Kluyvera, Salmonella were isolated only in healthy animals, while Acinetobacter, Enterobacter, Klebsiella and Morganella were isolated only from the wounds of the injured animals. When tested for susceptibility to ampicillin, amoxicillin + clavulanic acid, ceftazidime, cefuroxime, gentamicin, doxycycline, ciprofloxacin and enrofloxacin, the clinical isolates showed highly significant differences in AMR rates vs. commensal isolates for all the drugs tested, except for doxycycline. The detection of high AMR rates in loggerheads is of clinical and microbiological significance since it impacts both the choice of a proper antibiotic therapy and the implementation of conservation programs.

Description: The chemical composition of plant components of three maize hybrids harvested at the beginning of six reproductive stages of maturity was compared. The hybrids evaluated included Maximus VIP3, Defender VIP and Feroz VIP, which were evaluated at each of following stages: R1 (grain formation), R2 (milky grain), R3 (pasty grain), R4 (floury grain), R5 (hard grain) and R6 (ripe grain). The advancement in maturation was linearly related to the crude protein (CP) content of the stem, whole plant, and leaves, and there was a difference among the hybrids. Between R4 and R5 stages, Maximus and Defender presented the highest CP contents for husk (6.58 and 5.42% for Maximus; 5.54 and 5.17% for Defender). The neutral detergent fiber (NDF) of the leaves showed a quadratic relationship with the advancement of maturation but did not differ among the hybrids. For all the hybrids, the NDF content in the husk and cobs increased linearly during the reproductive stages (〉77 and 78%, respectively, for the three hybrids in R6). Defender had the lowest NDF content of the cob in R3. The acid detergent lignin contents did not differ among stages in the stems, and showed a linear decrease throughout the whole plant, though the contents did not differ among the hybrids. Due to the differences observed, recommendations for harvest based on the maturity stage for each hybrid should be taken into consideration. There seems to be no important distinction among hybrids for harvesting and use of straw. Despite the reduction in grain yield, an early harvest for earlage or snaplage can provide lower lignin content in husk and cob, as well as higher protein content in the husk, favoring the nutritional value of the vegetative fraction (husk and/or cob).

Description: Rice production in Tanzania, with 67% of its territory considered semi-dry and having average annual rainfall of 300 mm, must be increased to feed an ever-growing population. Water for irrigation and low soil fertility are among the main challenges. One way to decrease water consumption in paddy fields is to change the irrigation regime for rice production, replacing continuous flooding with alternate wetting and drying. In order to assess the impact of different irrigation regimes and nitrogen fertilizer applications on growth, yield, and water productivity of rice, a greenhouse pot experiment with soil from lowland rice ecology was conducted at Sokoine University of Agriculture, Tanzania during the 2019 cropping season. The experiment was split-plot based on randomized complete block design with 12 treatments and 3 replications. Water regimes were the main factors comparing continuous flooding (CF) and alternate wetting and drying (AWD) with nitrogen fertilizer levels as the subfactor, comparing absolute control (no fertilizer) with 0 (P and K fertilizers), 60, 90, 120, and 150 kg Nha−1. Alternate wetting and drying (AWD) significantly improved water productivity by 8.3% over CF (p 〈 0.05). Water productivity (WP) ranged from 0.6 to 1.5 kg of rice per m3 of water. Average water use ranged from 36 to 82 L per season, and water saving was up to 34.3%. Alternate wetting and drying significantly improved yields (p 〈 0.05) by 13.3%, and the yield ranged from 21.8 to 118.2 g pot−1. The combination of AWD water management and 60 kg N ha−1 nitrogen fertilization application was found to be the optimal management, however there was no significant difference between 60 and 90 kg N ha−1, in which case 60 kg N ha−1 is recommended because it lowers costs and raises net income. Nitrogen levels significantly affected water productivity, water use, and number of irrigations. Nitrogen levels had significant effect (p 〈 0.05) on plant height, number of tillers, flag leaf area, chlorophyll content, total tillers, number of productive tillers, panicle weight, panicle length, 1000-grain weight, straw yield, grain yield, and grain harvest index. The results showed that less water can be used to produce more crops under alternative wetting and drying irrigation practices. The results are important for water-scarce areas, providing useful information to policy makers, farmers, agricultural departments, and water management boards in devising future climate-smart adaptation and mitigation strategies.

Description: The knowledge of genetic variability and breeding techniques is crucial in crop improvement programs. This information is especially important in underutilized crops such as Bambara groundnut, which have limited breeding systems and genetic diversity information. Hence, this study evaluated the genetic variability and established the relationship between the yield and its components in Bambara groundnut based on seed weight using multivariate analysis. A field trial was conducted in a randomized complete block design with three replications on 28 lines. Data were collected on 12 agro-morphological traits, and a statistical analysis was conducted using SAS version 9.4 software, while the variance component, genotypic and phenotypic coefficient variation, heritability, and genetic advance values were estimated. A cluster analysis was performed using NT-SYS software to estimate the genetic relations among the accessions. The results showed significant variability among the accessions based on the yield and yield component characteristics. The evaluated lines were grouped into seven primary clusters based on the assessed traits using the UPGMA dendrogram. Based on the overall results, G5LR1P3, G1LR1P3, G4LR1P1, G2SR1P1 and G3SR1P4 performed the best for the yield and yield components. These improved lines are recommended for large-scale evaluation and utilization in future breeding programs to develop high-yield Bambara groundnut varieties.

Description: The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, the control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper, we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multiplanar reformatting, flow visualisation and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation and multiuser interactions. The available features were evaluated by imaging and nonimaging clinicians, showing that the virtual reality system can help improve the understanding and communication of three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.

Description: Root-knot nematode (RKN) is a serious threat to crops worldwide due to the difficulty in controlling it and the limited eco-friendly alternatives to deal with the biotic stress it causes. In the present work, water-extractable fractions obtained from vermicompost (WSFv), vermicompost enriched with Trichoderma asperellum (WSFta) and T. virens (WSFtv) were tested as biotechnological tools to reduce the impacts of RKN on gas exchange, water use efficiency (WUE) and nutrient concentration in tomato and bell pepper plants. The plants were infected with 5000 eggs and eventual J2 of RKN and then treated with the water-extractable fractions for seven weeks. It was observed that the addition of WSFta, WSFtv and WSFv increased the CO2 assimilation, stomatal conductance and WUE in the tomato plants. In the bell pepper plants, WSFta, WSFtv, WSFv increased the stomatal conductance, while WUE was higher in the treatment with WSFtv. In fact, the parameters associated with the gas exchange were usually higher in the bell pepper than in the tomato plants. Overall, higher contents of N, Mg, B and Mn were detected when the extracts were applied in both bell pepper and tomato plants. The application of the water-extractable fractions, inoculated or not with Trichoderma, attenuates the RKN damage on the gas exchange parameters and successfully enhanced the nutrient concentration in the infected tomato and bell pepper plants, showing that it could be an important and promising tool for reducing the damage caused by this pathogen. We suggest that both the tomato and pepper plants can cope with the dilemma between growth and stress response via stomata regulation that are modulated by the WSF and Trichoderma.

Description: During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security.

Description: In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local features exhibit similar geometrical characteristics to those of an “instructor” model, at corresponding layers. The investigated functions, inspired by manifold-to-manifold distance measures, are designed to compare the neighboring information inside the feature space of the involved activations without any restrictions in the features’ dimensionality, thus enabling knowledge transfer between different architectures. Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training. Furthermore, results indicate that the presented method can work synergistically with methods such as knowledge distillation, further increasing the accuracy of the trained models. Finally, experiments on training with limited data show that a combined regularization scheme can achieve the same generalization as a non-regularized training with 50% of the data in the CIFAR-10 classification task.

Description: Animals vary on intrinsic characteristics such as temperament and stress responsiveness, and this information can be useful to experimentalists for identifying more homogeneous subsets of animals that show consistency in risk for a particular research outcome. Such information can also be useful for balancing experimental groups, ensuring animals within an experiment have similar characteristics. In this review, we describe the BioBehavioral Assessment Program at the California National Primate Research Center, which, since its inception in 2001, has been providing quantitative information on intrinsic characteristics to scientists for subject selection and balancing, and to colony management staff for management purposes. We describe the program and review studies relating to asthma, autism, behavioral inhibition, etc., where the BBA Program was used to select animals. We also review our work, showing that factors such as rearing, ketamine exposure, and prenatal experience can affect biobehavioral organization in ways that some investigators might want to control for in their studies. Attention to intrinsic characteristics of subject populations is consistent with the growing interest in precision medicine and can lead to a reduction in animal numbers, savings in time and money for investigators, and reduced distress for the animals.

Description: Research was conducted in connection with the pressure exerted by man on the environment through the use of pesticides. The aim of the study was to assess the impact of pesticides on soil and to evaluate the effect of these changes on greenhouse gas emissions into the atmosphere. The research was carried out on soil sown with oilseed rape. The activity of protease and urease, ammonification, nitrification in soil, as well as CO2 (carbon dioxide) and N2O (nitrous oxide) gas emissions from soil were assessed. The analyses were carried out directly after harvest and 2 months after. Pesticides most frequently negatively affected the tested parameters, in particular enzymatic activities. Of the two herbicides used, Roundup had a stronger negative impact on microbial activity. The application of pesticides, especially the fungicide, resulted in an increase in gas emissions to the atmosphere over time. Pesticides disturbed soil environmental balance, probably interfering with qualitative and quantitative relationships of soil microorganism populations and their metabolic processes. This led to the accumulation of microbial activity products in the form of, among others, gases which contribute to the greenhouse effect by escaping from the soil into the atmosphere.

Description: Land suitability assessment is fundamental in space control planning and land development because of its effects on land use and urban layout. Rainstorms and waterlogging have become one of the most common natural disasters in the coastal areas of China. As a result, the concept of an ecological sponge city was incorporated into the construction of cities in the future. Taking Shenzhen–Shantou special cooperation zone (SSCZ), we constructed a storm flooding model based on the SCS flow generation model and GIS to explore the spatial distribution characteristics of the flooding risk in a rainstorm of 100-year lasting 1 h. Combined with population and economic indicators, a radial basis function (RBF) network was utilized to evaluate the environmental risk, the vulnerability of disaster-bearing bodies, and the rain–flood resilience of sponge cities. The self-organizing feature mapping (SOFM) model was used for cluster analysis. Spatial differences were found in the construction suitability of the study area. A suitable construction area (73.59% of the entire area) was located downtown. The construction of the artificial spongy body in the highest vulnerable area (3.25%) needs to be strengthened. The control construction area (3.3%) is located along the banks of the river, with relatively high risk and low resilience of flood control engineering. Ecological construction (19.85%) serves as the sponge body of ecological buffer. The factors of waterlogging, ecology, population, and economy could be integrated comprehensively by applying neural network methods for urban planning and construction.

Description: The silvopastoral system has the potential to alleviate the negative impacts of heat stress on livestock. Through a preliminary study, we assessed the thermal environment experienced by hair coat lambs, as well as the impacts on their bio-thermal and behavioural responses, when either kept in either the silvopastoral system, or exposed to full sun. Twelve hair coat lambs (Dorper × Santa Ines) were randomly assigned to a silvopastoral system or full sun exposure during the summer (from January to February 2017). Parameters, including air temperature, black globe temperature, relative humidity, wind speed, and ground surface temperature, were measured daily for both thermal environments. From 14:00 to 16:00, lambs kept in silvopastoral areas experienced lower levels of air temperature, radiant heat load, and ground surface temperature. Consequently, they had a lower hair coat surface and lower body rectal temperatures. Lambs exposed to a shaded environment spent more time grazing and walking, and less time standing at rest. In conclusion, lambs kept in a silvopastoral system experienced lower levels of radiant heat load and ground surface temperature. In addition, the animals showed a reduced requirement for evaporative cooling and expressed behaviours that indicated a comfortable thermal environment.

Description: Fusarium head blight (FHB) is one of the major threats for wheat production worldwide. It reduces yield, quality, and feeding value of wheat grains. In addition, mycotoxins produced by Fusarium pathogens can have a negative effect on livestock and human health. The aim of this study was to assess changes in technological quality traits and end-use quality of winter wheat varieties after artificial inoculation with Fusarium spp. over three years. Differences in dough development duration and extensibility were measured as the means of relative reductions due to different environments and varieties’ characteristics. Differences in dough softening during kneading were determined as the means of relative increases due to FHB inoculation. In addition, dough had reduced strength, was stickier, and therefore was more difficult to handle, due to a decrease of the average energy value and resistance to extension in FHB-inoculated wheat, compared to naturally infected plants. Dough development time, stability, and resistance usually varied in a similar way, with FHB-resistant varieties showing a good response to FHB inoculation and maintaining good quality. Increasing the level of Fusarium spp. contamination in more FHB-susceptible wheat varieties worsened their technological quality, primarily, the sedimentation value and the gluten index, and hence had a negative effect on the rheological properties.

Description: Molecular genetic techniques (FISH, RT-PCR, and metagenomic analysis) were used to investigate the comparative functional biodiversity in the prokaryotic complex in grassland and forests’ sod-podzol under polycyclic aromatic hydrocarbon influence. The polluted samples showed a decrease in the biomass of the prokaryotic community representatives and a change in the metabolically active dominants–representatives of the Bacteria and Archaea domains compared to the control samples. The suppression of the metabolic activity of prokaryote cells under the influence of PAHs in sod-podzolic soil under meadow vegetation was more pronounced compared to soils under forest vegetation. The representatives of prokaryotes that are sensitive and resistant to the studied PAHs were identified. The representatives of the phylogenetic groups from the bacterial complex resistant to PAH pollution were Proteobacteria (Alphaproteobacteria), Bacteroidetes, Firmicutes, Chloroflexi, and Thaumarhaeota in the archaeal complex. Representatives of the phylum Acidobacteria and Actinobacteria (Streptosporangiales) are noted among those sensitive to PAH contamination. The presence and expression of the functional alkane monooxygenase (alkB) gene were established in all the experimental variants studied. In the plant variants, the number of copies of alkB genes increased by an order of magnitude and the biomass of metabolically active prokaryotic representatives with the functional alkB gene doubled compared to the unpolluted territories. The copy number index of the alkB gene can be used as one of the parameters when characterizing an ecosystem for the presence of PAH pollutants.

Description: Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

Description: Motivation The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. Results Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availabilityand implementation polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.

Description: Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one’s understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.

Description: Efforts to elucidate protein–DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie’s outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie’s webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.

Description: Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Description: Motivation The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. Availabilityand implementation All data are available online at http://datasetmachat.drugdesign.fr. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Over the past decade, genome-wide assays for chromatin interactions in single cells have enabled the study of individual nuclei at unprecedented resolution and throughput. Current chromosome conformation capture techniques survey contacts for up to tens of thousands of individual cells, improving our understanding of genome function in 3D. However, these methods recover a small fraction of all contacts in single cells, requiring specialised processing of sparse interactome data. In this review, we highlight recent advances in methods for the interpretation of single-cell genomic contacts. After discussing the strengths and limitations of these methods, we outline frontiers for future development in this rapidly moving field.

Description: Good knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5–40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

Description: Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.

Description: DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Description: Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. Results A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). Availability and implementation The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.

Description: Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson’s r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody’s structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Description: Elevated or unnatural levels of arsenic (As) and phosphorus (P) concentrations in soils and waterbodies from anthropogenic sources can present significant hazards for both natural ecosystems and human food production. Effective, environmentally friendly, and inexpensive materials, such as biochar, are needed to reduce mobility and bioavailability of As and P. While biochar features several physicochemical properties that make it an ideal contaminant sorbent, certain modifications such as mineral-impregnation can improve sorption efficiencies for targeted compounds. Here, we conducted sorption experiments to investigate and quantify the potential utility of magnesium (Mg) for improving biochar sorption efficiency of P and As. We synthesized a Mg-modified walnut shells-derived biochar and characterized its ability to remove As and P from aqueous solutions, thereby mitigating losses of valuable P when needed while, at the same time, immobilizing hazardous As in ecosystems. SEM-EDX, FTIR and elemental analysis showed morphological and functional changes of biochar and the formation of new Mg-based composites (MgO, MgOHCl) responsible for improved sorption potential capacity by 10 times for As and 20 times for P. Sorption efficiency was attributed to improved AEC, higher SSA, chemical forms of sorbates and new sorption site formations. Synthetized Mg-composite/walnut shell-derived biochar also removed 〉90% of P from real samples of wastewater, indicating its potential suitability for contaminated waterbody remediation.

Description: Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Description: Motivation Co-evolution analysis can be used to accurately predict residue–residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue–residue distance predictions to be informative of protein flexibility rather than simply static structure. Results We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA—a novel effective environment capable to compute and analyze multiple structure alignments. Results DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA’s capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases. Availability and implementation DAMA is available online at http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Genetic algorithms (GA’s) are mostly used as an offline optimisation method to discover a suitable solution to a complex problem prior to implementation. In this paper, we present a different application in which a GA is used to progressively adapt the collective performance of an ad hoc collection of devices that are being integrated post-deployment. Adaptive behaviour in the context of this article refers to two dynamic aspects of the problem: (a) the availability of individual devices as well as the objective functions for the performance of the entire population. We illustrate this concept in a video surveillance scenario in which already installed cameras are being retrofitted with networking capabilities to form a coherent closed-circuit television (CCTV) system. We show that this can be conceived as a multi-objective optimisation problem which can be solved at run-time, with the added benefit that solutions can be refined or modified in response to changing priorities or even unpredictable events such as faults. We present results of a detailed simulation study, the implications of which are being discussed from both a theoretical and practical viewpoint (trade-off between saving computational resources and surveillance coverage).

Description: Understanding the factors that result in people becoming dog owners is key to developing messaging around responsible acquisition and providing appropriate support for prospective owners to ensure a strong dog–owner bond and optimise dog welfare. This qualitative study investigated factors that influence pet dog acquisition. Semi-structured interviews were conducted with 142 sets of dog owners/caretakers at 23 Dogs Trust community events. Interviews focused on the motivations and influences that impacted how people acquired their dogs. Transcribed interviews and notes were thematically analysed. Two acquisition types were reported, that each accounted for half of our interviewees’ experiences: planned and unplanned. Whilst planned acquisitions involved an intentional search for a dog, unplanned acquisitions occurred following an unexpected and unsought opportunity to acquire one. Unplanned acquisitions frequently involved a participant’s family or friends, people happening upon a dog in need, or dogs received as gifts. Motivations for deciding to take the dog included emotional attachments and a desire to help a vulnerable animal. Many reported making the decision to acquire the dog without hesitation and without conducting any pre-acquisition research. These findings present valuable insights for designers of interventions promoting responsible acquisition and ownership, because there is minimal opportunity to deliver messaging with these unplanned acquisitions. Additionally, these findings may guide future research to develop more complete understandings of the acquisition process. Further studies are required to understand the prevalence of unplanned acquisitions.

Description: Within the context of structure deterioration studies, we propose a new numerical method based on the use of fragility curves. In particular, the present work aims to theoretically study the degradation of concrete bridge structures subjected to aggressive environments. A simple probabilistic method based on fragility curves is presented which allows the forecasting of the lifetime of the considered structural system and the best monitoring time. The method was applied to investigate the degradation of a concrete bridge used as a case study. A Monte Carlo numerical procedure was used to simulate the variation over time of the residual resistant section and the ultimate bending moment of the deck of the case study. Within this context, fragility curves are used as reliable indicators of possible monitoring scenarios. In comparison with other methods, the main advantage of the proposed approach is the small amount of computing time required to obtain rapid assessment of reliability and deterioration level of the considered structure.

Description: It is important to understand price premiums related to certified raw wood to predict the future of forest certifications from the perspective of forestry enterprises. We focused on identifying the trading roundwood market data in the economic center of power in Tokyo. This study aimed to clarify Tama-certified raw wood prices under the local area certification scheme, forest-certified raw wood prices, and the handling volumes at the Tama Roundwood Market Center in Tokyo. Sales details of the Tama Roundwood Market Center were used to identify the handling volumes, Tama-certified raw wood prices, and FSC (Forest Stewardship Council) or SGEC (Japan-specific Sustainable Green Ecosystem Council)-certified raw wood prices. The FSC and SGEC have operated from the center since 2016. Data were collected from the 2006–2018 fiscal years. Our results showed that the volume of raw wood handled increased due to the regeneration-cutting project conducted by the Tokyo Metropolitan Government. On the other hand, there was no price premium for Tama-certified raw wood under the local certification scheme or for FSC- or SGEC-certified raw wood. Price premiums for forest certifications are necessary for ongoing sustainable forest management. There is a need to increase consumer awareness of forest certifications and to differentiate quality certifications further, and these would likely create price premiums.

Description: Using automotive smartphone applications (apps) provided by car manufacturers may offer numerous advantages to the vehicle owner, including improved safety, fuel efficiency, anytime monitoring of vehicle data, and timely over-the-air delivery of software updates. On the other hand, the continuous tracking of the vehicle data by such apps may also pose a risk to the car owner, if, say, sensitive pieces of information are leaked to third parties or the app is vulnerable to attacks. This work contributes the first to our knowledge full-fledged security assessment of all the official single-vehicle management apps offered by major car manufacturers who operate in Europe. The apps are scrutinised statically with the purpose of not only identifying surfeits, say, in terms of the permissions requested, but also from a vulnerability assessment viewpoint. On top of that, we run each app to identify possible weak security practices in the owner-to-app registration process. The results reveal a multitude of issues, ranging from an over-claim of sensitive permissions and the use of possibly privacy-invasive API calls, to numerous potentially exploitable CWE and CVE-identified weaknesses and vulnerabilities, the, in some cases, excessive employment of third-party trackers, and a number of other flaws related to the use of third-party software libraries, unsanitised input, and weak user password policies, to mention just a few.

Description: Environmental pollution is an important issue in metropolitan areas, and roadside trees are directly affected by various sources of pollution to which they exhibit numerous responses. The aim of the present study was to identify morpho-physio-biochemical attributes of maidenhair tree (Ginkgo biloba L.) and American sycamore (Platanus occidentalis L.) growing under two different air quality conditions (roadside with high air pollution, RH and roadside with low air pollution, RL) and to assess the possibility of using their physiological and biochemical parameters as biomonitoring tools in urban areas. The results showed that the photosynthetic rate, photosynthetic nitrogen-use efficiencies, and photochromic contents were generally low in RH in both G. biloba and P. occidentalis. However, water-use efficiency and leaf temperature showed high values in RH trees. Among biochemical parameters, in G. biloba, the lipid peroxide content was higher in RH than in RL trees, but in P. occidentalis, this content was lower in RH than in RL trees. In both species, physiological activities were low in trees planted in areas with high levels of air pollution, whereas their biochemical and morphological variables showed different responses to air pollution. Thus, we concluded that it is possible to determine species-specific physiological variables affected by regional differences of air pollution in urban areas, and these findings may be helpful for monitoring air quality and environmental health using trees.

Description: Suburban rural landscape multifunction has received increasing attention from scholars due to its high demand and impact on main urban areas. However, few studies have been focused on suburban rural landscape multifunction because of data constraints. The present study quantified the four landscape services based on ecological service system, i.e., regulating function (RF), provision function (PF), culture function (CF), and support function (SF), determined the interaction through the Spearman correlation coefficient, and ultimately identified the landscape multifunction hotspots and dominant functions through overlay analysis. The result indicated that suburban rural communities have exhibited the characteristics of regional multifunction, and the landscape multifunction hotspots accounted for 64.2%; it should be particularly noted that, among single-function, dual-function, and multifunction hotspots, both support function, and culture function was dominant, while only one case was found in which the regulating function was dominant. Furthermore, all landscape functions other than SF-CF exhibited certain correlations. The study suggests that planning and management should be performed in future in combination with landscape multifunction to ensure the sustainable development of suburban rural communities.

Description: Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI – Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

Description: Background: The use of wheat and flaxseed to produce omega-3 (ω-3) enriched poultry meat and eggs is very popular in the world. However, wheat and flaxseed contain some anti-nutritional factors (ANFs), and enzymes are usually used to alleviate the deleterious influence of ANFs. Method: A 2 × 3 two factors design was used in the experiment. A total of 540 twenty-week-old Nongda-3 laying hens were randomly allocated to six dietary treatments, two diets (corn/flaxseed and wheat/flaxseed), and three enzymes (enzyme-a contains neutral protease 10,000, xylanase 35,000, β-mannanase 1500, β-glucanase 2000, cellulose 500, amylase 100, and pectinase 10,000 (U g−1); enzyme-b contains alkaline protease 40,000 and neutral protease 10,000 (U g−1); enzyme-c contains alkaline protease 40,000, neutral protease 10,000, and cellulase 4000 (U g−1). Results: There was an interaction between dietary treatment and supplemental enzymes for liver weight and liver inflammatory cytokines of broilers. A significant increase was observed in the fat weight of birds fed a corn diet as compared with a wheat diet. A corn diet and wheat diet with the addition of enzyme-a (p 〈 0.001) showed the highest level of liver fat followed by enzyme-c (p 〈 0.01) and enzyme-b. Moreover, a high level of secretory IL-1β, IL-6, and IL-10 and comparatively higher inflammatory changes in the liver tissue were found in birds fed a corn diet as compared with a wheat diet, and enzyme-b showed more beneficial effects as compared with enzyme-a and -c. The gut microbial composition of hens fed a corn diet was significantly different than that of birds fed a wheat diet. Bacteroides were significantly (p 〈 0.05) abundant in the corn-fed birds as compared with wheat-fed birds. However, Firmicutes were less abundant in the wheat-fed birds than the corn-fed birds (16.99 vs. 31.80%, respectively). The microbial community at the genus level differed significantly in the dietary groups and we observed that Bacteroides are the predominant cecal microbiota. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of co-factors, carbohydrates, vitamins, protein, and energy were expressed at slightly higher levels in the microbiota of the wheat-fed birds, whereas, metabolic pathways for nucleotides, lipids, and glycine were expressed at higher levels in the wheat-fed birds. Furthermore, expression of the growth and cellular processes pathway and endocrine system pathway levels were predicted to be higher for the wheat-fed group as compared with the corn-fed group. Conclusions: In conclusion, our findings suggest that inflammatory changes in laying birds were mediated by a corn diet with flaxseed and enzymes instead of a wheat diet. Additionally, in the wheat-fed group, enzyme-b and -c showed more encouraging results as compared to enzyme-a.

Description: The purpose of this study was to assess the suitability of the Romanian Albanus hybrid of Sorghum bicolor as a potential functional food ingredient. Ultrasound-assisted extraction in different solvents, together with spectrophotometric and chromatographic methods, was used to monitor the variation in total phenolic and flavonoid content and the antioxidant activity of raw sorghum grains before and during short germination periods (24, 36 and 48 h). The High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) investigation of the extracts revealed that the ungerminated sorghum grains extracted with methanol had the highest diversity of phenolic compounds, while the total phenolic content (TPC) was the highest after 36 h of germination in both extract types: 1853 mg GAE/100 g for the methanolic extract and 1726 mg GAE/100 g for the ethanolic extract. The findings of this study showed that the TPC of sorghum extracts is strongly correlated with their antioxidant activity and, overall, that the studied extracts presented a good radical scavenger activity, which supports the benefits of alimentary uses of Sorghum bicolor grains.

Description: Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

Description: Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

Description: There is a growing demand by the modern consumer for meat containing less fat and lower levels of saturated fatty acids, which are considered to increase the risk of coronary heart disease. In southern Italy, the Gentile di Puglia breed is one of the most common on farms, and the light lambs are often consumed. The study evaluates the effect of a diet containing extruded linseed (Linum usitatissimum) on growth performances, carcass traits, and meat quality in Gentile di Puglia light lambs. Thirty-six male lambs are weaned at about 20 days of age, and divided into three groups—each group is either fed a control diet (C), a diet containing 3% extruded linseed (L), or a diet containing 3% extruded linseed and 0.6% oregano (Origanum vulgare) (L + O). The lambs’ growth performances and the slaughtering and dissection data did not differ between groups. Dietary treatments have no significant effect on the quality and chemical composition of Longissimus lumborum (Ll). The mount of linoleic acid in Ll meat is significantly higher in the L group, and this positively affected the total content of n-3 Polyunsaturated Fatty Acid, as well as the n-6/n-3 ratio. The good results obtained concerning the sensory traits meet the requirements of the market’s consumer.

Description: The onset of COVID has impacted the field of veterinary medicine and the lives of cat guardians in numerous ways, yet the subject remains largely unexplored. The purpose of this study was to better understand cat guardians’ fears and concerns pertaining to veterinary care and obtainment of pet care products and food during the initial lock down phase of the COVID pandemic to better address these concerns now and in the future. To this end, an anonymous online survey was distributed to cat guardians during the first two months of the pandemic. The survey included questions pertaining to guardians’ relationship with their cat and their veterinary related concerns and experiences as a result of the pandemic. Quantitative and qualitative data were collected from 956 participants. The results revealed that the increased amount of time guardians spent with their cat had a positive impact on their bond. Participants’ veterinary related concerns, particularly for participants between 18-29 years of age, centered around availability of their veterinarian for both emergency and non-emergency care. Other concerns included fears about the ability to afford emergency veterinary care and obtain cat food and supplies. Awareness of these concerns can help veterinarians better support cat guardians by directly communicating their continued availability and presence in the face of a pandemic as well as other challenging times.

Description: Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.

Description: The following document is The Aquaponic Association’s response to a recent publication on E. coli in Aquaponic and Hydroponic systems. [...]

Description: Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

Description: Emerald ash borer (EAB), (Agrilus planipennis Fairmaire), first identified in 2002 in southeast Michigan, has caused catastrophic ash (Fraxinus spp.) mortality in forests within the core of the invasion and has spread to 35 states and five Canadian provinces. Little is known about persistence and densities of EAB populations in post-invasion sites after most ash trees have died. We monitored EAB populations from 2014 to 2016 using double decker (DD) traps set in the midst of white ash (F. americana) trees in 30 post-invasion sites in southeast and south-central Michigan. Two DD traps were deployed at each site. One trap had a dark green upper prism and light purple lower prism, both baited with cis-3-hexenol lures. The other had two dark purple prisms baited with cis-3-hexenol on the upper prism and Manuka oil on the lower prism. In 2014 and 2016, size and condition of ash trees were recorded and area of live white ash phloem was estimated in an 18-m-radius plot centered around each of the DD traps. Area of live white ash phloem per site ranged from approximately 24 to 421 m2 in 2014 and from 24 to 411 m2 in 2016. Canopy condition of live white ash trees generally improved; 65% and 89% of the trees had healthy canopies (