ALBERT

Description: A trial was conducted during the 2005–2006 season in order to determine the effects of different leaf-to-fruit ratios on yield components and fruit composition in four Vitis vinifera L. cultivars. The treatments consisted of selecting shoots of four lengths (〉1.3 m, 1.3–0.8 m, 0.8–0.4 m, and 〈0.4 m) with two crop levels (1–2 clusters/shoot), which allowed defining eight ratios. Berry composition and yield components were measured. The treatments affected the accumulation of soluble solids in “Sauvignon blanc”, “Cabernet Sauvignon”, and “Syrah”, delaying it as the ratio decreased. All yield components were affected in “Sauvignon blanc”, while bunch weight and the number of berries per bunch were altered without a clear trend. None of the yield components were affected in “Cabernet Sauvignon”, while the lowest ratio presented the lowest number of berries per bunch in “Syrah”. Total polyphenol index (TPI) was affected in “Carmenère” without a clear trend. A highly significant correlation was found between shoot length and leaf area in all studied cultivars. As the ratio increased, the shoot lignification increased in “Sauvignon blanc”. However, studies must be conducted during more seasons to establish better conclusions about the effects of leaf-to-fruit ratios on yield and fruit composition.

Description: As women in developing world settings gain access to formal work sectors, it is important to understand how such changes might influence child nutrition. The purpose of this paper is to examine the relationship between maternal employment status and minimum meal frequency (MMF) among children in Tanzania. Interviews were conducted with 5000 mothers of children ages 0–23 months. The questionnaire used in these interviews was developed by adopting questions from Tanzania’s latest Demographic and Health Survey (2015–2016) where possible and creating additional questions needed for programmatic baseline measurements. MMF was used as proxy for child nutrition. Logistic regression analyses were used to identify associations between employment status and parenting practices of Tanzanian mothers and MMF of their children. After adjusting for confounders, informal maternal employment [OR = 0.58], lack of financial autonomy [OR = 0.57] and bringing the child with them when working away from home [OR = 0.59] were negatively associated with meeting MMF. Payment in cash [OR = 1.89], carrying food for the child [OR = 1.34] and leaving food at home for the child [OR = 2.52] were positively associated with meeting MMF. Informal maternal employment was found to be negatively associated with meeting MMF among Tanzanian children. However, behaviors such as bringing or leaving prepared food, fiscal autonomy and payment in cash showed significant positive associations. These findings could help direct future programs to reduce child stunting.

Description: Precipitation climatologies for the period 1961–1990 were generated for all climatic regions of Costa Rica using an irregular rain-gauge observational network comprised by 416 rain-gauge stations. Two sub-networks were defined: a high temporal resolution sub-network (HTR), including stations having at least 20 years of continuous records during the study period (157 in total); and a high spatial resolution sub-network (HSR), which includes all HTR-stations plus those stations with less than 20 years of continuous records (416 in total). Results from the kriging variance reduction efficiency (KRE) objective function between the two sub-networks, show that ordinary kriging (OK) is unable to fully explain the spatio-temporal variability of precipitation within most climatic regions if only stations from the HTR sub-network are used. Results also suggests that in most cases, it is beneficial to increase the density of the rain-gauge observational network at the expense of temporal fidelity, by including more stations even though their records may not represent the same time step. Thereafter, precipitation climatologies were generated using seven deterministic (IDW, TS2, TS2PARA, TS2LINEAR, TPS, MQS and NN) and two geostatistical (OK and KED) interpolation methods. Performance of the various interpolation methods was evaluated using cross validation technique, selecting the mean absolute error (MAE) and the root-mean square error (RMSE) as agreement metrics. Results suggest that IDW is marginally superior to OK and KED for most climatic regions. The remaining deterministic methods however, considerably deviate from IDW, which suggests that these methods are incapable of properly capturing the true-nature of spatial precipitation patterns over the considered climatic regions. The final generated IDW climatology was then validated against the Global Precipitation Climatology Centre (GPCC), Climate Research Unit (CRU) and WorldClim datasets, in which overall spatial and temporal coherence is considered satisfactory, giving assurance about the use this new climatology in the development of local climate impact studies.

Description: The ideal in vitro recreation of the micro-tumor niche—although much needed for a better understanding of cancer etiology and development of better anticancer therapies—is highly challenging. Tumors are complex three-dimensional (3D) tissues that establish a dynamic cross-talk with the surrounding tissues through complex chemical signaling. An extensive body of experimental evidence has established that 3D culture systems more closely recapitulate the architecture and the physiology of human solid tumors when compared with traditional 2D systems. Moreover, conventional 3D culture systems fail to recreate the dynamics of the tumor niche. Tumor-on-chip systems, which are microfluidic devices that aim to recreate relevant features of the tumor physiology, have recently emerged as powerful tools in cancer research. In tumor-on-chip systems, the use of microfluidics adds another dimension of physiological mimicry by allowing a continuous feed of nutrients (and pharmaceutical compounds). Here, we discuss recently published literature related to the culture of solid tumor-like tissues in microfluidic systems (tumor-on-chip devices). Our aim is to provide the readers with an overview of the state of the art on this particular theme and to illustrate the toolbox available today for engineering tumor-like structures (and their environments) in microfluidic devices. The suitability of tumor-on-chip devices is increasing in many areas of cancer research, including the study of the physiology of solid tumors, the screening of novel anticancer pharmaceutical compounds before resourcing to animal models, and the development of personalized treatments. In the years to come, additive manufacturing (3D bioprinting and 3D printing), computational fluid dynamics, and medium- to high-throughput omics will become powerful enablers of a new wave of more sophisticated and effective tumor-on-chip devices.

Description: Most of current life-cycle approaches show an anthropocentric standpoint for the evaluation of human-dominated activities. However, this perspective is insufficient when it comes to assessing the contribution of natural resources to production processes. In this respect, emergy analysis evaluates human-driven systems from a donor-side perspective, accounting for the environmental effort performed to make the resources available. This article presents a novel methodological framework, which combines emergy analysis and dynamic Data Envelopment Analysis (DEA) for the ecocentric assessment of multiple resembling entities over an extended period of time. The use of this approach is shown through a case study of wind energy farms. Furthermore, the results obtained are compared with those of previous studies from two different angles. On the one hand, a comparison with results from anthropocentric approaches (combined life cycle assessment and DEA) is drawn. On the other hand, results from similar ecocentric approaches, but without a dynamic model, are also subject to comparison. The combined use of emergy analysis and dynamic DEA is found to be a valid methodological framework for the computation of resource efficiency and the valuation of ecosystem services. It complements traditional anthropocentric assessments while appropriately including relevant time effects.

Description: The increasing incidence of Candida albicans infections and resistance to current antifungal therapies has led to the search for new and more effective antifungal compounds. Actinobacterial species from the Streptomyces genus are recognized as some of the major producers of antimicrobial compounds. Therefore, the aims of this study were: (1) the identification of Streptomyces strains isolated from Mexican tropical acidic soils, (2) the evaluation of their antifungal activity on C. albicans, and (3) the exploration of the presence of polyketide synthase genes in their genome and antifungal secondary metabolites in their extracts. Four actinobacterial strains, isolated from previously unexplored soils with antibacterial antecedents, were selected. These strains were identified as Streptomyces angustmyceticus S6A-03, Streptomyces manipurensis S3A-05 and S3A-09, and Streptomyces parvisporogenes S2A-04, according to their molecular analyses. The ethanol extract of the lyophilized supernatant of S. parvisporogenes displayed the most interesting antifungal activity against C. albicans, with a minimum inhibitory concentration (MIC) of 0.5 mg/mL. Type I polyketide synthase (PKS-I) and non-ribosomal peptide synthase (NRPS) genes were detected in all strains. In addition, type II PKS genes (PKS-II) were also found in S. manipurensis S3A-05 and S. parvisporogenes. LC-UV-HRMS analysis of the active organic extract of S. parvisporogenes indicated the presence of the known antifungal compound carbazomycin G as the major component.

Description: (1) Background: Degradation of soils and erosion have been described for most of the soils presented along the Maule Valley. Organic and integrated management promotes agroecosystem health, improving soil biological activity. Due to this, the aim of this research was to study the effect of organic, integrated, and conventional management on the productive, oenological and soil variables of a vineyard cultivated under semiarid conditions during 5 consecutive seasons; (2) Methods: Yield, grape and wine oenological, and soil physicochemical parameters were evaluated. Bioclimatic indices were calculated in the studied seasons; (3) Results: Conventional management allowed to improve yield and the number of bunches per vine compared to organic management. However, this latter enhanced mineral nitrogen and potassium content in soil. Based on bioclimatic indices, heat accumulation improved number of bunches per plant and most of the soil physicochemical parameters; (4) Conclusions: Organic management improved the accumulation of some microelements in soils at the expense of yield. Organic matter decreased along the study was carried out. Season was the conditioning factor of the variability of most of the studied parameters, while the interaction between season and type of management affected soluble solids, probable alcohol and pH in grapes, and total polyphenol index and pH in wines.

Description: Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and transportation. The development of Human Activity Recognition (HAR) algorithms involves the collection of a large amount of labelled data which should be annotated by an expert. However, the data annotation process on large datasets is expensive, time consuming and difficult to obtain. The development of a HAR approach which requires low annotation effort and still maintains adequate performance is a relevant challenge. We introduce a Semi-Supervised Active Learning (SSAL) based on Self-Training (ST) approach for Human Activity Recognition to partially automate the annotation process, reducing the annotation effort and the required volume of annotated data to obtain a high performance classifier. Our approach uses a criterion to select the most relevant samples for annotation by the expert and propagate their label to the most confident samples. We present a comprehensive study comparing supervised and unsupervised methods with our approach on two datasets composed of daily living activities. The results showed that it is possible to reduce the required annotated data by more than 89% while still maintaining an accurate model performance.

Description: Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.

Description: The demand for easily deployable indoor localisation solutions has been growing. Although several systems have been proposed, their limitations regarding the high implementation costs hinder most of them to be widely used. Fingerprinting-based IPS (Indoor Positioning Systems) depend on characteristics pervasively available in buildings. However, such systems require indoor floor plans, which might not be available, as well as environmental fingerprints, that need to be collected through human resources intensive processes. To overcome these limitations, this paper proposes an algorithm for the automatic construction of indoor maps and fingerprints, solely depending on non-annotated crowdsourced data from smartphones. Our system relies on multiple gait-model based filtering techniques for accurate movement quantification in combination with opportunistic sensing observations. After the reconstruction of users’ movement with PDR (Pedestrian Dead Reckoning) techniques, Wi-Fi measurements are clustered to partition the trajectories into segments. Similar segments, which belong to the same cluster, are identified using an adaptive approach based on a geomagnetic field distance. Finally, the floor plans are obtained through a data fusion process. Merging the acquired environmental data using the obtained floor plan, fingerprints are aligned to physical locations. Experimental results show that the proposed solution achieved comparable floor plans and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free IPS.