The 21st century has marked the dawn of an aging population. China’s aging process ranks first worldwide. The country has recognized the gravity of this demographic shift and implemented strategies to address it at the national level. A vast majority of elderly Chinese individuals (approximately 90%) aspire to age in their own homes. Smart homes, endowed with cutting-edge digital technologies, such as AI, the Internet of Things, and big data, hold vast potential for enabling this vision. However, acceptance of smart home products and services among elderly individuals in China remains low. The main reason is that the related products and services fail to effectively alleviate the perceived risk of this population in the R&D process of related products and services, and there is a lack of effective measurement methods. To holistically assess the potential obstacles faced by elderly individuals using smart home products and services, this study targeted individuals aged 45–60 years in China. This study aimed to develop a comprehensive perceived risk scale specific to smart homes for this demographic. Initially, this study identified key risk dimensions and corresponding measurement items through a rigorous literature review, user interviews, and expert consultations. Subsequently, it ensured the reliability and validity of each dimension and its corresponding observation variables through preliminary research, exploratory factor analysis, and confirmatory factor analysis. This approach allowed for a comprehensive understanding of the challenges faced by future elderly individuals when utilizing smart home products and services, thus enabling the development of more effective solutions. The scale encompassed ten factors and seventy measurement items, including Privacy and Security Risk (seven items), Physical Risk (seven items), Technological Risk (nine items), Performance Risk (seven items), Service Risk (nine items), Financial Risk (five items), Psychological Risk (seven items), Industry and Market Risk (six items), Social Support Risk (six items), and Policy and Legal risk (seven items). The measurement scale developed in this study represents a groundbreaking first attempt to create a systematic scale for assessing the perceived risks associated with smart homes for the elderly in China. It not only enables professionals, businesses, and manufacturers to avoid or reduce barriers in the R&D process of related products and services, facilitating smart home industry growth and enhancing user adoption, but also serves as a universal reference for the potential obstacles that digital technology may encounter in addressing aging-related issues, which has significant theoretical value and practical importance. Full article

Nasopharyngeal myiasis in European roe deer (Capreolus capreolus) is a pathological condition caused by the larval stages of Cephenemyia stimulator, a fly from the Oestridae family. These larvae reside in the host’s upper respiratory tract for months, inducing significant tissue damage and clinical symptoms. The lifecycle of Cephenemyia stimulator is complex, involving three larval stages before maturation into adult flies, with each stage contributing to the progressive pathology observed in the host. Despite their prevalence, the histopathological effects of these larvae in the nasal and nasopharyngeal cavities have been understudied. Our study fills this knowledge gap by providing a detailed histopathological analysis of the affected tissues, using various staining techniques to reveal the extent and nature of the damage caused by these parasitic larvae. This histopathological examination reveals significant alterations within the nasopharyngeal mucosa and nasal cavity, including erythematous changes, mucosal metaplasia, fibrosis, and tissue necrosis. Parasitic cysts and eosinophilic infiltration further characterize the impact of the infestation, compromising not only the mucosal integrity but also potentially the olfactory function of the affected animals. This research is crucial for understanding the impact of myiasis on both the health and olfactory capabilities of roe deer populations and could have significant implications for wildlife management and conservation. Full article

A myogenetic oligodeoxynucleotide (myoDN), iSN04 (5′-AGA TTA GGG TGA GGG TGA-3′), is a single-stranded 18-base telomeric DNA that serves as an anti-nucleolin aptamer and induces myogenic differentiation, which is expected to be a nucleic acid drug for the prevention of disease-associated muscle wasting. To improve the drug efficacy and synthesis cost of myoDN, shortening the sequence while maintaining its structure-based function is a major challenge. Here, we report the novel 12-base non-telomeric myoDN, iMyo01 (5′-TTG GGT GGG GAA-3′), which has comparable myogenic activity to iSN04. iMyo01 as well as iSN04 promoted myotube formation of primary-cultured human myoblasts with upregulation of myogenic gene expression. Both iMyo01 and iSN04 interacted with nucleolin, but iMyo01 did not bind to berberine, the isoquinoline alkaloid that stabilizes iSN04. Nuclear magnetic resonance revealed that iMyo01 forms a G-quadruplex structure despite its short sequence. Native polyacrylamide gel electrophoresis and a computational molecular dynamics simulation indicated that iMyo01 forms a homodimer to generate a G-quadruplex. These results provide new insights into the aptamer truncation technology that preserves aptamer conformation and bioactivity for the development of efficient nucleic acid drugs. Full article

To reduce the friction loss and the piston-knocking noise from the perspective of the design of the cylinder bore profile, the piston-ring cylinder bore (PRCB) dynamic model of an L6 diesel engine was developed using AVL-Excite-Piston & Rings. Based on the full-scale test method, the effects of bore taper, starting height of tapered profile, and ellipticity on the friction power and knocking energy of the PRCB system were investigated, and the optimization of the design of the bore profile was carried out with the objectives of minimizing the system’s friction power and the peak knocking kinetic energy. The results showed that the taper of the cylinder bore has the greatest influence on the system’s friction power and the peak knocking kinetic energy, followed by the starting height of the conical profile. For the peak knocking kinetic energy of the piston, there was an obvious interaction between the taper and the starting height of the conical profile. When the taper was 35 μm and 45 μm, the peak knocking kinetic energy showed a decreasing and then increasing trend with the increase in the starting height of the profile, and when the taper was 55 μm the peak knocking kinetic energy monotonically was decreased with the increase in the starting height of the conical profile. The optimization results showed that the system’s friction power was decreased by 15.05% and the peak knocking kinetic energy was decreased by 21.41% for a taper degree of 55 μm, a tapered profile starting height of 31 mm, and an ellipticity of 50 μm compared to the initial cylindrical cylinder bore. Full article

The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window. Full article

Legionella pneumophila can cause a large panel of symptoms besides the classic pneumonia presentation. Here we present a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of Legionnaires’ disease in the same ward. While the first case was easily assumed as nosocomial based on the date of symptom onset, the second case required clear typing results to be assigned either as nosocomial and related to the same environmental source as the first case, or community acquired. To untangle this specific question, we applied core-genome multilocus typing (MLST), whole-genome single nucleotide polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large-scale epidemiological investigations, next-generation sequencing typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of the L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in the environment. Full article

The synergistic effects of boron (B) and rare earth (RE) elements on the microstructure and stress rupture properties were investigated in a Ni-based superalloy. The stress rupture lifetime at 650 °C/873 MPa significantly increased with the addition of B as a single element. Furthermore, the stress rupture lifetime reached its peak (303 h), with a certain amount of B and RE added together in test alloys. Although the grain size and morphology of the γ′ phase varied a little with the change in B and RE addition, they were not considered to be the main reasons for stress rupture performance. The enhancement in stress rupture lifetime was mostly attributed to the segregation of the B and RE elements, which increased the binding force of the grain boundary and improved its strength and plasticity. In addition, the enrichment of B and RE inhabited the precipitation of carbides along grain boundaries. Furthermore, nano-scale RE precipitates containing sulfur (S) and phosphorus (P) were observed to be distributed along the grain boundaries. The purification of grain boundaries by B and RE elements was favorable to further improve the stress rupture properties. Full article

Super duplex stainless steel (SDSS) is a suitable structural material for various engineering applications due to its outstanding strength and corrosion resistance. In particular, its high-temperature strength can enhance the safety of electronic products and cars. SDSS AISI2507, known for its excellent strength and high corrosion resistance, was analyzed for its microstructure and electrochemical behavior at the ignition temperature of Li-ion batteries, 700 °C. At 700 °C, AISI2507 exhibited secondary phase precipitation values of 1% and 8% after 5 and 10 h, respectively. Secondary phase precipitation was initiated by the expansion of austenite, forming sigma, chi, and CrN phases. The electrochemical behavior varied with the fraction of secondary phases. Secondary phase precipitation reduced the potential (From −0.25 V to −0.31 V) and increased the current density (From 8 × 10⁻⁶ A/cm² to 3 × 10⁻⁶ A/cm²) owing to galvanic corrosion by sigma and chi. As the fraction of secondary phases increased (From 0.0% to 8.1%), the open circuit potential decreased (From −0.25 V to −0.32 V). Secondary phase precipitation is a crucial factor in reducing the corrosion resistance of SDSS AISI2507 and occurs after 1 h of exposure at 700 °C. Full article

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR² = 0.01~0.27, ΔRMSE = 1.88~13.77 m³·hm⁻², ΔMAE = 1.17~13.57 m³·hm⁻²). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R² is greater than 0.91, while the fitting R² of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the “airborne LiDAR + satellite remote sensing” data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project. Full article

Flavonoids are important secondary metabolites found in Juglans mandshurica Maxim., which is a precious reservoir of bioactive substances in China. To explore the antitumor actions of flavonoids (JMFs) from the waste branches of J. mandshurica, the following optimized purification parameters of JMFs by macroporous resins were first obtained. The loading concentration, flow rate, and loading volume of raw flavonoid extracts were 1.4 mg/mL, 2.4 BV/h, and 5 BV, respectively, and for desorption, 60% ethanol (4 BV) was selected to elute JMFs-loaded AB-8 resin at a flow rate of 2.4 BV/h. This adsorption behavior can be explained by the pseudo-second-order kinetic model and Langmuir isotherm model. Subsequently, JMFs were identified using Fourier transform infrared combined with high-performance liquid chromatography and tandem mass spectrometry, and a total of 156 flavonoids were identified. Furthermore, the inhibitory potential of JMFs on the proliferation, migration, and invasion of HepG2 cells was demonstrated. The results also show that exposure to JMFs induced apoptotic cell death, which might be associated with extrinsic and intrinsic pathways. Additionally, flow cytometry detection found that JMFs exposure triggered S phase arrest and the generation of reactive oxygen species in HepG2 cells. These findings suggest that the JMFs purified in this study represent great potential for the treatment of liver cancer. Full article

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors. Full article

Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases. Full article

To improve the utilization rate of cable lines while retaining sufficient security, the accurate thermal assessment of cable is significant for cable operation condition evaluation. The thermal rating for a cable joint, which is regarded as a hot spot of cable lines, is not covered by the scope of IEC 60287. While the existing publications for cable joint thermal evaluation also have some limitations. In this paper, the quasi-three-dimensional thermal model of the cable joint was established and the iterative solution method for the model is presented. Based on the model, an improved thermal rating method for the cable joint was proposed, which was implemented with monitored surface temperature and load data. The improved method was verified by the finite element method and the results showed an error of less than 5%. The superiority of the improved method was conducted by the comparison between the previously published method and the improved method. The improved method showed a better accuracy than the previously published method. The proposed method in this paper can be complementary to the IEC method, and is easy to use for the operating evaluation of cable joints in the field with the on-line condition monitoring technology. Full article

Due to the difficulty of accurately characterizing properties such as the molecular weight (M_n) and grafting density (σ) of gradient brushes (GBs), these properties are traditionally assumed to be uniform in space to simplify analysis. Applying a stochastic reaction model (SRM) developed for heterogeneous polymerizations, we explored surface-initiated polymerizations (SIPs) with initiator gradients in lattice Monte Carlo simulations to examine this assumption. An initial exploration of SIPs with ‘homogeneously’ distributed initiators revealed that increasing σ slows down the polymerization process, resulting in polymers with lower molecular weight and larger dispersity (Đ) for a given reaction time. In SIPs with an initiator gradient, we observed that the properties of the polymers are position-dependent, with lower M_n and larger Đ in regions of higher σ, indicating the non-uniform properties of polymers in GBs. The results reveal a significant deviation in the scaling behavior of brush height with σ compared to experimental data and theoretical predictions, and this deviation is attributed to the non-uniform M_n and Đ. Full article

A blend of only commercial powders, including Ni625, CoCrF75, and 316L, were used as the raw material for fabricating non-equiatomic CoCrFeNiMo_xNb_y high entropy alloys (HEAs) through laser powder bed fusion (PBF-LB/M) via in situ alloying, instead of using pure elemental powders, thus reducing the raw materials cost. The rapid cooling inherent in the PBF-LB/M process facilitated the dissolution of Mo and Nb, resulting in a single FCC phase characterized by high relative densities. High-temperature tensile tests were conducted at room temperature, 700 °C, 800 °C, and 900 °C, revealing mechanical properties that surpassed those reported in existing HEA literature. The remarkable strength of the HEAs developed in this study primarily stemmed from the incorporation of Mo and Nb, leading to the precipitation of Mo and Nb-rich lave phases at elevated temperatures. While constraining elongation when confined to grain boundaries, these precipitates enhanced strength without compromising elongation when distributed throughout the matrix. This work is a feasibility study to explore the usage of commodity compositions from the market to develop HEAs using PBF-LB/M, which opens the possibility of using scraps to further the development of new materials. Consequently, this study presents a rapid and cost-effective approach for HEA development, improving efficiency and sidestepping the direct utilization of critical raw metals for sustainable manufacturing. Moreover, this work also underscores the outstanding mechanical performance of these HEAs at high temperatures, paving the way for the design of innovative alloys for future high-temperature applications. Full article

Iron (Fe) is a micronutrient that can be toxic at elevated concentrations, prompting its significance in frequent environmental monitoring. Typically analyzed using methods such as FAAS, ICP-OES and ICP-MS, the challenge of expensive instrumentation operated only in the laboratory presents a barrier for rapid and frequent testing. This study aimed to develop a silica-based smartphone-assisted on-site method for rapid detection of Fe in water using ImageJ software. Suitable conditions, including reagents and a color intensity measurement tool, were optimized for this method. Figures of merit such as detection limit, accuracy and precision were determined. The results showed that adding polyacrylic acid to detection points for silica worsened the results, in contrast to results for paper devices. It was also found that, on ImageJ, it is best to use an integrated density tool to measure color intensity, contrary to the previously reported mean gray tool. Results showed a limit of detection of 0.2 ng, a limit of quantification of 0.6 ng, a linear range of 0.6 ng to 4.5 ng and RSD of <20%. This method is therefore an alternative in field pre-testing and screening. Future studies include application of this method in the field with real samples and in the analysis of other metals. Full article

Social media has dramatically influenced how individuals and groups express their demands, concerns, and aspirations during social demonstrations. The study of X or Twitter hashtags during those events has revealed the presence of some temporal points characterised by high correlation among their participants. It has also been reported that the connectivity presents a modular-to-nested transition at the point of maximum correlation. The present study aims to determine whether it is possible to characterise this transition using entropic-based tools. Our results show that entropic analysis can effectively find the transition point to the nested structure, allowing researchers to know that the transition occurs without the need for a network representation. The entropic analysis also shows that the modular-to-nested transition is characterised not by the diversity in the number of hashtags users post but by how many hashtags they share. Full article

This research delves deeply into the intricate degradation kinetics of triclosan, employing two distinct methodologies: UV and simulated solar irradiation. Through a comprehensive comparative analysis, the study endeavors to elucidate the efficacy of these techniques, aiming to shed light on their respective methodological strengths and limitations. The study compares the efficacy of UV and simulated solar irradiation techniques for triclosan degradation, revealing that both methods exhibit effectiveness in degrading triclosan, with variations observed in degradation rates and byproduct formation. Through a detailed examination of the kinetics of triclosan degradation, the study reveals the intricate pathways and mechanisms involved in the photodegradation process. Results highlight the influence of irradiance levels and residence times on degradation efficiency. The research identifies optimal conditions for triclosan degradation, emphasizing the importance of residence time and irradiance levels. Results show that a residence time of 4 h and an irradiance level of 450 W m⁻² maximize degradation efficiency. Analysis of degradation byproducts provides insights into the transformation pathways of triclosan under UV and simulated solar irradiation, indicating the formation of 2,4-dichlorophenol, quinone, and hydroquinone as primary byproducts. Full article

Fish models used for chemical exposure in toxicological studies are normally kept in barren tanks without any structural environmental enrichment. Here, we tested the combined effects of environmental enrichment and exposure to two mixtures of endocrine disrupting chemicals (EDCs) in zebrafish. Firstly, we assessed whether developmental exposure to an EDC mixture (MIX G1) combined with rearing the fish in an enriched environment influenced behaviour later in life. This was evaluated using locomotion tracking one month after exposure, showing a significant interaction effect between enrichment and the MIX G1 exposure on the measured locomotion parameters. After three months, we assessed behaviour using custom-made behaviour tanks, and found that enrichment influenced swimming activity. Control fish from the enriched environment were more active than control fish from the barren environment. Secondly, we exposed adult zebrafish to a separate EDC mixture (MIX G0) after rearing them in a barren or enriched environment. Behaviour and hepatic mRNA expression for thyroid-related genes were assessed. There was a significant interaction effect between exposure and enrichment on swimming activity and an effect of environment on latency to approach the group of conspecifics, where enriched fish took more time to approach the group, possibly indicating that they were less anxious. Hepatic gene expression of a thyroid-related gene (thrb) was significantly affected by EDC exposure, while enrichment had no discernible impact on the expression of the measured genes. In conclusion, environmental enrichment is important to consider when studying the effects of EDCs in fish. Full article

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia. Full article

Introduction. Osteoporosis (OP) affects 30% of postmenopausal women, often complicated by metabolic syndrome (MetS) with a still controversial role. We aimed to characterize MetS and its components in relation to bone mineral density (BMD), body mass index (BMI), and insulin resistance. Methods. Patients (n = 188) underwent DEXA scans, spine X-rays, and metabolic and hormonal investigations, including bone biomarkers, muscular strength, and physical performance tests, while insulin resistance was evaluated by the Homeostasis Model Assessment (HOMA-IR). Results. Patients with a normal BMD or osteopenia (n = 68) and with OP (n = 120) displayed 51.5% and 30.8% of MetS, but without differences in insulin resistance. When BMD was studied as a function of the cumulative MetS criteria and centiles of BMI, lower levels of BMD were observed beyond an inflection point of 27.2 kg/m² for BMI, allowing for further stratification as lean and overweight/obese (OW/OB) subjects. In contrast with lean individuals (n = 74), in OW/OB patients (n = 46), MetS was associated with HbA1c (p < 0.0037, OR 9.6, 95% CI [1.64–55.6]) and insulin resistance (p < 0.0076, OR 6.7, 95% CI [1.49–30.8]) in the context where BMD values were lower than those predicted from BMI in non-OP subjects. In OP patients with fragility fractures (31% of MetS), glycemia also appeared to be the dominant factor for MetS (p < 0.0005, OR 4.1, 95% CI [1.63–10.39]). Conclusions. These data indicate a detrimental effect of insulin resistance in MetS on OP patients, while the prevalence of the syndrome depends on the proportion of obesity. These findings provide new insights into the pathogenic role of MetS and reveal the need to consider different strata of BMI and insulin resistance when studying postmenopausal OP. Full article

Krascheninnikovia ewersmanniana is a dominant desert shrub in Xinjiang, China, with high economic and ecological value. However, molecular systematics research on K. ewersmanniana is lacking. To resolve the genetic composition of K. ewersmanniana within Amaranthaceae and its systematic relationship with related genera, we used a second-generation Illumina sequencing system to detect the chloroplast genome of K. ewersmanniana and analyze its assembly, annotation, and phylogenetics. Total length of the chloroplast genome of K. ewersmanniana reached 152,287 bp, with 84 protein-coding genes, 36 tRNAs, and eight rRNAs. Codon usage analysis showed the majority of codons ending with base A/U. Mononucleotide repeats were the most common (85.42%) of the four identified simple sequence repeats. A comparison with chloroplast genomes of six other Amaranthaceae species indicated contraction and expansion of the inverted repeat boundary region in K. ewersmanniana, with some genes (rps19, ndhF, ycf1) differing in length and distribution. Among the seven species, the variation in non-coding regions was greater. Phylogenetic analysis revealed Krascheninnikovia ceratoides, Dysphania ambrosioides, Dysphania pumilio, and Dysphania botrys to have a close monophyletic relationship. By sequencing the K. ewersmanniana chloroplast genome, this research resolves the relatedness among 35 Amaranthaceae species, providing molecular insights for germplasm utilization, and theoretical support for studying evolutionary relationships. Full article