Social media uses amongst adolescents: motives, minority stress and eudaimonic well-being / Uso de redes sociales en adolescentes: motivación, estrés de minorías y bienestar eudaimónico

Introduction: The scientific evidence regarding the effects of online social media use on the well-being of adolescents is mixed. In gen-eral, passive uses (receiving, viewing content without interacting) and more screen time are related to lower well-being when compared with active uses (direct interactions and interpersonal exchanges). Objectives:This study ex-amines the types and motives for social media usage amongst adolescents, differentiating them by gender identity and sexual orientation, as well as its effects on eudaimonic well-being and minority stress. Method: A cross-sectional study was conducted with 1259 adolescents, aged 14 to 19 (M= 16.19; SD= 1.08), analysing the Scale of Motives for Using Social Net-working Sites, eudaimonic well-being, the Sexual Minority Adolescent Stress Inventory, screen time and profile type. Results:The results found that longer use time is related to finding partners, social connection and friendships; that gay and bisexual (GB) adolescents perceive more distal stressors online;and that females have higher levels of well-being. Discus-sion: The public profiles of GB males increase self-expression, although minority stress can be related to discrimination, rejection or exclusion. Dif-ferentiated socialization may contribute to a higher level of well-being in females, with both active and passive uses positively effecting eudaimonic well-being in adolescents.(AU)

Introduction: The scientific evidence regarding the effects of online social media use on the well-being of adolescents is mixed. In general, passive uses (receiving, viewing content without interacting) and more screen time are related to lower well-being when compared with active uses (direct interactions and interpersonal exchanges). Objectives: This study examines the types and motives for social media usage amongst adolescents, differentiating them by gender identity and sexual orientation, as well as its effects on eudaimonic well-being and minority stress. Method: A cross-sectional study was conducted with 1259 adolescents, aged 14 to 19 (M = 16.19; SD = 1.08), analysing the Scale of Motives for Using Social Networking Sites, eudaimonic well-being, the Sexual Minority Adolescent Stress Inventory, screen time and profile type. Results: The results found that longer use time is related to finding partners, social connection and friendships; that gay and bisexual (GB) adolescents perceive more distal stressors online; and that females have higher levels of well-being. Discussion: The public profiles of GB males increase self-expression, although minority stress can be related to discrimination, rejection or exclusion. Differentiated socialization may contribute to a higher level of well-being in females, with both active and passive uses positively effecting eudaimonic well-being in adolescents.(AU)

Facial expressivity in dominant macaques is linked to group cohesion.

Social living affords primates (including humans) many benefits. Communication has been proposed to be the key mechanism used to bond social connections, which could explain why primates have evolved such expressive faces. We assessed whether the facial expressivity of the dominant male (quantified from the coding of anatomically based facial movement) was related to social network properties (based on social proximity and grooming) in nine groups of captive rhesus macaques (Macaca mulatta) housed in uniform physical and social environments. More facially expressive dominant male macaques were more socially connected and had more cohesive social groups. These findings show that inter-individual differences in facial expressivity are related to differential social outcomes at both an individual and group level. More expressive individuals occupy more beneficial social positions, which could help explain the selection for complex facial communication in primates.

Optimization and evaluation of modified release solid dosage forms using artificial neural network.

This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.

Harnessing systems biology approach for characterization of carotenoid biosynthesis pathways in microalgae.

Systems biology is an interdisciplinary field that aims to understand complex biological processes at the system level. The data, driven by high-throughput omics technologies, can be used to study the underpinning mechanisms of metabolite production under different conditions to harness this knowledge for the construction of regulatory networks, protein networks, metabolic models, and engineering of strains with enhanced target metabolite production in microalgae. In the current study, we comprehensively reviewed the recent progress in the application of these technologies for the characterization of carotenoid biosynthesis pathways in microalgae. Moreover, harnessing integrated approaches such as network analysis, meta-analysis, and machine learning models for deciphering the complexity of carotenoid biosynthesis pathways were comprehensively discussed.

An end-to-end gait recognition system for covariate conditions using custom kernel CNN.

Gait recognition is the identification of individuals based on how they walk. It can identify an individual of interest without their intervention, making it better suited for surveillance from afar. Computer-aided silhouette-based gait analysis is frequently employed due to its efficiency and effectiveness. However, covariate conditions have a significant influence on individual recognition because they conceal essential features that are helpful in recognizing individuals from their walking style. To address such issues, we proposed a novel deep-learning framework to tackle covariate conditions in gait by proposing regions subject to covariate conditions. The features extracted from those regions will be neglected to keep the model's performance effective with custom kernels. The proposed technique sets aside static and dynamic areas of interest, where static areas contain covariates, and then features are learnt from the dynamic regions unaffected by covariates to effectively recognize individuals. The features were extracted using three customized kernels, and the results were concatenated to produce a fused feature map. Afterward, CNN learns and extracts the features from the proposed regions to recognize an individual. The suggested approach is an end-to-end system that eliminates the requirement for manual region proposal and feature extraction, which would improve gait-based identification of individuals in real-world scenarios. The experimentation is performed on publicly available dataset i.e. CASIA A, and CASIA C. The findings indicate that subjects wearing bags produced 90 % accuracy, and subjects wearing coats produced 58 % accuracy. Likewise, recognizing individuals with different walking speeds also exhibited excellent results, with an accuracy of 94 % for fast and 96 % for slow-paced walk patterns, which shows improvement compared to previous deep learning methods.© 2017 Elsevier Inc. All rights reserved.

Advancements in rice disease detection through convolutional neural networks: A comprehensive review.

This review paper addresses the critical need for advanced rice disease detection methods by integrating artificial intelligence, specifically convolutional neural networks (CNNs). Rice, being a staple food for a large part of the global population, is susceptible to various diseases that threaten food security and agricultural sustainability. This research is significant as it leverages technological advancements to tackle these challenges effectively. Drawing upon diverse datasets collected across regions including India, Bangladesh, Türkiye, China, and Pakistan, this paper offers a comprehensive analysis of global research efforts in rice disease detection using CNNs. While some rice diseases are universally prevalent, many vary significantly by growing region due to differences in climate, soil conditions, and agricultural practices. The primary objective is to explore the application of AI, particularly CNNs, for precise and early identification of rice diseases. The literature review includes a detailed examination of data sources, datasets, and preprocessing strategies, shedding light on the geographic distribution of data collection and the profiles of contributing researchers. Additionally, the review synthesizes information on various algorithms and models employed in rice disease detection, highlighting their effectiveness in addressing diverse data complexities. The paper thoroughly evaluates hyperparameter optimization techniques and their impact on model performance, emphasizing the importance of fine-tuning for optimal results. Performance metrics such as accuracy, precision, recall, and F1 score are rigorously analyzed to assess model effectiveness. Furthermore, the discussion section critically examines challenges associated with current methodologies, identifies opportunities for improvement, and outlines future research directions at the intersection of machine learning and rice disease detection. This comprehensive review, analyzing a total of 121 papers, underscores the significance of ongoing interdisciplinary research to meet evolving agricultural technology needs and enhance global food security.

Time is of the essence: A general framework for uncovering temporal structures of communities.

Ecological communities are inherently dynamic: species constantly turn over within years, months, weeks or even days. These temporal shifts in community composition determine essential aspects of species interactions and how energy, nutrients, information, diseases and perturbations 'flow' through systems. Yet, our understanding of community structure has relied heavily on static analyses not designed to capture critical features of this dynamic temporal dimension of communities. Here, we propose a conceptual and methodological framework for quantifying and analysing this temporal dimension. Conceptually, we split the temporal structure into two definitive features, sequence and duration, and review how they are linked to key concepts in ecology. We then outline how we can capture these definitive features using perspectives and tools from temporal graph theory. We demonstrate how we can easily integrate ongoing research on phenology into this framework and highlight what new opportunities arise from this approach to answer fundamental questions in community ecology. As climate change reshuffles ecological communities worldwide, quantifying the temporal organization of communities is imperative to resolve the fundamental processes that shape natural ecosystems and predict how these systems may change in the future.

Vadose-zone characteristic pollutants distribution, microbial community structure and functionality changes in response to long-term leachate pollution of an informal landfill site.

The study embarked on a comprehensive examination of the evolution and diversity of microorganisms within long-term leachate pollution environments, with a focus on varying depths and levels of contamination, and its linkage to soil characteristics and the presence of heavy metals. It was observed that microbial diversity presented distinct cross-depth trend, where archaeal communities were found to be particularly sensitive to alterations in soil depth. Noteworthily, Euryarchaeota increased by 4.82 %, 7.64 % and 9.87 % compared with topsoil. The abundance of Tahumarchaeota was successively reduced by 5.79 %, 9.58 %, and 12.66 %. The bacterial community became more sensitive to leachate pollution, and the abundance of Protebacteria in contaminated soil decreased by 10.27 %, while the abundance of Firmicutes increased by 7.46 %. The bacterial genus Gemmobacter, Chitinophaga and Rheinheimera; the archaeal genus Methanomassiliicoccus and Nitrosopumilus; along with the fungal genus Goffeauzyma, Gibberella, and Setophaeosphaeria emerged as pivotal biological markers for their respective domains, underpinning the biogeochemical dynamics of these environments. Furthermore, the study highlighted that geochemical factors, specifically nitrate (NO3--N) levels and humic acid (HA) fractions, played crucial roles in modulating the composition and metabolic potential of these communities. Predictive analyses of functional potentials suggested that the N functional change of archaea was more pronounced, with anaerobic ammonia oxidation and nitrification decreased by 15.78 % and 14.62 %, respectively. Overall, soil characteristics alone explained 57.9 % of the total variation in the bacterial community structure. For fungal communities within contaminated soil, HMs were the primary contributors, explaining 46.9 % of the variability, while soil depth accounting for 6.4 % of the archaeal variation. This research enriches the understanding of the complex interrelations between heavy metal pollution, soil attributes, and microbial communities, paving the way for informed strategies in managing informal landfill sites effectively.

Neural correlates of approach-avoidance behavior in healthy subjects: Effects of low-frequency repetitive transcranial magnetic stimulation (rTMS) over the right dorsolateral prefrontal cortex.

The dorsolateral prefrontal cortex (dlPFC) is implicated in top-down regulation of emotion, but the detailed network mechanisms require further elucidation. To investigate network-level functions of the dlPFC in emotion regulation, this study measured changes in task-based activation, resting-state and task-based functional connectivity (FC) patterns following suppression of dlPFC excitability by 1-Hz repetitive transcranial magnetic stimulation (rTMS). In a sham-controlled within-subject design, 1-Hz active or sham rTMS was applied to the right dlPFC of 19 healthy volunteers during two separate counterbalanced sessions. Following active and sham rTMS, functional magnetic resonance imaging (fMRI) was conducted in the resting state (rs-fMRI) and during approach-avoidance task responses to pictures with positive and negative emotional content (task-based fMRI). Activation and generalized psychophysiological interaction analyses were performed on task-based fMRI, and seed-based FC analysis was applied to rs-fMRI data. Task-based fMRI revealed greater and more lateralized activation in the right hemisphere during negative picture responses compared to positive picture responses. After active rTMS, greater activation was observed in the left middle prefrontal cortex compared to sham rTMS. Further, rTMS reduced response times and error rates in approach to positive pictures compared to negative pictures. Significant FC changes due to rTMS were observed predominantly in the frontoparietal network (FPN) and visual network (VN) during the task, and in the default mode network (DMN) and VN at rest. Suppression of right dlPFC activity by 1-Hz rTMS alters large-scale neural networks and modulates emotion, supporting potential applications for the treatment of mood disorders.

Dynamically Cross-linked Oligo[2]rotaxane Networks Mediated by Metal-Coordination.

Polyrotaxanes (PRs) have attracted significant research attention due to their unique topological structures and high degrees of conformational freedom. Herein, we take advantage of an oligo[2]rotaxane to  construct a novel class of dynamically cross-linked rotaxane network (DCRN) mediated by metal-coordination. The oligo[2]rotaxane skeleton offers several distinct advantages: In addition to retaining the merits of traditional polymer backbones, the ordered intramolecular motion of the [2]rotaxane motifs introduced dangling chains into the network, thereby enhancing the stretchability of the DCRN. Additionally, the dissociation of host‒guest recognition and subsequent sliding motion, along with the breakage of metal-coordination interactions, represented an integrated energy dissipation pathway to enhance mechanical properties. Moreover, the resulting DCRN demonstrated responsiveness to multiple stimuli and displayed exceptional self-healing capabilities in a gel state. Upon exposure to PPh3, which induced network deconstruction by breaking the coordinated cross-linking points, the oligo[2]rotaxane could be recovered, showcasing good recyclability. These findings demonstrate the untapped potential of the oligo[2]rotaxane as a polymer skeleton to develop DCRN and open the door to extend their advanced applications in intelligent mechanically interlocked materials.

Cuticle Development And The Underlying Transcriptome-Metabolome Associations During Early Seedling Establishment.

The plant cuticle is a complex extracellular lipid barrier that has multiple protective functions. We investigated cuticle deposition by integrating metabolomics and transcriptomics data gathered from six different maize seedling organs of four genotypes, the inbred lines B73 and Mo17, and their reciprocal hybrids. These datasets captured the developmental transition of the seedling from heterotrophic skotomorphogenic growth to autotrophic photomorphogenic growth, which is a transition that is highly vulnerable to environmental stresses. Statistical interrogation of these data reveals that the predominant determinant of cuticle composition is seedling organ type, whereas the seedling genotype has a smaller effect on this phenotype. Gene-to-metabolite associations assessed by integrated statistical analyses identified three gene networks connected with the deposition of different elements of the cuticle: a) cuticular waxes; b) monomers of lipidized cell wall biopolymers, including cutin and suberin; and c) both of these elements. These gene networks reveal three metabolic programs that appear to support cuticle deposition, including processes of chloroplast biogenesis, lipid metabolism, and molecular regulation (e.g., transcription factors, post-translational regulators and phytohormones). This study demonstrates the wider physiological metabolic context that can determine cuticle deposition and lays the groundwork for new targets for modulating properties of this protective barrier.

20 years of BioMalPar: Building a collaborative malaria research network.

In 2004 the first annual BioMalPar meeting was held at EMBL Heidelberg, bringing together researchers from around the world with the goal of building connections between malaria research groups in Europe. Twenty years on it is time to reflect on what was achieved and to look ahead to the future.

Effects of Screen-Based Leisure Time on 24 Subsequent Health and Wellbeing Outcomes: A Longitudinal Outcome-Wide Analysis.

BACKGROUND: Previous research has shown that screen-based leisure time is related to physical and mental health, relationships, and prosocial behaviors. However, it remains unclear whether screen-based leisure time causally affects wellbeing, as previous studies have relied on cross-sectional data, focused on one type of media use (e.g., social media, video games, or internet), or assessed a narrow set of outcomes. METHOD: We used three waves (2016, 2017, 2019) of national longitudinal data from the New Zealand Attitudes and Values Study to investigate the effects of screen-based leisure time on 24 parameters of wellbeing (n = 11,085). We operationalized screen-based leisure as the sum of time spent browsing the internet, using social media, watching/reading the news, watching videos, and playing video games. We followed the outcome-wide analytic design for observational data by performing a series of multivariable regression models estimating the effect of screen-based leisure time on 24 wellbeing outcomes and assessed potential unmeasured confounding using sensitivity analyses. RESULTS: In our primary analysis with the total sample, total screen-based leisure time was associated with a very modest decrease in body satisfaction and a very modest increase in body mass index. Possible evidence of associations was found with increases in number of hours spent exercising and volunteering each week, as well as decreases in number of average daily hours of sleep, self-control, and subjective health. CONCLUSION: Screen-based leisure time has the potential to affect health and wellbeing. Results are discussed in light of the high prevalence of screen-based leisure time.

Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.

Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.

Hepatitis C Virus Reinfection Among Men Who Have Sex With Men With HIV in New York City.

BACKGROUND: Hepatitis C virus (HCV) reinfection rates are substantially higher than primary infection rates among men who have sex with men (MSM) with human immunodeficiency virus (HIV) in European cohorts. The behaviors mediating this high rate of transmission among MSM are poorly characterized. METHODS: We performed a prospective cohort study in New York City (NYC) of MSM with HIV who cleared HCV to determine the incidence of and risk factors for HCV reinfection. We assessed the risk behaviors for primary HCV in NYC: receipt of semen in the rectum, and sexualized methamphetamine use, along with route of use. Multivariable analysis was performed with Andersen-Gill extension of the Cox proportional hazards model. RESULTS: From 2000 through 2018, among 304 MSM with HIV who cleared HCV, 42 reinfections occurred over 898 person-years, for an incidence rate of 4.7 per 100 person-years. Assessing 1245 postclearance visits, only receipt of semen into the rectum was associated with reinfection (hazard ratio, 9.7 [95% confidence interval: 3.3-28.3], P < .001); methamphetamine use was not. CONCLUSIONS: The high HCV reinfection rate over almost 2 decades demonstrates that sexual transmission of HCV is not inefficient or unusual and that direct-acting antiviral treatment is not sufficient for HCV elimination among MSM in NYC. The contrasts between both the rates of and risk factors for primary and HCV reinfection suggest that HCV prevalence is highly heterogenous among sexual networks and that sexualized methamphetamine use, rather than mediating transmission, is instead a surrogate marker for the highest HCV prevalence networks. As neither condoms nor treatment have been successful strategies for HCV prevention in NYC, novel interventions are needed to stem this sexually transmitted HCV epidemic.

Nutrient removal efficacy and microbial dynamics in constructed wetlands using Fe(III)-mineral substrates for low carbon-nitrogen ratio sewage treatment.

This study evaluated the roles of two common sources of Fe(III)-minerals-volcanic rock (VR) and synthetic banded iron formations from waste iron tailings (BIF-W)-in vertical flow-constructed wetlands (VFCWs). The evaluation was conducted in the absence of critical environmental factors, including Fe(II), Fe(III), and soil organic matter (SOM), using metagenomic analysis and integrated correlation networks to predict nitrogen removal pathways. Our findings revealed that Fe(III)-minerals enhanced metabolic activities and cellular processes related to carbohydrate decomposition, thereby increasing the average COD removal rates by 10.7% for VR and 5.90% for BIF-W. Notably, VR improved nitrogen removal by 1.70% and 5.40% compared to BIF-W and the control, respectively. Fe(III)-mineral amendment in bioreactors also improved the retention of denitrification and nitrification bacteria (phylum Proteobacteria) and anammox bacteria (phylum Planctomycetes), with increases of 3.60% and 3.20% using VR compared to BIF-W. Metagenomic functional prediction indicated that the nitrogen removal mechanisms in VFCWs with low C/N ratios involve simultaneous partial nitrification, ANAMMOX, and denitrification (SNAD). Network-based analyses and correlation pathways further suggest that the advantages of Fe(III)-minerals are manifested in the enhancement of denitrification microorganisms. Microbial communities may be activated by the functional dissolution of Fe(III)-minerals, which improves the stability of SOM or the conversion of Fe(III)/Fe(II). This study provides new insights into the functional roles of Fe(III)-minerals in VFCWs at the microbial community level, and provides a foundation for developing Fe-based SNAD enhancement technologies.

Friendship Network and School Socialization Correlates of Adolescent Ethnic-Racial Identity Development.

Ethnic-racial identity (ERI) development is consequential for youth adjustment and includes exploration, resolution, and affect about the meaning of one's ethnic-racial group membership. Little is known about how identity-relevant experiences, such as ethnic-racial socialization and discrimination in peer relationships and school contexts, catalyze adolescent ERI development. The present study examines how identity-relevant experiences in friend and school contexts (i.e., proportion of same-ethnoracial friends, cultural socialization among friends, friends' ERI dimensions, friends' experiences of ethnoracial discrimination, and school promotion of cultural competence and critical consciousness) are associated with ERI development. A multivariate path model with a sample from four southwestern U.S. schools (N = 717; 50.5% girls; Mage = 13.76; 32% Latinx, 31.5% Multiethnic, 25.7% White, 11% other) was used to test these associations. Findings showed that friend and school predictors of ERI did not differ between early and middle adolescents, but significant differences and similarities emerged in some of these associations between ethnoracially minoritized and White youth. Specifically, friend cultural socialization was positively associated with ERI exploration for ethnoracially minoritized youth only, whereas school critical consciousness socialization was positively linked with ERI exploration only for White youth. Friend cultural socialization and friend network's levels of ERI resolution were positively associated with ERI resolution across both ethnoracial groups. These friend and school socialization associations were documented above and beyond significant contributions of personal ethnoracial discrimination to ERI exploration and negative affect for both ethnoracially minoritized and White youth. These findings expand our understanding of how friend and school socialization mechanisms are associated with adolescent ERI development, which is vital to advancing developmental theory and fostering developmental competences for youth to navigate their multicultural yet socially stratified and inequitable world.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage.

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau.

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

Deep learning-based measurement of split glomerular filtration rate with 99mTc-diethylenetriamine pentaacetic acid renal scan.

PURPOSE: To develop a deep learning (DL) model for generating automated regions of interest (ROIs) on 99mTc-diethylenetriamine pentaacetic acid (DTPA) renal scans for glomerular filtration rate (GFR) measurement. METHODS: Manually-drawn ROIs retrieved from a Picture Archiving and Communications System were used as ground-truth (GT) labels. A two-dimensional U-Net convolutional neural network architecture with multichannel input was trained to generate DL ROIs. The agreement between GFR values from GT and DL ROIs was evaluated using Lin's concordance correlation coefficient (CCC) and slope coefficients for linear regression analyses. Bias and 95% limits of agreement (LOA) were assessed using Bland-Altman plots. RESULTS: A total of 24,364 scans (12,822 patients) were included. Excellent concordance between GT and DL GFR was found for left (CCC 0.982, 95% confidence interval [CI] 0.981-0.982; slope 1.004, 95% CI 1.003-1.004), right (CCC 0.969, 95% CI 0.968-0.969; slope 0.954, 95% CI 0.953-0.955) and both kidneys (CCC 0.978, 95% CI 0.978-0.979; slope 0.979, 95% CI 0.978-0.979). Bland-Altman analysis revealed minimal bias between GT and DL GFR, with mean differences of - 0.2 (95% LOA - 4.4-4.0), 1.4 (95% LOA - 3.5-6.3) and 1.2 (95% LOA - 6.5-8.8) mL/min/1.73 m² for left, right and both kidneys, respectively. Notably, 19,960 scans (81.9%) showed an absolute difference in GFR of less than 5 mL/min/1.73 m². CONCLUSION: Our DL model exhibited excellent performance in the generation of ROIs on 99mTc-DTPA renal scans. This automated approach could potentially reduce manual effort and enhance the precision of GFR measurement in clinical practice.