Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in health behavior at an afterschool program: the impact of COVID-19 on students’ behavior

Background: A minority of American youth meet CDC physical activity (PA) recommendations; children in the Bronx face additional structural barriers to engaging in PA. The B’N Fit Power expansion draws on pilot programming to increase the proportion of middle school students who engage in one hour of daily PA. The COVID-19 pandemic presented additional obstacles, including increased food insecurity and suspension of organized PA programming. This research aimed to evaluate differences in baseline target behavior attainment before and after the start of the COVID-19 pandemic to inform future programming to help children reduce their risk of obesity. Methods: Afterschool program leaders at three Bronx public schools collected demographic and target behavior data at baseline and attendance data throughout the school year. Results: During the 2016–2017 and 2017–2018 school years, 76 students enrolled and completed one year of programming, which was administered at a single site (61 % Hispanic, 46% female). Of these, 76 (100%) completed a baseline target behaviors questionnaire. During the 2021–2022 school year, 417 students enrolled and completed one year of programming at one of the three sites (70% Hispanic, 48% female). 89 (21%) completed a baseline target behaviors questionnaire. Participants surveyed after the start of the COVID-19 pandemic reported drinking more sugar-sweetened beverages (Median=3 daily, IQR 2–5), sleeping less (Median=8 hours daily, IQR 6–9 hours), and consuming fast food more frequently (Median=1 time weekly, IQR 0 times weekly-2 to 3 times weekly) than those surveyed prior to the start of the pandemic. The number of PA hours completed each week trended toward significant decline (Median=3, IQR 2–5, p=0.09) in students tracked after the start of the pandemic. Conclusions: The attainment of several target behaviors among school children linked to the reduction of childhood obesity declined during the COVID-19 pandemic. These findings can be applied to enhancing existing real-world afterschool PA programming.
Model ensembles of ecosystem services fill global certainty and capacity gaps

Sustaining ecosystem services (ES) critical to human well-being is hindered by many practitioners lacking access to ES models (“the capacity gap”) or knowledge of the accuracy of available models (“the certainty gap”), especially in the world’s poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2 to 14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity, indicating that accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision-making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local- to global-scale movement toward ES sustainability.
A Musculoskeletal Model of the Upper Limb for Real Time Interaction

With the ever-increasing power of real time graphics and computational ability of desktop computers, the desire for a real-time simulation of the musculoskeletal system has become more pronounced. It is important that this simulation is realistic, interactive, runs in real time, and looks realistic, especially in our climate of Hollywood special-effects and stunning video games. An effective simulation of the musculoskeletal system hinges on three key features: accurate modeling of kinematic movement, realistic modeling of the muscle attachment points, and determining the direction of the forces applied at the points. By taking known information about the musculoskeletal system and applying it in a real time environment, we have created such a model of the human arm. This model includes realistic constraints on the joints and real-time wrapping algorithms for muscle action lines. Preliminary evaluation shows that the moment arms calculated by our model are similar to those shown in the literature. Furthermore, by coupling our model with known optimization algorithms, muscle activation levels for prescribed joint torques can be calculated in real time.
Comparison of GAL80ts and Tet-off GAL80 transgenes

The manipulation of gene expression requires complete control of space, time and level of expression. In Drosophila , the UAS/GAL4 system has been instrumental in achieving spatial control, but the pursuit for the ideal system for temporal control is ongoing. One strategy used by many groups is to use GAL80. Two different kinds of GAL80 transgenic constructs have been reported but have never been compared directly. Here, we characterize and compare the repression ability of the GAL80ts and Tet-off GAL80 transgenes by two different assays. We found that GAL80ts was generally more efficient than Tet-off GAL80 and that the level of repression was dependent on the GAL4 driver, and did not necessarily correlate with expression level. We also investigated the level of expression upon inactivation of GAL80ts. The level and kinetics of inactivation were found to differ depending on the GAL4 driver and the stage of the life cycle, and in many cases the inactivation was incomplete. These results emphasize that it is essential to measure experimentally the level of expression under repressed and unrepressed states when multiple GAL4 drivers are used with GAL80 transgenes.
Ultrathin, soft, radiative cooling interfaces for advanced thermal management in skin electronics

In this paper, we report a generic thermal management strategy by using an ultrathin, soft, radiative-cooling interface (USRI) to cool down the temperature in wearable electronics through both radiative and non-radiative cooling strategy, achieving temperature reduction greater than 56°C. Meanwhile, the intrinsically soft nature of the USRI makes it possible to use as a conformable encapsulation layer and thus can be easily integrated with skin electronics based on current microfabrication process. Demonstrations include passive heat transfer of Joule heat for flexible electronics, improving working efficiency for epidermal device and stabling performance outputs for skin-interfaced wireless photoplethysmography sensors. These results offer an alternative strategy towards achieving effective thermal management in advanced skin interfaced electronics for high-power consumption, multifunctionally and wirelessly operated healthcare monitoring.
bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.
QSAR Modelling of Biological Activity in Cannabinoids with Quantum Similarity Combinations of Charge Fitting Schemes and 3D‐QSAR

Quantitative structure−activity relationship(QSAR) modeled the biological activities of 30 cannabinoids with quantum similarity descriptors(QSD) and Comparative Molecular Field Analysis (CoMFA). The PubChem[https://pubchem.ncbi. nlm.nih. gov/] database provided the geometries, binding affinities(Ki) to the cannabinoid receptors type 1(CB1) and 2(CB2), and the median lethal dose(LD50) to breast cancer cells. An innovative quantum similarity approach combining (self)‐similarity indexes calculated with different charge‐fitting schemes under the Topo‐Geometrical Superposition Algorithm(TGSA) were used to obtain QSARs. The determination coefficient(R2) and leave‐one‐out cross‐validation[Q2(LOO)] quantified the quality of multiple linear regression and support vector machine models. This approach was efficient in predicting the activities, giving predictive and robust models for each endpoint [pLD50: R2=0.9666 and Q2(LOO)=0.9312; pKi(CB1): R2=1.0000 and Q2(LOO)=0.9727, and pKi(CB2): R2=0.9996 and Q2(LOO)=0.9460], where p is the negative logarithm. The descriptors based on the electrostatic potential encrypted better electronic information involved in the interaction. Moreover, the similarity‐based descriptors generated unbiased models independent of an alignment procedure. The obtained models showed better performance than those reported in the literature. An additional 3D‐QSAR CoMFA analysis was applied to 15 cannabinoids, taking THC as a template in a ligand‐based approach. From this analysis, the region surrounding the amino group of the SR141716 ligand is the more favorable for the antitumor activity.
Anti-inflammatory effect of a pomegranate extract on LPS-stimulated HepG2 cells.

Pomegranate is an important source of bioactive molecules with proven beneficial effects on human health. The aim of this study was to investigate the potential anti-inflammatory effect of a pomegranate extract (PE), obtained from the whole fruit and previously characterized by Reversed Phase-Ultra High-Pressure Liquid Chromatography-High Resolution Mass Spectrometry (RP-UHPLC-HRMS), on HepG2 human hepatocellular carcinoma cells challenged with the lipopolysaccharide (LPS). In LPS-treated cells (1 µg/ml, 24h), the PE treatment (administered at the non-cytotoxic dose of 1 µg/ml, 24h) induced a significant reduction of three key pro-inflammatory cytokines, i.e. interleukin-8 (IL-8), interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α), at both gene expression (as assayed by real-time PCR) and secretion levels (by Enzyme-linked Immunosorbent Assay, ELISA). Although further in vivo studies are needed to prove its efficacy, this preliminary in vitro study suggests that the PE might be useful for ameliorating liver inflammation.
Forest tree species adaptation to climate across biomes: Building on the legacy of ecological genetics to anticipate responses to climate change

Intraspecific variation plays a critical role in extant and future forest responses to climate change. Forest tree species with wide climatic niches rely on the intraspecific variation resulting from genetic adaptation and phenotypic plasticity to accommodate spatial and temporal climate variability. A centuries‐old legacy of forest ecological genetics and provenance trials has provided a strong foundation upon which to continue building on this knowledge, which is critical to maintain climate‐adapted forests. Our overall objective is to understand forest trees intraspecific responses to climate across species and biomes, while our specific objectives are to describe ecological genetics models used to build our foundational knowledge, summarize modeling approaches that have expanded the traditional toolset, and extensively review the literature from 1994 to 2021 to highlight the main contributions of this legacy and the new analyzes of provenance trials. We reviewed 103 studies comprising at least three common gardens, which covered 58 forest tree species, 28 of them with range‐wide studies. Although studies using provenance trial data cover mostly commercially important forest tree species from temperate and boreal biomes, this synthesis provides a global overview of forest tree species adaptation to climate. We found that evidence for genetic adaptation to local climate is commonly present in the species studied (79%), being more common in conifers (87.5%) than in broadleaf species (67%). In 57% of the species, clines in fitness‐related traits were associated with temperature variables, in 14% of the species with precipitation, and in 25% of the species with both. Evidence of adaptation lags was found in 50% of the species with range‐wide studies. We conclude that ecological genetics models and analysis of provenance trial data provide excellent insights on intraspecific genetic variation, whereas the role and limits of phenotypic plasticity, which will likely determine the fate of extant forests, is vastly understudied.
A Procedure for Stability Analysis with Multivariate Data in Vegetable Crops

For overcoming the problem of dissimilar pattern of G × E interactions for different traits in a multi-trait selection programme, a simple procedure (based on principle component approach) is suggested for reducing the multivariate data set to one of univariate, which can then be subjected to usual stability analysis. The suggested procedure is used to analyse the bivarlate data on six varieties of onion and seven varieties of tomato for yield and total soluble solids, generated under All-India Coordinated Vegetable Improvement Project during 1980–82 at Five locations. In onion, the two best adapted varieties are Pusa Red and Line 102, both developed at the Indian Agricultural Research Iastitute, New Delhi, while Udaipur 103 is the most stable cultivar. Among the tomato varieties, the two best adapted varieties are La Bonita and Roma, the latter being the most stable variety.
The unexpected long period of elevated CH4 emissions from an inundated fen meadow ended only with the occurrence of cattail (Typha latifolia)

Drainage and agricultural use transform natural peatlands from a net carbon (C) sink to a net C source. Rewetting of peatlands, despite of high methane (CH4) emissions, holds the potential to mitigate climate change by greatly reducing CO2 emissions. However, the time span for this transition is unknown because most studies are limited to a few years. Especially, nonpermanent open water areas often created after rewetting, are highly productive. Here, we present 14 consecutive years of CH4 flux measurements following rewetting of a formerly long‐term drained peatland in the Peene valley. Measurements were made at two rewetted sites (non‐inundated vs. inundated) using manual chambers. During the study period, significant differences in measured CH4 emissions occurred. In general, these differences overlapped with stages of ecosystem transition from a cultivated grassland to a polytrophic lake dominated by emergent helophytes, but could also be additionally explained by other variables. This transition started with a rapid vegetation shift from dying cultivated grasses to open water floating and submerged hydrophytes and significantly increased CH4 emissions. Since 2008, helophytes have gradually spread from the shoreline into the open water area, especially in drier years. This process was periodically delayed by exceptional inundation and eventually resulted in the inundated site being covered by emergent helophytes. While the period between 2009 and 2015 showed exceptionally high CH4 emissions, these decreased significantly after cattail and other emergent helophytes became dominant at the inundated site. Therefore, CH4 emissions declined only after 10 years of transition following rewetting, potentially reaching a new steady state. Overall, this study highlights the importance of an integrative approach to understand the shallow lakes CH4 biogeochemistry, encompassing the entire area with its mosaic of different vegetation forms. This should be ideally done through a study design including proper measurement site allocation as well as long‐term measurements.
Mathematical foundations and algorithms for clique relaxations in networks

This dissertation establishes mathematical foundations for the properties exhibited by generalizations of cliques, as well as algorithms to find such objects in a network. Cliques are a model of an ideal group with roots in social network analysis. They have since found applications as a part of grouping mechanisms in computer vision, coding theory, experimental design, genomics, economics, and telecommunications among other fields. Because only groups with ideal properties form a clique, they are often too restrictive for identifying groups in many real-world networks. This motivated the introduction of clique relaxations that preserve some of the various defining properties of cliques in relaxed form. There are six clique relaxations that are the focus of this dissertation: s-clique, s-club, s-plex, k-core, quasi-clique , and k-connected subgraphs. Since cliques have found applications in so many fields, research into these clique relaxations has the potential to steer the course of much future research. The focus of this dissertation is on bringing organization and rigorous methodology to the formation and application of clique relaxations. We provide the first taxonomy focused on how the various clique relaxations relate on key structural properties demonstrated by groups. We also give a framework for how clique relaxations can be formed. This equips researchers with the ability to choose the appropriate clique relaxation for an application based on its structural properties, or, if an appropriate clique relaxation does not exist, form a new one. In addition to identifying the structural properties of the various clique relaxations, we identify properties and prove propositions that are important computationally. These assist in creating algorithms to find a clique relaxation quickly as it is immersed in a network. We give the first ever analysis of the computational complexity of finding the maximum quasi-clique in a graph. Such analysis identifies for researchers the appropriate set of computational tools to solve the maximum quasiclique problem. We further create a polynomial time algorithm for identifying large 2-cliques within unit disk graphs, a special class of graphs often arising in communication networks. We prove the algorithm to have a guaranteed 1/2-approximation ratio and finish with computational results.
Global asymptotic synchronization of a class of non-linear systems via sampled-data feedback

In this paper, we investigate the problem of using sampled-data feedback to synchronize a slave (driven) system with a master (driver) system. Based on the domination approach, both state-feedback and output-feedback control methods using sampled-data are proposed to make the tracking error converge to zero. The problem is of practical importance since in practice the system state is transmitted as sampled signal, and very often only the output is measurable. The effectiveness of the proposed approach is illustrated by the simulation for a chaotic Chua oscillator.
Impacts of the COVID‐19 pandemic on antimicrobial use in companion animals in an academic veterinary hospital in North Carolina

Antimicrobial resistance (AMR) in bacterial pathogens reduces the effectiveness of these drugs in both human and veterinary medicine, making judicious antimicrobial use (AMU) an important strategy for its control. The COVID‐19 pandemic modified operations in both human and veterinary healthcare delivery, potentially impacting AMU. The goal of this research is to quantify how antimicrobial drug prescribing practices for companion animals in an academic veterinary hospital changed during the pandemic. A retrospective study was performed using prescribing data for dogs and cats collected from the NC State College of Veterinary Medicine (NCSU‐CVM) pharmacy, which included prescriptions from both the specialty referral hospital and primary care services. Records (n = 31,769) for 34 antimicrobial drugs from 2019–2020—before and during the pandemic‐related measures at the NCSU‐CVM—were compared. The prescribed antimicrobials' importance was categorized using the FDA's Guidance for Industry (GFI #152), classifying drugs according to medical importance in humans. A proportional odds model was used to estimate the probability of more important antimicrobials being administered in patients seen during the pandemic versus before (i.e., critically important vs. highly important vs. important). Rates of AMU per week and per patient visit were also compared. During the pandemic, cumulative antimicrobials prescribed per week were significantly decreased in most services for dogs. Weekly rates for Highly Important antimicrobials were also significantly lower in dogs. For important and critically important antimicrobials, rates per week were significantly decreased in various services overall. Rates of antimicrobial administration per patient visit were significantly increased for Highly Important drugs. Patients in the internal medicine, dermatology, and surgery services received significantly more important antimicrobials during the pandemic than before, while cardiology patients received significantly less. These results suggest that the pandemic significantly impacted prescribing practices of antimicrobials for companion animals in this study.
Cardiac Related Psychosocial Distress and Traumatic Distress in Individuals with Advanced Heart Failure

Abstract Guided by the Transactional Model of Stress and Coping, this study explored relationships between cardiac related psychosocial distress, traumatic distress, and the number of lifetime traumas in people with advanced heart failure. Utilizing a cross-sectional survey design, a convenience sample of participants with advanced heart failure who received treatment in Washington, DC, completed standardized measures. Bivariate analysis showed a statistically significant difference in mean number of exposures and distress associated with advanced heart failure therapies and linear relationships between exposures and traumatic distress, distress and traumatic distress, and exposures and distress, as well as between the number of exposures and distress and traumatic distress and between the distress and traumatic distress. Multivariate analysis suggested that the higher the number of reported traumatic events, the higher the distress, and the higher the distress, the higher the traumatic distress. Social workers and allied team members should consider how a patient’s distress relates to traumatic distress in the advanced stage heart failure population.