Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic review protocol of yoga therapy as a modality in occupational therapy practice for adults experiencing mood disorders

Introduction Mood disorders can have a negative impact on daily functioning because cognitive deficits are exacerbated when individuals experience associated symptoms. Nevertheless, yoga therapy has been found to have enhancing features to well-being and quality of life. Occupational therapists are well positioned to include yoga as a modality to benefit clients experiencing mood disorders. However, literature on yoga interventions for mood disorders is underdeveloped causing an inadequate understanding of the health benefits. Thus, the aim of this study is to gain further knowledge associated with the implications of yoga as an intervention to increase participation in activities of daily living and enhance the quality of life of individuals experiencing mood disorders. This review will answer the following research question: can yoga therapy be used as an effective modality in occupational therapy practice to manage symptomatology related to mood disorders through increasing engagement in daily tasks? Methods and analysis OVID Medline, Embase as well as CINAHL Plus, Cochrane Library (Wiley), APA PsycINFO and Scopus will be explored to adhere to the following criteria: (1) studies discussing adults diagnosed with mood disorders, specifically bipolar and related disorders or depressive disorders as stated in the Diagnostic Statistical Manual of Mental Disorders-5; (2) studies discussing implementation of yoga therapy; (3) a correlation between mood disorders and effectiveness of yoga therapy. Ethics and dissemination Ethics approval is not applicable for this study, due to obtaining data from existing research articles. The completed manuscript will be submitted in a peer-reviewed journal for publication. PROSPERO registration number CRD42021283157
Growth in early infancy drives optimal brain functional connectivity which predicts cognitive flexibility in later childhood

Functional brain network organization, measured by functional connectivity (FC), reflects key neurodevelopmental processes for healthy development. Early exposure to adversity, e.g. undernutrition, affects neurodevelopment, observable via disrupted FC, and leads to poorer outcomes from preschool age onward. We assessed longitudinally the impact of early growth trajectories on developmental FC in a rural Gambian population from age 5 to 24 months. To investigate how these early trajectories relate to later childhood outcomes, we assessed cognitive flexibility at 3-5 years. We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC that in turn predicted cognitive flexibility at pre-school age. In contrast to previously studied developmental populations, this Gambian sample exhibited long-range interhemispheric FC that decreased with age. Our results highlight the measurable effects that poor growth in early infancy has on brain development and the subsequent impact on pre-school age cognitive development, underscoring the need for early life interventions throughout global settings of adversity.
FveDAD2 negatively regulates branch crowns by affecting abscisic acid metabolism through FveHB7 in woodland strawberry

Strigolactones (SLs) are a significant hormone in plant growth response, crucial for regulating branching. DECREASED APICAL DOMINANCE2 (DAD2) is a novel receptor of SL. Here, FveDAD2 in woodland strawberries (Fragaria vesca) as the receptor for SL was identified, and three FveDAD2-RNAi transgenic lines that exhibited the phenotype of multi-branched crowns and smaller fruits were obtained. Gene expression, phenotypic analysis, and yeast assays were used to investigate the function of FveDAD2 in regulating branch crowns in strawberries. Like the alpha/beta hydrolase D14, FveDAD2 interacts with FveSMXL7 and depends on SL. Furthermore, the yeast single-hybrid, GUS activity assay, and LUC assay results demonstrate that FveSMXL7 binds to the promoter of FveHB7 and repress its transcription. FveHB7, a homeobox transcription factor, negatively regulates the transcription of FveABA8’OH1, which encodes the enzyme that catabolizes abscisic acid (ABA). ABA contents were reduced in the shoot tips of the FveDAD2-RNAi lines, while treating wide-type plants with 20 μM ABA significantly suppressed the number of branches. In conclusion, we discovered a novel pathway of SL signaling to regulate branching through ABA. One-sentence summary FveDAD2 negatively regulates branch crowns by affecting abscisic acid levels by interacting with FveSMXL7 to regulate the expression of FveABA8’OH1 via the transcription factor FveHB7.
The proximity-based protein interaction landscape of the transcription factor p65 NF-κB / RELA and its gene-regulatory logics

The protein interactome of p65 / RELA, the most active subunit of the transcription factor (TF) NF-κB, has not been previously determined in living cells. Using p65-miniTurbo fusion proteins, we identified by biotin tagging > 350 RELA interactors from untreated and IL-1α-stimulated cells, including many TFs (47 % of all interactors) and > 50 epigenetic regulators belonging to different classes of chromatin remodeling complexes. According to point mutants of p65, the interactions primarily require intact dimerization rather than DNA binding properties. A targeted RNAi screen for 38 interactors and subsequent functional transcriptome and bioinformatics studies identified gene regulatory (sub)networks, each controlled by RELA in combination with one of the TFs ZBTB5, GLIS2, TFE3 / TFEB or S100A8 / A9. The remarkably large, dynamic and versatile high resolution interactome of RELA and its gene-regulatory logics provides a rich resource and a new framework for explaining how RELA cooperativity determines gene expression patterns. Highlights Identification of > 350 largely dimerization-dependent interactors of p65 / RELA by miniTurboID The interactome is dominated by transcription factors and epigenetic regulator complexes Functional validation of 38 high confidence interactors by targeted siRNA screen Identification of genetic networks regulated by RELA and six of its interactors in the IL-1α response
Beyond benchmarking: towards predictive models of dataset-specific single-cell RNA-seq pipeline performance

The advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies have sought to compare methods to answer this, but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of publicly available scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset? Here we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find both that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. Finally, we identify characteristics of scRNA-seq datasets that correlate with strong prediction performance that could guide when such prediction models may be useful.
The asymmetric transfers of visual perceptual learning determined by the stability of geometrical invariants

We could recognize the dynamic world quickly and accurately benefiting from extracting invariance from highly variable scenes, and this process can be continuously optimized through visual perceptual learning. It is widely accepted that more stable invariants are prior to be perceived in the visual system. But how the structural stability of invariants affects the process of perceptual learning remains largely unknown. We designed three geometrical invariants with varying levels of stability for perceptual learning: projective (e.g., collinearity), affine (e.g., parallelism), and Euclidean (e.g., orientation) invariants, following the Klein’s Erlangen program. We found that the learning effects of low-stability invariants could transfer to those with higher stability, but not vice versa. To uncover the mechanism of the asymmetric transfers, we used deep neural networks to simulate the learning procedure and further discovered that more stable invariants were learned faster. Additionally, the analysis of the network’s weight changes across layers revealed that training on less stable invariants induced more changes in lower layers. These findings suggest that the process of perceptual learning in extracting different invariants is consistent with the Klein hierarchy of geometries and the relative stability of the invariants plays a crucial role in the mode of learning and generalization.
Defense Against Adversarial Attacks for Neural Representations of Text

In this paper, we focus on defending against adversarial attacks for privacy-preserving Natural Language Processing (NLP) under a model partitioning scenario, where the model splits into a local, on-device part and a remote, cloud-based part. Model partitioning improves the scalability and protects the privacy of inputs into the model. However, we argue that privacy protection breaks during inference with model partitioning. In this paper, an adversary eavesdrops on the hidden representations output from the local devices and tries to use the representations to obtain private information from the input text. We study two types of adversarial attacks, i.e., adversarial classification and adversarial generation. Based on these two attack models, we correspondingly propose two defenses: defending the adversarial classification (DAC) and defending the adversarial generation (DAG). Specifically, the DAC and DAG approaches are both bilevel optimization-based defense methods. Both methods optimally modify a subpopulation of the neural representations that are subject to maximally decreasing the adversary’s ability. The representations trained with this bilevel optimization protect sensitive information from the adversary attack while maintaining their utility for downstream tasks. Our experiments show that both DAC and DAG approaches improve the performance of the main text classifier and achieve even higher privacy of neural representations compared with the current state-of-the-art methods (Coavoux et al., 2018; Y. Li et al., 2018).
Chemical Composition Fluctuations in the Gaseous and Particulate Phases of Urban Aerosols

From June 2006 to March 2007, the concentrations of water soluble inorganic particulates and their associated precursor gases were semi-continuously measured adjacent to a high traffic street in downtown Toronto, Canada. Measurements underwent extensive quality assurance and control protocols. Seasonal and diurnal variations in HNO3 and NH3 partitioning to NH4NO3 were observed. Long range transported air masses from southwest of Toronto were the predominant source of measured SO4 2for all seasons. The contributing sources of PM2.5 nitrate mass fluctuated between seasons: pNO3 was predominantly locally derived in the summer and resulted from long range transport in the winter. Comparison between measurements and ISORROPIA thermodynamic model predictions identified model weaknesses and was used to explore the effect of modulating primary gas concentrations on consequent particulate levels. SO2 emissions reductions were the most influential and direct method to reduce overall PM2.5 concentrations; however, limiting ammonia emissions was also another successful strategy.
Measurements of ice nucleating particles in the atmosphere : method development and results from field campaigns in North America and Europe

ii Preface . iv Table of Contents . viii List of Tables . xiv List of Figures . xvi List of Symbols . xxvii List of Abbreviations . xxx Acknowledgements . xxxii Dedication . xxxiv Chapter 1. Introduction . 1 1.1 Atmospheric aerosols . 1 1.2 Ice nucleation in the atmosphere . 3 1.2. 1 Mechanisms of ice nucleation . 3 1.2. 2 Types of ice nucleating particles . 4 1.3 How aerosols influence climate . 5 1.3. 1 Direct and indirect climate effects . 5 1.3. 2 The climatic impact of ice nucleating particles . 6 1.4 Motivation and overview of dissertation . 8 1.5 Chapter 1 figures . 10
Assessment of flood risk to performance of highway pavements

Highway pavements generally perform poorly after flood events. However, no study has addressed this in detail, so the aim of the current research was to evaluate pavement performance before and after a flood. Roughness against time data for the last 10–12 years for 34 000 km of roads in Queensland, Australia, were used to obtain an abrupt jump in roughness, which was termed a flood, and the time gap between two floods was considered as the likelihood of a flood. The distribution changes in roughness data before and after a flood were used to calculate flood consequences and then risk scores. The results revealed that a strong and high-traffic-loading road of high standard performed the best, which is consistent with previous studies. This information could thus help to identify flood-resilient pavements and upgrade flood-damaged roads to flood-resilient pavements. The flood consequence and risk scores were validated with actual data for two road groups, and reasonable agreement was found. The paper highli.
Risk analysis of mycotoxins by the joint FAO/WHO expert committee on food additives (JECFA)

The Joint FAO/World Health Organization (WHO) Expert Committee on Food Additives (JECFA) has been meeting regularly since 1956 to evaluate food additives, contaminants, naturally occurring toxicants and residues of veterinary drugs in food. To date, the Committee has evaluated more than 1 200 chemicals, including more than 20 contaminants. JECFA provides scientific advice to the Codex Alimentarius Commission (CAC), primarily through two of its general subject committees, the Codex Committee on Food Additives and Contaminants (CCFAC) and the Codex Committee on Residues of Veterinary Drugs in Foods (CCRVDF). JECFA also provides FAO and WHO member countries with scientific advice on chemicals in food. Because its primary client is CAC, its priorities are generally established by CCFAC and CCRVDF. To date, JECFA has evaluated three mycotoxins: aflatoxins B, G, and M; patulin and fumonisins. However, governments and CAC are becoming increasingly concerned about the presence of mycotoxins in foodstuffs, as evidenced by the presence of several additional mycotoxins on the CCFAC priority list: fumonisins, trichothecenes and zearalenone for evaluation, and ochratoxin A for reevaluation. Zearalenone will be evaluated at JECFA’s fiftythird meeting in June 1999, but the others are awaiting further data before they are placed on the agenda for evaluation.1 As with nearly all contaminants, the lack of relevant toxicological and epidemiological data is the primary impediment to the evaluation of most mycotoxins.
Quantifying relationships between governance, agriculture, and nature: empirical-statistical-and pattern-oriented modeling

In this research we studied empirical relationships between agricultural production dynamics and six quantitative World Bank governance indicators for 173 countries between 1975 and 2007. It is hypothesized that in countries with lower quality of governance, agricultural production increases are more likely to be achieved by area expansions than by increases in yields. We distinguished four groups of countries: those with both area and yield increases; those with increasing yields but decreasing area; those with decreasing yields but a growing area; and those with both declines in yields and area. We analyzed differences between these four groups, and also analyzed governanceproduction relationships within these groups. On average, quality of governance is low in countries with both area and yield increases and high in countries with increasing yields but decreasing area. Countries with declining yields were too few in number to allow for quantitative analyses. The analysis of governance-production relationships within the four groups suggests that countries with a lower quality of governance are more inclined to achieve production increases by expanding agricultural area rather than increasing yields. Additional explanatory value of governance indicators to agricultural production dynamics is generally small, but nevertheless significant in most cases. Our results suggest that, in order for agricultural production to increase without excessive expansions of agricultural area, governance issues should be resolved. Should we assume a causal relationship, the tendency of expanding cultivated area in less developed countries can be stopped by improving the quality of governance.
Novel applications of polarimetric radar in mixed-phase clouds and rainfall

This thesis presents novel uses of routinely measured dual polarisation radar variables to improve our understanding of the microphysics of mixed-phase clouds and rainfall. Fundamentally, a new variable L = - log10(1-Ρhv) is defined, which has preferable statistical properties to the co-polar correlation coefficient (Ρhv) and allows rigorous confidence intervals on Ρhv to be derived. The use of this variable also removes biases introduced by averaging many Ρhv samples and allows, for the first time, Ρhv to be used quantitatively. An emphasis is placed on how these variables can be used to retrieve microphysical information in embedded mixed-phase regions, which are particularly poorly understood at present. Using a combination of differential reflectivity (ZDR) and differential Doppler velocity (DDV), new statistics of the frequency of occurrence of mixed-phase clouds are presented. During a 3 month observational campaign, it is estimated that embedded mixed-phase clouds occur 26% of the time. A technique to remove the ambiguity of interpreting ZDR measurements when pristine oriented crystals are present amongst larger aggregate crystals is also presented. By combining L and ZDR, the contribution to the radar signal from pristine oriented crystals (C) and their \intrinsic" ZDR (ZPDRI) that would otherwise be hidden is retrieved.The results show that elevated ZDR above the melting layer was typically the result of pristine oriented crystals with ZPD RI between 3 and 7 dB, with varying contributions to the radar reflectivity. The retrieval provides an insight into the microphysics of embedded mixed-phase clouds using dual polarisation radar not before possible. Finally, the possibility of using L to measure the shape parameter (µ) in the gamma drop size distribution to improve rain rate retrievals is also investigated. It is shown that including drop oscillations is essential for this application. In a convective rain case study, µ appears to be substantially larger than 0 (an exponential DSD), and would result in an overestimated rain rate by up to 50% compared to if a simple exponential DSD is assumed. The potential to retrieve µ with operational radars is also discussed.
Design and analysis of composite Leaf Spring for light Weight Vehicle

In recent year automobile industries are mostly concentrating on weight reduction and in improving the riding quality. To reduce vehicle weight, three techniques have been studied rationalizing the body structure, utilizing light weight materials for parts and decreasing the size of the vehicles. In this approach by introducing composite materials into automobile industries, which is having low cost, high strength to weight ratio and excellent corrosive resistance can fulfill the requirement. The suspension leaf spring is one of the potential entities for weight reduction in automobiles as it results in large unstrung mass. The introduction of fiber reinforced plastics (FRP) is used to reduce the weight of the product without any reduction on load carrying capacity and spring rate. As the materials high strain energy storage capacity and high strength-to-weight ratio compared to steel, multi-leaf springs are being replaced by mono-leaf FRP spring.FRP springs also have excellent fatigue resistance and durability.
A Study of Dental Pathology in River Otters (Lontra canadensis) in A Study of Dental Pathology in River Otters (Lontra canadensis) in Arkansas Arkansas

River otters ( Lontra canadensis ) consume a variety of foods, including mussels, fishes, and crayfishes. These foods have hard body parts that cause wear of the teeth as the predator ages and incurs more damaging feeding experiences. This can lead to exposure of the pulp cavity and possible abscess and resorption of bone around the alveolus. Further, strong bites against harder parts of prey sometimes results in mechanical breakage, which can lead to pulpitis and severe pathology leading to tooth loss. We investigated the frequency of different forms of dental issues in a sample of 178 skulls of river otters collected from Arkansas. Canines and carnassial teeth, most used in capture and mastication of prey, were most severely affected. Based on post-traumatic tooth wear, it is evident that some otters are able to survive severe dental disease.
Environmental Impact Analysis of Dam Failure Based on Fuzzy Mathematical Theory

There are many dams in China,and the proportion of dangerous dams is nearly half.Once the dam break,it will bring disastrous consequences to downstream residents.To measure the dam risk is the product of outburst probability and dam failure.At present,in the risk study,the research about the environmental impact of dam failure is nearly empty at home and abroad.As result,this article will reference to related research,use the theoretical basis of the fuzzy mathematics to analyze and evaluate the environmental impact of the dam failure and analyze with reference examples.
Understanding Energy-Based Modeling of Proteins via an Empirically Motivated Minimal Ground Truth Model

Energy-based models (EBM) of sequences of evolutionarily related families of proteins have the ability to learn the generic constraints necessary to make novel functional sequences, which have been validated by in vivo experiments. However, these learned energy functions require re-scaling by a temperature parameter in order to sample novel functional sequences. Here, we generate data from a minimal model motivated by a wide array of empirical evidence for a synergistic cluster of amino acids, or sector, within a sequence. We find our setting captures salient learning behaviors similar to those exhibited by EBMs fitted to real proteins, namely the necessity for temperature tuning to increase generative performance. We discuss how this guides insight into the functional sequence space of proteins.
Lessons from Nowcasting GDP across the World

In economics, we need to forecast the present because reliable and comprehensive measures of the state of the economy are released with a substantial delay and considerable measurement error. Nowcasting exploits timely data to obtain early estimates of the state of the economy and updates these estimates continuously as new macroeconomic data are released. In this chapter, we describe how the framework used to nowcast GDP has evolved and is applied worldwide.