Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinician experiences with using assistive technology in brain injury rehabilitation: a survey of clinician capability, attitudes, and barriers

Abstract Background: The rise in assistive technology (AT) solutions to support people with an acquired brain injury (ABI) has warranted clinicians to build capability in assisting clients to select goal-centred AT. The study explored, amongst ABI clinicians, (a) capability, attitudes, and barriers with AT implementation, (b) age-related differences in technology self-efficacy and capability (c) strategies to support AT use in rehabilitation and (d) thematic analysis of AT-related experiences. Method: Mixed methods design. Online survey circulated to ABI clinicians across New South Wales, Australia, comprising purpose-designed items as well as the Modified Computer Self-Efficacy Scale (MCSES; range 0–100) Results: Clinicians (n = 123) were evenly distributed across decadal age groups. The majority were female (90%, n = 111) and one-third were occupational therapists. Clinicians scored strongly on the MCSES (Mdn = 76, IQR = 19), with younger age groups significantly associated with higher scores (H[3] = 9.667, p = .022). Most clinicians (92%) were knowledgeable of mainstream technology for personal use, but over half (65%) reported insufficient knowledge of suitable AT for clients. Clinicians reported positive attitudes towards AT, however, time to research and develop proficiency with a range of AT was the primary barrier (81%). Thematic analysis suggested that whilst the ideal AT experience is client-motivated requiring multidisciplinary guidance, the clinician role and experience with AT is evolving, influenced by rapid technological advancement and extrinsic opportunities to access AT. Conclusions: Whilst clinicians have positive attitudes towards AT, there is a gap in clinician implementation. There is need to support further resources to build clinician capability and access to AT.
The role of global reanalyses in climate services for health: Insights from the Lancet Countdown

As the linkages between extreme weather events, changes in climatic conditions and health impacts in exposed populations become clearer, so does the need for climate‐smart decisions aimed at making the public health sector more responsive and resilient. By integrating climate and health information, climate services for health provide robust decision‐support tools. The Lancet Countdown monitoring system uses global climate reanalyses products to track annual changes in a set of health‐related outcomes. In the monitoring system, multiple variables from reanalysis datasets such as ERA5 and ERA5‐Land are retrieved and processed to capture heatwaves, precipitation extremes, wildfires, droughts, warming and ecosystem changes across the globe and over multiple decades. This reanalysis‐derived information is then input into a hazard–exposure–vulnerability framework that delivers, as outcomes, indicators tracking the year‐by‐year impacts of climate‐related hazards on human mortality, labour capacity, physical activity, sentiment, infectious disease transmission, and food security and undernutrition. Building on the reanalysis gridded format, the indicators create worldwide ‘maps without gaps’ of climate–health linkages. Our experience shows that reanalysis datasets allow standardization across the climate information used in the framework, making the system potentially adaptable to multiple geographical scales. An ongoing challenge is to quantify how the inherent bias of global reanalyses influences indicator outcomes. We foresee the health sector as a key user of reanalysis products. Therefore, public health professionals and health impact modellers should be involved in the co‐development of future iterations of reanalysis datasets, to reach finer spatial resolutions and provide a wider set of health‐relevant climate variables.
Dual inhibition of EZH2 and G9A/GLP histone methyltransferases by HKMTI-1-005 promotes differentiation of acute myeloid leukemia cells

All-trans-retinoic acid (ATRA)-based differentiation therapy of acute promyelocytic leukemia (APL) represents one of the most clinically effective examples of precision medicine and the first example of targeted oncoprotein degradation. The success of ATRA in APL, however, remains to be translated to non-APL acute myeloid leukemia (AML). We previously showed that aberrant histone modifications, including histone H3 lysine 4 (H3K4) and lysine 27 (H3K27) methylation, were associated with this lack of response and that epigenetic therapy with small molecule inhibitors of the H3K4 demethylase LSD1/KDM1A could reprogram AML cells to respond to ATRA. Serving as the enzymatic component of Polycomb Repressive Complex 2, EZH2/KMT6A methyltransferase plays a critical role in normal hematopoiesis by affecting the balance between self-renewal and differentiation. The canonical function of EZH2 is methylation of H3K27, although important non-canonical roles have recently been described. EZH2 mutation or deregulated expression has been conclusively demonstrated in the pathogenesis of AML and response to treatment, thus making it an attractive therapeutic target. In this study, we therefore investigated whether inhibition of EZH2 might also improve the response of non-APL AML cells to ATRA-based therapy. We focused on GSK-343, a pyridone-containing S-adenosyl-L-methionine cofactor-competitive EZH2 inhibitor that is representative of its class, and HKMTI-1-005, a substrate-competitive dual inhibitor targeting EZH2 and the closely related G9A/GLP H3K9 methyltransferases. We found that treatment with HKMTI-1-005 phenocopied EZH2 knockdown and was more effective in inducing differentiation than GSK-343, despite the efficacy of GSK-343 in terms of abolishing H3K27 trimethylation. Furthermore, transcriptomic analysis revealed that in contrast to treatment with GSK-343, HKMTI-1-005 upregulated the expression of differentiation pathway genes with and without ATRA, while downregulating genes associated with a hematopoietic stem cell phenotype. These results pointed to a non-canonical role for EZH2, which was supported by the finding that EZH2 associates with the master regulator of myeloid differentiation, RARα, in an ATRA-dependent manner that was enhanced by HKMTI-1-005, possibly playing a role in co-regulator complex exchange during transcriptional activation. In summary, our results strongly suggest that addition of HKMTI-1-005 to ATRA is a new therapeutic approach against AML that warrants further investigation.
A moderated mediation analysis of depression and age on the relationship between resilience and frailty among HIV-positive adults

Background Given the continuing challenges frailty poses among people living with human immunodeficiency virus (HIV) (PLHIV), accumulating evidence suggests that frailty is linked to psychological factors. However, the mutual influences of resilience, depression, and frailty have not yet been clarified. This study aimed to identify the potential mechanistic pathway through which psychological factors mitigate frailty. Methods Data were collected from June to August 2019 by trained investigators through face-to-face interviews with 375 HIV-positive Chinese adults. Each participant completed structured questionnaires to collect data in respect of their socio-demographic characteristics, and levels of frailty, depression, and resilience. These assessment measures included a self-designed questionnaire, the Tilburg Frailty Indicator (TFI), the 10-item Center for Epidemiological Studies Depression Scale (CES-D-10), and the 10-item Connor-Davidson Resilience Scale (CD-RISC-10). SPSS PROCESS macro was used to analyze the mediation and moderated mediation models. Results The overall prevalence of frailty was 26.4%, and the prevalence of frailty among older and younger adults living with HIV was 22 and 31.4%, respectively. Mediation analysis showed that an association between resilience and frailty was mediated by depression, whereas resilience did not mediate the relationship between depression and frailty. Compared to physical frailty, depression was a stronger mediator of resilience to psychological frailty. We further found that age moderated the indirect effect of resilience on psychological frailty, with resilience being a stronger negative predictor of depression and depression being a stronger positive predictor of psychological frailty for older PLHIV than for younger PLHIV. Conclusion Lower levels of resilience and greater levels of depression may be significant risk factors for frailty among PLHIV. Levels of resilience influenced frailty directly and frailty was indirectly affected by depression. Therefore, it is recommended that PLHIV, especially older patients, should be encouraged to establish positive psychological coping strategies to slow the progression of frailty.
Yeast and derived products: their uses in preventing mycotoxins in poultry feeds

SUMMARY Poultry is an important source of human protein so research was done to identify methods of improving the feed quality and overcoming the challenge of using this feed type. Poultry feed is commonly contaminated with certain organisms like fungi which produce a group of secondary metabolites called mycotoxins, which affect the quality of the feed. These mycotoxins are highly toxic even in a small concentration like ochratoxin A (OTA), deoxynivalenol, and T-2 toxin causing a wide range of disorders such as hepatic and kidney injury, etc. This review aims to provide an insight into this problem and the use of yeast and its derived products as a method to overcome these mycotoxins. In the literature, yeast is applied to poultry feed as it promotes the growth of broilers when added to the diet and enhances immunity by increasing the antibody titre and macrophages. It increases the size of immunity organs like the thymus gland and the spleen, improves gut immunity, and decreases the proportion of certain microorganisms like E. coli and Salmonella. Lachancea Thermotoleran is an example of a yeast strain that produces volatile organic compounds like 2-phenyl ethanol, which decreases the production of OTA by certain percentages according to the applied conditions. Therefore, the inclusion of yeast was applied as a promising feed additive to overcome mycotoxins in poultry diets because of its many benefits.
Introduction to the Theory of Random Particle Methods for Boltzmann Equation

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Introduction to the theory of random particle methods for Boltzmann equation Benoît Perthame
Spatiotemporal Snowfall Trends in Central New York

AbstractCentral New York State, located at the intersection of the northeastern United States and the Great Lakes basin, is impacted by snowfall produced by lake-effect and non-lake-effect snowstorms. The purpose of this study is to determine the spatiotemporal patterns of snowfall in central New York and their possible underlying causes. Ninety-three Cooperative Observer Program stations are used in this study. Spatiotemporal patterns are analyzed using simple linear regressions, Pearson correlations, principal component analysis to identify regional clustering, and spatial snowfall distribution maps in the ArcGIS software. There are three key findings. First, when the long-term snowfall trend (1931/32–2011/12) is divided into two halves, a strong increase is present during the first half (1931/32–1971/72), followed by a lesser decrease in the second half (1971/72–2011/12). This result suggests that snowfall trends behave nonlinearly over the period of record. Second, central New York spatial snowfall pa.
Large eddy simulation of homogeneous shear flows with several subgrid‐scale models

In this article, large eddy simulation is used to simulate homogeneous shear flows. The spatial discretization is accomplished by the spectral collocation method and a third‐order Runge–Kutta method is used to integrate the time‐dependent terms. For the estimation of the subgrid‐scale stress tensor, the Smagorinsky model, the dynamic model, the scale‐similarity model and the mixed model are used. Their predicting performance for homogeneous shear flow is compared accordingly. The initial Reynolds number varies from 33 to 99 and the initial shear number is 2. Evolution of the turbulent kinetic energy, the growth rate, the anisotropy component and the subgrid‐scale dissipation rate is presented. In addition, the performance of several filters is examined. Copyright © 2005 John Wiley & Sons, Ltd.
An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

This study deals with a new strategy of the re-assignment for multi-robot seamless coverage tasks using the concept of propagation in a multi-robot surveillance system (MRSS). In the context of MRSSs, multi-robot coverage tasks play a critical role. These tasks require generating paths for two or more robots to cover an entire area, with the objective of minimizing the time needed to complete the task. However, over time, robots may need to be excluded from coverage missions due to issues such as battery charging or malfunctions. It is important to handle these situations efficiently in order to maintain the completeness and balance of the coverage mission. Typically, it can be resolved by either recomputing the coverage algorithm for the remaining robots or redistributing the coverage task of the excluded robot to its neighbors. However, in the proposed method, the amount of coverage area of the excluded robot is equally and efficiently assigned to the remaining robots. First off, a relational graph between robots and a tree based on the excluded robot are sequentially constructed to necessarily know how the robots are geometrically arranged in the given area centered on the excluded robot. The excluded robot becomes the root of the tree, and the depth of the tree indicates the proximity of the coverage areas. Subsequently, the amount of the original coverage area of the excluded robot can be differently assigned to its nearest neighbor robots according to the size of the subtree. Then, the coverage area of the robots corresponding to the second level of the tree are added from the partial coverage area of their parent robot to keep their coverage area balanced, respectively. The similar process is continuously performed, such as ‘propagation’, until the re-assignment of the coverage area over the leaf nodes is complete. Finally, balanced coverage area is re-assigned to the remaining robots, which is time-efficiently computed. Simulations were performed on two occupancy grid maps that were acquired from a simultaneous localization and mapping method. The proposed method was evaluated against conventional methods on three factors such as the balanced re-assignment of the coverage area (Balancing), the variation of the individual coverage area before and after the re-assignment process (Seamless Coverage), and the total computational efficiency over time (Time-efficiency). The coverage area was uniformly re-allocated after the proposed method was applied. In addition, the proposed method had a short calculation time and enables seamless coverage even after re-allocation. In the future, probabilistic maps related to the importance rate, accident rate, and crowds in the coverage area will also be taken into consideration.
Cox's Periodic Regression Model

Cox's regression model has been successfully used for censored survival data. It can be adapted to model a counting process having a periodic underlying intensity. In survival analysis, the asymptotic properties, as studied by Andersen and Gill, correspond to a large number of processes running parallel over the same time interval. Here a single point process is observed over a large number of successive periods. Cox's model can easily be adapted to this situation and conditions are given which ensure the estimators have the classical large sample properties. Proofs use both martingale techniques and theorems for convergence of empirical probability measures. Finally, an example concerning the feeding pattern of domestic rabbits is included.
Formation and Evolution of Red Hδ-strong Galaxies in Distant Clusters: Two Different Evolutionary Paths to S0 Galaxies?

We numerically investigate the spectrophotometric evolution of galaxies with a variety of different star formation histories and degrees of dust extinction in order to elucidate the origin of the "red Hδ-strong" (RHDS) galaxies—characterized by strong [EW(Hδ) > 3 Å] Balmer line absorption but with colors as red as the dormant E/S0 systems—first observed in distant clusters of galaxies. Specifically, we investigate the evolution in the [(B-R), EW(Hδ)]-plane of two different galaxy models: a "truncation" model in which a galaxy with a declining star formation rate (typical of a normal spiral) has its star formation abruptly truncated, and a "starburst" model in which a galaxy undergoes a secondary burst of star formation that is abruptly halted. This leads to the following three main results: (1) Both models pass through the zone inhabited by the RHDS galaxies in this plane, but the "truncation" models cannot explain the most extreme cases, which have EW(Hδ) > 6 Å. (2) The reddest RHDS galaxies can only be explained by "truncation" or "starburst" models that have very heavy dust extinction (Av > 0.5 mag). (3) Irrespective of whether dust effects are included, the "truncation" models are incapable of reproducing the very blue (B-R < 1.6) analogs of the RHDS galaxies. By comparing these results with recent Hubble Space Telescope-based morphological and ground-based spectroscopic studies of distant cluster galaxies, we propose that there are two possible different evolutionary paths for the transformation of cluster spirals into S0 galaxies.
A Backward-Forward Sweep Based Power Flow Algorithm for Generalized AC-DC Distribution Network With Distributed Generations

Developing a power flow (PF) methodology for the AC-DC distribution network is complex but important, especially due to the involvement of power converters. In this work, a Backward-Forward Sweep (BFS) approach developed based on graph theory and matrix algebra is proposed for solving the PF for both the radial and mesh configured AC-DC distribution networks with distributed energy resources (DERs). For this purpose, first, the per-unit current, voltage, and power model of the 3-<inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> pulse width modulated (PWM) rectifier, 3-<inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> PWM inverter, and DC/DC power converter have been developed. Then, a generalized sensitivity matrix has been developed for computing the loop breakpoint, PV breakpoint, and V<inline-formula><tex-math notation="LaTeX">$^{dc}$</tex-math></inline-formula> breakpoint injections simultaneously allowing the approach to work for both mesh and radial networks with DERs modeled as PQ, P, PV, and V<inline-formula><tex-math notation="LaTeX">$^{dc}$</tex-math></inline-formula> buses. The major benefits of the proposed algorithm are that it can incorporate various functional/control modes of the aforementioned converters. The feasibility of the proposed power flow algorithm has been explored on AC-DC distribution test networks. The test results show that the approach is feasible and precise.
Maximum likelihood inference on a mixed conditionally and marginally specified regression model for genetic epidemiologic studies with two‐phase sampling

Summary. Two‐phase stratified sampling designs can reduce the cost of genetic epidemiologic studies by limiting expensive ascertainments of genetic and environmental exposure to an efficiently selected subsample (phase II) of the main study (phase I). Family history and some covariate information, which may be cheaply gathered for all subjects at phase I, can be used for sampling of informative subjects at phase II. We develop alternative maximum likelihood methods for analysis of data from such studies by using a novel regression model that permits the estimation of ‘marginal’ risk parameters that are associated with the genetic and environmental covariates of interest, while simultaneously characterizing the ‘conditional’ risk of the disease associated with family history after adjusting for the other covariates. The methods and appropriate asymptotic theories are developed with and without an assumption of gene–environment independence, allowing the distribution of the environmental factors to remain non‐parametric. The performance of the alternative methods and of sampling strategies is studied by using simulated data involving rare and common genetic variants. An application of the methods proposed is illustrated by using a case–control study of colorectal adenoma embedded within the prostate, lung, colorectal and ovarian cancer screening trial.
OPTIMIZATION MODELS FOR SHAPE-CONSTRAINED FUNCTION ESTIMATION PROBLEMS INVOLVING NONNEGATIVE POLYNOMIALS AND THEIR RESTRICTIONS

OF THE DISSERTATION Optimization models for shape-constrained function estimation problems involving nonnegative polynomials and their restrictions by DÁVID PAPP Dissertation Director: Dr. Farid Alizadeh In this thesis function estimation problems are considered that involve constraints on the shape of the estimator or some other underlying function. The function to be estimated is assumed to be continuous on an interval, and is approximated by a polynomial spline of given degree. First the estimation of univariate functions is considered under constraints that can be represented by the nonnegativity of affine functionals of the estimated function. These include bound constraints, monotonicity, convexity and concavity constraints. A general framework is presented in which arbitrary combination of such constraints, along with monotonicity and periodicity constraints, can be modeled and handled in a both theoretically and practically efficient manner, using second-order cone programming and semidefinite programming. The approach is illustrated by a variety of applications in statistical estimation. Wherever possible, the numerical results are compared to those obtained using methods previously proposed in the statistical literature. Next, multivariate problems are considered. Shape constraints that are tractable in the univariate case are intractable in the multivariate setting. Hence, instead of following the univariate approach, polyhedral and spectrahedral (semidefinite) approximations are proposed, which are obtained by replacing nonnegative splines by piecewise weighted-sum-of-squares polynomial splines. A decomposition method for solving very large scale multivariate problems is also investigated. In order to include more constraints in the same unified framework, the notion of weighted-sumof-squares functions is generalized to weighted-sum-of-squares cones in finite-dimensional real linear spaces endowed with a bilinear multiplication. It is shown that such cones are semidefinite representable, and the representation is explicitly constructed. An example of a constraint representable in this form is the pointwise positive semidefiniteness of a time-varying covariance matrix. Finally, seeking a method to circumvent the use of semidefinite programming in the solution of optimization problems over nonnegative and weighted-sum-of-squares polynomials, we study the conditions of optimality (for homotopy methods), as well as barrier functions (for barrier methods)