Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic Accuracy of a Trauma Risk Assessment Tool Among Geriatric Patients With Crash Injuries

Supplemental Digital Content is Available in the Text. ABSTRACT The Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) is a risk stratification tool. We evaluated the STTGMA's accuracy in predicting 30-day mortality and the odds of unfavorable clinical trajectories among crash-related trauma patients. This retrospective cohort study (n = 912) pooled adults aged 55 years and older from a single institutional trauma database. The data were split into training and test data sets (70:30 ratio) for the receiver operating curve analysis and internal validation, respectively. The outcome variables were 30-day mortality and measures of clinical trajectory. The predictor variable was the high-energy STTGMA score (STTGMAHE). We adjusted for the American Society of Anesthesiologists Physical Status. Using the training and test data sets, STTGMAHE exhibited 82% (95% CI: 65.5–98.3) and 96% (90.7–100.0) accuracies in predicting 30-day mortality, respectively. The STTGMA risk categories significantly stratified the proportions of orthopedic trauma patients who required intensive care unit (ICU) admissions, major and minor complications, and the length of stay (LOS). The odds of ICU admissions, major and minor complications, and the median difference in the LOS increased across the risk categories in a dose–response pattern. STTGMAHE exhibited an excellent level of accuracy in identifying middle-aged and geriatric trauma patients at risk of 30-day mortality and unfavorable clinical trajectories.
The preservation of Soyiru with dichloromethane extract of ginger

One of the major components of fermented soybeans is fat which is susceptible to oxidative rancidity. Since synthetic antioxidants are not without side effects, ginger extract was evaluated as an alternative. Soybean seeds were fermented to give soyiru, and 5gms of Soyiru treated with 0.5ml aqueous suspension of freshly prepared ginger extract(GE) or aqueous solution of Ascorbic acid(AA) giving 1600 and 2400ppm GE or 250 and 500ppm AA. Antioxidant activity (AOA) was quantified through Free fatty acid(FFA) and peroxide value(PV) of Soyiru stored at ambient temperature of 30±10C and aerobic mesophilic plate counts(AMPC) were determined. At 500ppm AA reduced FFA by 78.7%, PV by 93.3% while GE at 2400ppm reduced these parameters by 87.2% and 90.7% respectively. A two way ANOVA test (P<0.05) of AMPC revealed significant differences with respect to the preservative concentration and days of storage. These observations can be exploited to give acceptable products.
Identification of homozygosity-rich regions in the Holstein genome

In this study, 371 Holstein cows from six herds and 26 Holstein bulls, which were used in these herds, were genotyped by the Illumina BovineSNP50 array. For runs of homozygosity (ROH) identification, consecutive and sliding runs were performed by the detectRUNS and Plink software. The missing calls did not significantly affect the ROH data. The mean number of ROH identified by consecutive runs was 95.4 ± 2.7, and that by sliding runs was 86.0 ± 2.6 in cows, while this number for Holstein bulls was lower 58.9 ± 1.9. The length of the ROH segments varied from 1 Mb to over 16 Mb, with the largest number of ROH having a length of 1–2 Mb. Of the 29 chromosomes, BTA 14, BTA 16, and BTA 7 were the most covered by ROH. The mean coefficient of inbreeding across the herds was 0.111 ± 0.003 and 0.104 ± 0.004 based on consecutive and sliding runs, respectively, and 0.078 ± 0.005 for bulls based on consecutive runs. These values do not exceed those for Holstein cattle in North America. The results of this study confirmed the more accurate identification of ROH by consecutive runs, and also that the number of allowed heterozygous SNPs may have a significant effect on ROH data.
Psychiq and Wwwyzzerdd: Wikidata completion using Wikipedia

Despite its size, Wikidata remains incomplete and inaccurate in many areas. Hundreds of thousands of articles on English Wikipedia have zero or limited meaningful structure on Wikidata. Much work has been done in the literature to partially or fully automate the process of completing knowledge graphs, but little of it has been practically applied to Wikidata. This paper presents two interconnected practical approaches to speeding up the Wikidata completion task. The first is Wwwyzzerdd, a browser extension that allows users to quickly import statements from Wikipedia to Wikidata. Wwwyzzerdd has been used to make over 100 thousand edits to Wikidata. The second is Psychiq, a new model for predicting instance and subclass statements based on English Wikipedia articles. Psychiq’s performance and characteristics make it well suited to solving a variety of problems for the Wikidata community. One initial use is integrating the Psychiq model into the Wwwyzzerdd browser extension.
The impact of radar data assimilation on warm season rainfall forecasts for use in hydrologic models: examples from extreme rain events in Iowa

CHAPTER 1. GENERAL INTRODUCTION 1. Background and Overview 2. Thesis Organization CHAPTER 2. LITERATURE REVIEW Chapter 3. THE IMPACT OF RADAR DATA ASSIMILATION ON WARM SEASON HEAVY RAINFALL FORECASTS FOR USE IN HYDROLOGIC MODELS: EXAMPLES FROM EXTREME RAIN EVENTS IN IOWA 1. Abstract 2. Introduction 3. Methodology 1. Case Selection and WRF Set-Up 2. ARPS 3DVAR 3. Hydrology Model and Flash Flood Forecasts 4. Analysis Methods 4. Results 1. 6-Hour ETS and Rainfall Characteristics Analysis 2. 1-Hour ETS and Rainfall Characteristics Analysis 3. Hydrology Model Flash Flood Predictions 5. Conclusions 6. Acknowledgments 7. References 8. Figures 9. Tables CHAPTER 4. ADDITIONAL RESULTS: RADAR DATA ASSIMILATION VS. PROBABILITY-MATCHED RAINFALL FORECAST
GS-Net: Global Self-Attention Guided CNN for Multi-Stage Glaucoma Classification

Glaucoma is a common eye disease that leads to irreversible blindness unless timely detected. Hence, glaucoma detection at an early stage is of utmost importance for a better treatment plan and ultimately saving the vision. The recent literature has shown the prominence of CNN-based methods to detect glaucoma from retinal fundus images. However, such methods mainly focus on solving binary classification tasks and have not been thoroughly explored for the detection of different glaucoma stages, which is relatively challenging due to minute lesion size variations and high inter-class similarities. This paper proposes a global self-attention based network called GS-Net for efficient multi-stage glaucoma classification. We introduce a global self-attention module (GSAM) consisting of two parallel attention modules, a channel attention module (CAM) and a spatial attention module (SAM), to learn global feature dependencies across channel and spatial dimensions. The GSAM encourages extracting more discriminative and class-specific features from the fundus images. The experimental results on a publicly available dataset demonstrate that our GS-Net outperforms state-of- the-art methods. Also, the GSAM achieves competitive performance against popular attention modules.
Missing data estimation using polynomial Kernels

— In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statistical model. We presents two model in this paper : a linear model based on PCA and a non-linear model based on KPCA. The present work attempts to localize of non visible parts of an object, from the visible part and from the model, using the variability represented by the models. Both are applied to synthesis data and to cephalometric data with good results. 1 INTRODUCTION ATA compression, reconstruction, estimation and de-noising are common applications of linear Principal Component Analysis (PCA) [1,2] and Kernel PCA [3,4]. In the latter case, this is a non-trivial task as the results provided by Kernel PCA live in some high dimensional feature space. The main problem of KPCA reconstruction and denoising scheme is to retrieve the data in the input space whose image in Kernel Space is known : in fact, every point of the kernel space does not have a pre image in the input space. This is the pre-image problem [3-5]. In this paper, the estimation of a partially observed object in the input space, using a model learned in the feature space F. is addressed. Some part of the observation is known. To solve this problem, spatial relationships between the known part of the observation and the unknown one are represented in a statistical model and used to localize the unknown part. Those relationships are automatically learned in the model. Like in KPCA reconstruction problem, there are two possible approaches to solve this problem. The first one use an explicit mapping function ϕ, the second one use Kernel PCA making ϕ implicit. In the first case estimation consists in computing the inverse of ϕ (step 2 in Fig. 1) : a global model (polynomial, sigmoid) of the relations is an a-priori knowledge in this case. In the second case the problem is much more complicate (step 5 in Fig. 1). The paper is organized as follow : First, the extension of the PCA model to spatial relationship and partial object recognition is presented. Next, the KPCA model is described and the extension to partial object localization is given. Polynomial Kernels are detailed and results are illustrated with synthetic and real examples. 2 LINEAR PCA MODEL The extension of the linear PCA …
Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient

Simple Summary The increase in litter sizes in pigs obtained through selection has introduced challenges to breeders such as birth weight variability, piglet nursing, and piglet survival. Differences in birth weights can affect the later growth of piglets. We analysed the most important effects on the growth of piglets born in hyperprolific herds using a random regression coefficient model, which provides a smoother estimation of parameters compared to traditional fixed effects models and accounts for heterogeneous variances between measurements. Birth weight was the most influential factor on the final weight at 85 days of age. Litter size and parity also showed significant effect on the final weight. The results obtained using a random regression coefficient model could be encouraging for further application in pig growth analysis due to the ability of the model to describe individual growth patterns of piglets of variable birth weights and estimate the future growth of such piglets. In addition, the practical contribution of the paper is deeper insight into growth patterns of piglets from highly prolific sows under farm conditions, focusing on the need to control the variability of birth weight of large litters. Abstract Management of hyperprolific sows is challenging when it comes to controlling birth weight variability and piglet survival in large litters. The growth of low birth weight piglets can be compromised and have a negative impact on production efficiency. The objective of the study was to apply a random regression coefficient model to estimate the main effects of the growth of piglets of highly prolific sows. The dataset contained growth data for 360 piglets from 25 Pen Ar Lan Naima sows. In addition to routine procedures after farrowing, piglets were weighed five times: on day 1 after farrowing, on day 14 of life, at weaning on day 28, on day 30 of nursery period, and at the end of the nursery period when piglets were 83 days old. Data were treated as longitudinal, with body weight as the dependent variable. Fitting age as a quadratic regression within piglets in the random part of the model helped to determine the significant effect of birth weight, litter size, and parity on the growth of the piglets. Since the piglets from large litters often have non-uniform birth weights and this can affect further growth, the use of a random regression coefficient model is practical for analysing the growth of such piglets due to the ability to describe the individual growth pattern of every individual.
Sequencing , annotation and characterization of the 1 influenza ferret infectome 2

Alberto J. León, David Banner, Luoling Xu, Longsi Ran, Zhiyu Peng, Kang Yi, Chao Chen, Fengping 3 Xu, Jinrong Huang , Zhen Zhao, Zhen Lin, Stephen H.S. Huang, Yuan Fang, Alyson A. Kelvin, Ted 4 M. Ross, Amber Farooqui, David J. Kelvin 5 Division of Immunology, International Institute of Infection and Immunity, Shantou University Medical 6 College, Shantou, Guangdong, China 7 Division of Experimental Therapeutics, Toronto General Research Institute, University Health Network, 8 Toronto, Ontario, Canada 9 Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou, 10 Guangdong, China 11 Research & Cooperation Division, BGI-Shenzhen, Shenzhen, Guangdong, China 12 Department of Immunology, University of Toronto, Toronto, Ontario, Canada 13 Immune Diagnostics & Research (IDR), Toronto, Ontario, Canada 14 Center for Vaccine Research and Department of Microbiology and Molecular Genetics, University of 15 Pittsburgh, Pittsburgh, Pennsylvania, USA 16 Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Sardinia, Italy 17 Address correspondence to: David J. Kelvin, dkelvin@jidc.org 18
Fast Nitrogen Dioxide Sensing with Ultralow‐Power Nanotube Gas Sensors

The study reports fast, ultralow‐power operation of carbon nanotube‐based nitrogen dioxide (NO2) sensors enabled by nanotube self‐heating and transient sensing. The self‐heating effect in the nanotube channel significantly accelerates the desorption of gas molecules, reducing the sensor recovery time to a minute. As gas molecules re‐adsorb on the nanotube after cooling, the initial rate of the sensor transient is used to determine NO2 concentration within a few minutes. To accelerate and optimize the operation of the sensor, the study considered temperature profiles along the self‐heated carbon nanotube, their effect on different sensing regions, and a physical model‐based fit. As a result, the nanotube‐based NO2 sensor demonstrates recovery and readout times below 5 min and an extrapolated limit of detection below 10 ppb. The peak power consumption of this operation mode is below 6 µW. The combination of fast readout, fast recovery, low limit of detection, and ultralow power consumption demonstrated in this work shows strong promise of carbon nanotube‐based NO2 sensors in mobile or Internet‐of‐Things (IoT) applications.
Understanding normativity

Unlike Kelsen, Hart never had the ambition to explain what ‘enables’ or ‘conditions’ law’s normativity. Nor does Bix. Hart’s inscription within a philosophical context dominated by J.L. Austin’s theory of language, predisposed him to developing what I have called elsewhere a ‘downstream account’ of legal normativity: its focus is to unveil how law’s normative dimension manifests itself (notably through the ‘critical reflective attitude’), rather than what enables it. That law is normative is a given, both for Hart and for Bix. So far, so good: different accounts of legal normativity can and do proceed from different starting points, with different explanatory ambitions. The problems start when one dismisses the significance of these differences, or the possibility that fruitful insights may arise from a theory that explicitly questions one’s foundational premises. That there is an unbridgeable gap between is and ought is just one of those assumptions. This paper questions this assumption. It argues that legal theory has a lot to learn from various forms of non-reductive naturalism that will, among other things, help dislodge the idea that any account of the social practices that give rise to legal norms must presuppose intentional agency all the way through (as per conventionalist accounts).
The Genome-Wide Characterization of Alternative Splicing and RNA Editing in the Development of Coprinopsis cinerea

Coprinopsis cinerea is one of the model species used in fungal developmental studies. This mushroom-forming Basidiomycetes fungus has several developmental destinies in response to changing environments, with dynamic developmental regulations of the organism. Although the gene expression in C. cinerea development has already been profiled broadly, previous studies have only focused on a specific stage or process of fungal development. A comprehensive perspective across different developmental paths is lacking, and a global view on the dynamic transcriptional regulations in the life cycle and the developmental paths is far from complete. In addition, knowledge on co- and post-transcriptional modifications in this fungus remains rare. In this study, we investigated the transcriptional changes and modifications in C. cinerea during the processes of spore germination, vegetative growth, oidiation, sclerotia formation, and fruiting body formation by inducing different developmental paths of the organism and profiling the transcriptomes using the high-throughput sequencing method. Transition in the identity and abundance of expressed genes drive the physiological and morphological alterations of the organism, including metabolism and multicellularity construction. Moreover, stage- and tissue-specific alternative splicing and RNA editing took place and functioned in C. cinerea. These modifications were negatively correlated to the conservation features of genes and could provide extra plasticity to the transcriptome during fungal development. We suggest that C. cinerea applies different molecular strategies in its developmental regulation, including shifts in expressed gene sets, diversifications of genetic information, and reversible diversifications of RNA molecules. Such features would increase the fungal adaptability in the rapidly changing environment, especially in the transition of developmental programs and the maintenance and balance of genetic and transcriptomic divergence. The multi-layer regulatory network of gene expression serves as the molecular basis of the functioning of developmental regulation.
Joint Under-Sampling Pattern Optimization and Content-Based Reconstruction Network for Fast MRI Reconstruction

Magnetic Resonance Imaging (MRI) is frequently used by physicians for diagnosing human tissue, which requires focusing on specific content regions. However, existing compressed-sensing MRI (CS-MRI) reconstruction methods do not optimize under-sampling adaptively based on content or utilize k-space sensing resources effectively. To address these issues, we propose CBRecNet, a model that combines under-sampling pattern optimization with content-based reconstruction network. We extract multi-scale features of the content and calibrate the features of the reconstruction network using a pixel attention mechanism, which can guide the optimizable pattern to get a more suitable sampling pattern. To validate the proposed CBRecNet, we conduct extensive experiments on the CHAOS dataset with a four-stage learning strategy and the result demonstrates a very favorable outcome in CS-MRI. Our model provides a new approach to calibrated feature schemes based on meaningful medical content and demonstrates promising applications in CS-MRI.
Adaptive Importance Sampling Based Surrogation Methods for Bayesian Hierarchical Models, via Logarithmic Integral Optimization ∗

This paper investigates computational algorithms for Maximum a Posteriori (MAP) and Maximum Likelihood Estimation (MLE) inference of Bayesian Hierarchical Models (BHMs), via a uniﬁed formulation as a nonconvex and nondiﬀerentiable logarithmic integral optimization problem. Speciﬁcally, we explore situations where a BHM comprises density functions with intractable normalizers, a feature that can present signiﬁcant computational obstacles, particularly when combined with nonconvexity and nondiﬀerentiability, which are increasingly prevalent in contemporary applications of computational statistics. To deal with these challenges, we propose an eﬃcient algorithmic approach, termed Adaptive Importance Sampling-based Surrogation , to simultaneously handle nonconvexity and nondiﬀerentiability while also improving the sampling approximation of the intractable normalizer through variance reduction. Performance of this al-gorithm is guaranteed by our analysis which establishes an almost sure subsequential convergence to a necessary candidate for a local minimizer, referred to as a surrogation stationary point . We also demonstrate the eﬀectiveness of our algorithm through extensive numerical experiments, verifying its eﬃciency and stability in enabling more advanced BHMs where intractable normalizers arise as a result of enhanced modeling capability.
A New Classification Of UK Local Authorities Using 2001 Census Key Statistics

The 2001 Census has been successfully administered and the Census Organisations are currently engaged in processing the returns. A very large and rich dataset will be produced for the 58,789,194 people of the UK. The Census Area Statistics, for example, delivers 190 tables containing about 6 thousand unique counts relating to the characteristics of the UK population, for output areas and all higher geographies. This paper represents the first results of a project that aims to develop, in collaboration with the Office for National Statistics, a set of general purpose classifications at different geographic scales, including households, neighbourhoods, wards, local authorities and to link the classifications at different levels together. The paper reports on the methods used and results of a classification of the UK’s 434 Local Authorities, using the Key Statistics released in February 2003. This initial classification and description of methods will feed into the ONS/GROS/NISRA project to classify Local Authorities for the whole UK. Further data or digital versions of the classification system are available on request.