Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermoneutral housing does not accelerate metabolic dysfunction-associated fatty liver disease in male or female mice fed a Western diet

Objective Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a growing cause of mortality and morbidity and encompasses a spectrum of liver pathologies. Potential therapeutic targets have been identified and are currently being pre-clinically and clinically tested. However, while dozens of preclinical models have been developed to recapitulate various stages of MAFLD, few achieve fibrosis using an experimental design that mimics human pathogenesis. We sought to clarify whether the combination of thermoneutral (TN) housing and consumption of a classical Western diet (WD) would accelerate the onset of MAFLD and progression in male and female mice. Methods Male and female C57Bl/6J mice were fed a nutrient-matched low-fat control or Western diet (41% Kcal from fat, 43% carbohydrate and 0.2% cholesterol; WD) starting at ∼12 wk of age for a further 16 wk. Mice were divided and housed with littermates at either standard temperature (TS; 22°C) or thermoneutral conditions (TN; ∼29°C). Mice underwent tests for glucose tolerance, insulin sensitivity and body composition, as well as intestinal permeability. Following tissue harvest, circulating and liver markers of hepatic disease progression toward steatosis and fibrosis were determined. Results While male mice housed at TN and fed a WD were significantly heavier than TS -housed control animals, no other differences in body weight or composition were observed. WD-fed females housed under TN conditions had higher circulating LDL-cholesterol; however, there were no other significant differences between TN and TS -housing in circulating or hepatic lipid levels. While WD-fed TN males had higher ALT levels, no other differences in markers of liver injury or disease progression were observed. Moreover, females housed at TN conditions and fed a WD remained significantly protected against the induction of fibrosis compared to male counterparts. Interestingly, sex-specific differences were observed in markers of glucose and insulin tolerance, where TN housing and WD feeding resulted in hyperglycemia and impaired insulin responsiveness in both sexes, but glucose intolerance only in male mice. Conclusions While TN housing has been demonstrated to exacerbate high fat-induced hepatic steatosis and inflammation in male and female mice, coupling TN housing with a WD for 16 wk was not sufficient to augment fatty liver progression in male or female mice. Highlights Thermoneutral housing and Western diet feeding does not progress to NASH Female mice are not more susceptible to obesity induced fatty liver under these conditions Temperature and diet had sex-specific effects on glucose tolerance and insulin sensitivity
A fluid biomarker reveals loss of TDP-43 splicing repression in pre-symptomatic ALS

Loss of TAR DNA-binding protein 43 kDa (TDP-43) splicing repression is well-documented in postmortem tissues of amyotrophic lateral sclerosis (ALS), yet whether this abnormality occurs during early-stage disease remains unresolved. Cryptic exon inclusion reflects functional loss of TDP-43, and thus detection of cryptic exon-encoded peptides in cerebrospinal fluid (CSF) could reveal the earliest stages of TDP-43 dysregulation in patients. Here, we use a newly characterized monoclonal antibody specific to a TDP-43-dependent cryptic epitope (encoded by the cryptic exon found in HDGFL2) to show that loss of TDP-43 splicing repression occurs in C9ORF72-associated ALS, including pre-symptomatic mutation carriers. In contrast to neurofilament light and heavy chain proteins, cryptic HDGFL2 accumulates in CSF at higher levels during early stages of disease. Our findings indicate that loss of TDP-43 splicing repression occurs early in disease progression, even pre-symptomatically, and that detection of HDGFL2’s cryptic neoepitope may serve as a prognostic test for ALS which should facilitate patient recruitment and measurement of target engagement in clinical trials.
Stem Cell Factor and Granulocyte Colony-Stimulating Factor Promote Remyelination in the Chronic Phase of Severe Traumatic Brain Injury

Severe traumatic brain injury (TBI) causes long-term disability and death in young adults. White matter is vulnerable to TBI damage. Demyelination is a major pathological change of white matter injury after TBI. Demyelination, which is characterized by myelin sheath disruption and oligodendrocyte cell death, leads to long-term neurological function deficits. Stem cell factor (SCF) and granulocyte colony-stimulating factor (G-CSF) treatments have shown neuroprotective and neurorestorative effects in the subacute and chronic phases of experimental TBI. Our previous study has revealed that combined SCF and G-CSF treatment (SCF + G-CSF) enhances myelin repair in the chronic phase of TBI. However, the long-term effect and mechanism of SCF + G-CSF-enhanced myelin repair remain unclear. In this study, we uncovered persistent and progressive myelin loss in the chronic phase of severe TBI. SCF + G-CSF treatment in the chronic phase of severe TBI enhanced remyelination in the ipsilateral external capsule and striatum. The SCF + G-CSF-enhanced myelin repair is positively correlated with the proliferation of oligodendrocyte progenitor cells in the subventricular zone. These findings reveal the therapeutic potential of SCF + G-CSF in myelin repair in the chronic phase of severe TBI and shed light on the mechanism underlying SCF + G-CSF-enhanced remyelination in chronic TBI.
Detecting cultural evolution in a songbird species using community science data and computational modelling

Song in oscine birds is learned across generations, and aspects of the song-learning process parallel genetic transmission: variation can be introduced into both cultural and genetic traits via copy-error, and both types of traits are subject to drift and selective pressure. Similarly to allele frequencies in population genetics, observing frequencies of birdsong features can improve our understanding of cultural transmission and evolution. Uniquely, community-science databases of birdsong provide rich spatiotemporal data with untapped potential to evaluate cultural evolution in songbirds. Here we use both community-science and field-study recordings of chipping sparrows to examine trends across nearly seven decades of song. We find that some syllable types tend to persist in the population for much longer than others. Persistent songs tend to contain more syllables of shorter duration than songs that were observed across fewer years. To draw inferences about the effects of learning biases on chipping sparrow syllables, we construct a spatially explicit agent-based model of song learning. By comparing our empirical analysis to simulated song distributions using three different song-learning strategies—neutral transmission, conformity bias, and directional selection—we suggest that chipping sparrows are unlikely to select tutors neutrally or with a conformity bias and that they learn their songs with a remarkably low copy-error rate.
Environment as a limiting factor of the historical global spread of mungbean

While the domestication history has been investigated in many crops, the process of cultivation range expansion and factors governing this process received relatively little attention. Here using mungbean (Vigna radiata var. radiata) as a test case, we investigated the genomes of more than one thousand accessions to illustrate climatic adaptation’s role in dictating the unique routes of cultivation range expansion. Despite the geographical proximity between South and Central Asia, genetic evidence suggests mungbean cultivation first spread from South Asia to Southeast, East, and finally reached Central Asia. Combining evidence from demographic inference, climatic niche modeling, plant morphology, and records from ancient Chinese sources, we showed that the specific route was shaped by the unique combinations of climatic constraints and farmer practices across Asia, which imposed divergent selection favoring higher yield in the south but short-season and more drought-tolerant accessions in the north. Our results suggest that mungbean did not radiate from the domestication center as expected purely under human activity, but instead the spread of mungbean cultivation is highly constrained by climatic adaptation, echoing the idea that human commensals are more difficult to spread through the south-north axis of continents.
Deep learning for music information retrieval in limited data scenarios

While deep learning (DL) models have achieved impressive results in settings where large amounts of annotated training data are available, overfitting often degrades performance when data is more limited. To improve the generalisation of DL models, we investigate “data-driven priors” that exploit additional unlabelled data or labelled data from related tasks. Unlike techniques such as data augmentation, these priors are applicable across a range of machine listening tasks, since their design does not rely on problem-specific knowledge. We first consider scenarios in which parts of samples can be missing, aiming to make more datasets available for model training. In an initial study focusing on audio source separation (ASS), we exploit additionally available unlabelled music and solo source recordings by using generative adversarial networks (GANs), resulting in higher separation quality. We then present a fully adversarial framework for learning generative models with missing data. Our discriminator consists of separately trainable components that can be combined to train the generator with the same objective as in the original GAN framework. We apply our framework to image generation, image segmentation and ASS, demonstrating superior performance compared to the original GAN. To improve performance on any given MIR task, we also aim to leverage datasets which are annotated for similar tasks. We use multi-task learning (MTL) to perform singing voice detection and singing voice separation with one model, improving performance on both tasks. Furthermore, we employ meta-learning on a diverse collection of ten MIR tasks to find a weight initialisation for a “universal MIR model” so that training the model on any MIR task with this initialisation quickly leads to good performance. Since our data-driven priors encode knowledge shared across tasks and datasets, they are suited for high-dimensional, end-to-end models, instead of small models relying on task-specific feature engineering, such as fixed spectrogram representations of audio commonly used in machine listening. To this end, we propose “Wave-U-Net”, an adaptation of the U-Net, which can perform ASS directly on the raw waveform while performing favourably to its spectrogrambased counterpart. Finally, we derive “Seq-U-Net” as a causal variant of WaveU-Net, which performs comparably to Wavenet and Temporal Convolutional Network (TCN) on a variety of sequence modelling tasks, while being more computationally efficient.
Background error statistics for aerosols

As part of the European initiative for Global Monitoring for Environment and Security (GMES), the Global and regional Earth‐system Monitoring using Satellite and in situ data (GEMS) project has been funded to provide forecasts and analysis of atmospheric constituents such as aerosols, and greenhouse and reactive gases. In this context, the development of an aerosol analysis system based on a four‐dimensional variational (4D‐Var) assimilation was started in March 2005 at the European Centre for Medium‐Range Weather Forecasts (ECMWF). As part of the main building blocks of the system, an error background covariance matrix for aerosol mixing ratio was built based on background statistics constructed from aerosol forecasts. These statistics were generated from the differences between the 48‐hour and 24‐hour forecasts of aerosol mixing ratios for sea salt, desert dust and continental particulate, using the NMC method. This method has been widely used by many numerical weather prediction centres to build background error statistics for state variables. This is the first time, to our knowledge, that it has been applied to a novel variable such as an aerosol mixing ratio, in the context of a global model. Aerosol assimilation systems have been successfully run with either observationally‐derived or model‐prescribed error covariance matrices with pre‐assigned correlation lengths. In this study, the aerosol vertical and horizontal structures of these error correlations as derived with the aid of a state‐of‐the‐art model are investigated and discussed in depth. Successively these error correlations are modelled through a spectral/wavelet approach and used to construct a background error covariance matrix. Application of this background matrix in the context of a single observation 4D‐Var experiment produced successful preliminary analyses of the total aerosol mixing ratio. Copyright © 2007 Royal Meteorological Society
Hubble Space Telescope Spectroscopy of the Dwarf Nova RX Andromedae. I. The Underlying White Dwarf

We obtained Hubble Space Telescope Goddard High Resolution Spectrograph phase-resolved spectroscopic observations of the dwarf nova RX Andromedae at three times in its outburst cycle: (1) near the end of an extraordinarily deep and long dwarf nova quiescence, 3 months after the last outburst; (2) during the rise to outburst; and (3) near the end of a decline from outburst. The spectral wavelength range covered was 1149-1435 Å. All of the spectra are dominated by absorption lines with weak to moderately strong emission wings due to the continued presence of disk material. Uncertainties in line velocities preclude a K1 determination or mass information. Our best-fitting model yielded Twd/1000 = 34.0 ± 0.1 K, log g = 8.0 ± 0.1, and Vrot= 600 km s-1. The Teff value is very similar to the Teff of the white dwarf in U Geminorum, but the rotational velocity appears to be higher than U Gem's value. We report approximate subsolar chemical abundances of carbon and silicon for RX And with C being 0.05 times solar and Si being 0.1 times solar while other elements are at essentially their solar values. However, accurate abundances are complicated by line emission, and we cannot exclude the possibility that the abundances of all species are essentially at the solar values. We see no evidence of thermonuclear-processed abundance ratios. If the white dwarf mass is 0.8 M☉, then the corresponding white dwarf cooling age, 4 × 106 years, is a lower limit to the age of this cataclysmic variable (CV). If the peculiar line features seen in the spectrum on the late decline from outburst are inverse P Cygni in nature, then infall velocities of ~ 2000 km s-1 are indicated during the decline from outburst. We compare the surface properties of the RX And white dwarf with the properties of other CV degenerates studied to date with the Hubble Space Telescope, the Hopkins Ultraviolet Telescope, and the International Ultraviolet Explorer.
Numerical and experimental characterization of a family of two–roll–mill flows

Two–dimensional creeping flow generated by a two–roll mill in a cylindrical domain is investigated numerically and experimentally for arbitrary ratios Ø of the angular velocities of the inner cylinders. The Stokes flow in the two–roll–mill domain is computed for various values of Ø using a least–squares approach, and it is compared with experimental visualizations of the flow field. This combined numerical and experimental study, together with critical–point concepts, has been used to uncover the complete sequence of complex transitions between the extreme cases of counter– and co–rotation. The results lead us to speculate that a continuous variation between the two end states may lead to an efficient chaotic advective mixer.
ON A CLASS OF ELLIPTIC PSEUDODIFFERENTIAL OPERATORS DEGENERATE ON A SUBMANIFOLD

There are investigated elliptic pseudodifferential operators p(x, D) which are degenerate on a submanifold Γ of any codimension. Under certain further assumptions, for the operator which is obtained by adjoining to p(x, D) boundary and coboundary conditions on the submanifold Γ, there are constructed left and right regularizers, and theorems on hypoellipticity and local solvability are proved. In case p(x, D) is defined on a smooth compact manifold it is shown to be noetherian on special weighted spaces of Sobolev type. Bibliography: 24 references.
Generalized material models in TLM .I. Materials with frequency-dependent properties

This paper presents the fundamentals of a unified approach for the treatment of general material properties in time-domain simulation based on transmission-line modeling (TLM). Linear frequency-dependent isotropic materials are dealt with in the first instance. The iteration schemes for one-dimensional (1-D) and three-dimensional (3-D) models are developed from Maxwell's curl equations and the constitutive relations. Results are presented showing the accuracy of this approach.
Convergence of finite volume schemes for Poisson's equation on nonuniform meshes

An error analysis of a family of cell-center finite volume schemes for Poisson’s equation on Cartesian product nonuniform meshes in two dimensions is presented. Optimal-order error estimates are derived in the discrete $H^1$ norm under minimum smoothness requirements on the exact solution and without any additional assumption on the regularity of the mesh. On quasi-uniform meshes analogous estimates are obtained in the maximum norm.
Capacitances and singularities of the unit triangle, square, tetrahedron and cube

The boundary element method has been used to obtain the electrical capacitances of the unit equilateral triangle, square, regular tetrahedron and cube. For each of these objects, a series of values of the number N of segments has been used and then empirical formulae have been used to extrapolate N to infinity. The singularities of the charge densities at the edges and corners of these objects can be characterised by power laws, the relevant exponents of which have also been determined.
Prior knowledge auxiliary for few-shot pest detection in the wild

One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive. However, existing deep-learning-based methods can detect only over a dozen common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest data sets for their base-class training and novel-class fine-tuning, and these significantly hinder the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests. In this paper, a few-shot pest detection network is introduced to detect rarely collected pest species in natural scenarios. Firstly, a prior-knowledge auxiliary architecture for few-shot pest detection in the wild is presented. Secondly, a hierarchical few-shot pest detection data set has been built in the wild in China over the past few years. Thirdly, a pest ontology relation module is proposed to combine insect taxonomy and inter-image similarity information. Several experiments are presented according to a standard few-shot detection protocol, and the presented model achieves comparable performance to several representative few-shot detection algorithms in terms of both mean average precision (mAP) and mean average recall (mAR). The results show the promising effectiveness of the proposed few-shot detection architecture.
Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder

Background This study aimed to investigate the relationships between regional neural activity and multiple related indicators in patients with major depressive disorder (MDD). Methods Forty-two patients and 42 healthy controls (HCs) were enrolled. Pearson/Spearman correlation analyses were applied to examine the associations between abnormal regional homogeneity (ReHo) and different indicators in the patients. Results Compared with HCs, patients with MDD had increased ReHo in the left inferior temporal gyrus (ITG) and decreased ReHo values in the left putamen, anterior cingulate cortex (ACC), and precentral gyrus. The ReHo of the left putamen was positively correlated with the PR interval, Repeatable Battery for the Assessment of Neuropsychological Status 4A, and Discriminant analysis (D), and negatively correlated with Ae (block) and Ae (total) in the patients. The ReHo value of the left ACC was positively correlated with the severity of depression, Stroop Color Word Test of C − 2B + 100 in reaction time, and negatively correlated with Ce (Missay) and Perseverative Responses in the patients. The ReHo of the left ITG was positively correlated with the Neuroticism scores and negatively correlated with the Lie scores in the patients. Conclusion These results suggested that the decreased ReHo of the salience network might be the underpinning of cognitive impairments in patients with MDD.
Computation and Measurement of the Flow in Axial Flow Fans With Skewed Blades

A numerical analysis of the flow in axial flow fans with skewed blades has been conducted to study the three-dimensional flow phenomena pertaining to this type of blade shape. The particular fans have a low pressure rise and are designed without stator. Initial studies focused on blades skewed in the circumferential direction, followed by investigations of blades swept in the direction of the blade chord. A Navier-Stokes code was used to investigate the flow. The simulation results of several fans were validated experimentally. The three-dimensional velocity field was measured in the fixed frame of reference with a triple sensor hot-film probe. Total pressure distribution measurements were performed with a fast response total pressure probe. The results were analyzed, leading to a design method for fans with swept blades. Forward swept fans designed accordingly exhibited good aerodynamic performance. The sound power level, measured on an acoustic fan test facility, improved.
A method for estimating age‐specific reference intervals (‘normal ranges’) based on fractional polynomials and exponential transformation

The age‐specific reference interval is an important screening tool in medicine. Put crudely, an individual whose value of a variable of interest lies outside certain extreme centiles may be suspected of abnormality. We propose a parametric method for constructing such intervals. It provides smooth centile curves and explicit formulae for the centile estimates and for standard deviation (SD) scores (age‐standardized values). Each parameter of an exponential–normal or modulus–exponential–normal density is modelled as a fractional polynomial function of age. Estimation is by maximum likelihood. These three‐ and four‐parameter models involve transformations of the data towards normality which remove non‐normal skewness and/or kurtosis. Fractional polynomials provide more flexible curve shapes than do conventional polynomials. The method easily accommodates binary covariates facilitating, for example, parsimonious modelling of age‐ and sex‐specific centile curves. A method of calculating precision profiles for centile estimates is proposed. Goodness of fit is assessed by using Q–Q‐plots and Shapiro–Wilk W‐tests of the SD scores, and likelihood ratio tests of the parameters of an enlarged model. Four substantial real data sets are used to illustrate the method. Comparisons are made with the semiparametric LMS method of Cole and Green.