Miracle-dz/UniFAR-FTUs huggingface dataset:
Antiphospholipid syndrome in patients over 65 years: A comparative study of clinical and biological features and thrombotic relapses

Objective The aim of the study was to describe clinical and biological characteristics and thrombotic relapses of patients diagnosed with antiphospholipid syndrome (APS) after the age of 65 years, in comparison with patients diagnosed with APS before 65. Methods This retrospective multicenter study was performed to 2005 from 2017 and included patients diagnosed with APS after the age of 65 years, in accordance with Sydney criteria. We compared these patients with APS patients diagnosed before the age of 65 years, and with control thrombotic patients older than 65 years. Results Fifty-eight APS patients over the age of 65 years were compared to 127 APS patients aged less than 65 and to 58 controls. In elderly APS versus younger APS, there was a male predominance (58.6% vs 36.2% p = .001); myocardial infarction and lower limb deep vein thrombosis (LLDVT) were more frequent in elderly, respectively, 12.1% versus 1.6% (p = .005), and 44.8% versus 29.9% (p = .048). Anticardiolipin antibody (aCL) IgM was more frequently found in old patients compared to younger patients (33.9% vs 18.1%, p = .02), contrary to lupus anticoagulant (LAC) (52.8% vs 66.9%, p = .02). Older patients were more often diagnosed with single positive APS (82.8% vs 59.8% p = .002). The thrombotic relapse free survival was lower in elderly APS patients (p = .044) compared to younger APS. Elderly APS patients had more recurrent arterial and venous thrombosis (p = .03) and had poorer overall survival (p = .004) than elderly controls. Conclusion In this study, APS was different in patients aged more than 65 years, with a male predominance and more myocardial infarctions and LLDVT at diagnosis. Single antiphopholipid positivity and aCL IgM were more frequent in older patients. Older patient with APS had more thrombotic recurrence during follow-up. Compared to elderly controls, elderly APS patients had more thrombosis recurrences and poorer survival.
Complex evolution of the electronic structure from polycrystalline to monocrystalline graphene: Generation of a new Dirac point

First principles calculations, employed to address the properties of polycrystalline graphene, indicate that the electronic structure of tilt grain boundaries in this system displays a rather complex evolution toward graphene bulk, as the tilt angle decreases, with the generation of a Dirac point, at the Fermi level, that lies not on the usual graphene Brillouin zone $\mathbf{K}$ point, and an anisotropic Dirac cone of low-energy excitations. Moreover, the usual $\mathbf{K}$-point Dirac cone falls below the Fermi level, and rises toward it as the tilt angle decreases. Further, our calculations indicate that the grain-boundary formation energy behaves nonmonotonically with the tilt angle, due to a change in the spatial distribution and relative contributions of the bond-stretching and bond-bending deformations associated with the formation of the defect.
The Effects of Spanwise Structures and Unsteady Forcing of Vortex Generators on a Shock-Induced Separated Flow Using Planar Laser Scattering

Abstract : Through the use of planar laser scattering, the effects of vortex generators (VGs) on the separation shock in an unswept compression ramp interaction have been investigated in a Mach 2 and Mach 5 wind funnel. Additionally, the undisturbed Mach 5 boundary layer was examined for the existence of long strips of uniform momentum fluid. These strips were previously identified in a Mach 2 boundary layer but had not been observed at Mach 5. The effects of the strips on the Mach 5 compression ramp interaction were also investigated. The counter-rotating vane-type VGs used in this study moved the shock downstream and reduced the separation region at both Mach 2 and Mach 5 however, at Mach 5 the control was sporadic and did not move the shock beyond the extent of the intermittent region. The reduced effectiveness at Mach 5 may be a result of the VGs rising to a significantly lower scaled height. This was necessary because the VGs resulted in large-scale separation when inserted farther into the boundary layer. Plan view PLS imaging in the streamwise-spanwise plans showed the VGs created a highly three-dimensional separation region in both the Mach 2 and Mach 5 interactions.
OCEAN g-MODES ON TRANSIENT NEUTRON STARS

The neutron star ocean is a plasma of ions and electrons that extends from the base of the neutron star’s envelope to a depth where the plasma crystallizes into a solid crust. During an accretion outburst in an X-ray transient, material accumulates in the envelope of the neutron star primary. This accumulation compresses the neutron star’s outer layers and induces nuclear reactions in the ocean and crust. Accretion-driven heating raises the ocean’s temperature and increases the frequencies of -modes in the ocean; when accretion halts, the ocean cools and ocean -mode frequencies decrease. If the observed low-frequency quasi-periodic oscillations on accreting neutron stars are -modes in the ocean, the observed quasi-periodic oscillation frequencies will increase during the outburst—reaching a maximum when the ocean temperature reaches steady state—and subsequently decrease during quiescence. For time-averaged accretion rates during outbursts between the predicted -mode fundamental n = 1 l = 2 frequency is between ≈3–7 Hz for slowly rotating neutron stars. Accreting neutron stars that require extra shallow heating, such as the Z-sources MAXI J0556-332, MXB 1659-29, and XTE J1701-462, have predicted -mode fundamental frequencies between ≈3–16 HZ. Therefore, observations of low-frequency quasi-periodic oscillations between in these sources, or in other transients that require shallow heating, will support a -mode origin for the observed quasi-periodic oscillations.
Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

Purpose Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype–phenotype correlations. Methods International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. Results Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. Conclusions This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.
Body mass index and comorbidities are associated with the duration of COVID-19 symptoms in non-hospitalized patients

Objective Evidence indicates that people with a high body mass index (BMI) tend to develop more severe forms of coronavirus disease 2019 (COVID-19). In this study, we aimed to determine the association between the duration of COVID-19 symptoms and variables such as BMI, age, presence of comorbidities, and smoking in non-hospitalized patients. Methods In this observational cross-sectional analytical study, we analyzed the data of patients with COVID-19 but without severe manifestations. We conducted descriptive statistics, non-parametric tests, and multivariate quasi-Poisson regression in the analysis. The quasi-Poisson regression model was configured with the duration of COVID-19 symptoms as the response variable, and BMI and the presence of comorbidities as the explanatory variables. Results Among 302 non-hospitalized patients, we found a significant difference in COVID-19 symptom duration between the overweight group and the group with normal weight. Multivariate quasi-Poisson regression analysis showed that BMI and the presence of comorbidities were associated with the duration of COVID-19 symptoms. On the contrary, sex, age, and smoking status were not related to COVID-19 symptom duration. Conclusions BMI and comorbidities were associated with the duration of COVID-19 symptoms in non-hospitalized patients.
Reply to Harber and to Confalonieri et al.

these cells most likely represents a regenerative attempt halted by impaired RAS-to-ATII transdifferentiation capacity, eventually leading to lung regeneration failure in patients with COPD (5). This inability to correctly regenerate is consistent with the interpretation of COPD as a structural lung disease, leading progressively to unfavorable lung mechanics and disabling symptoms largely present in older people. Noticeably, progenitor cell senescence may be a shared mechanism of different causes of COPD leading to a failed regenerative process and remodeling, and the search for senolytic drugs to treat patients with COPD has become an established field of investigation in recent years (6). Thus, we propose to also include the concept of “structural changes due to failed regeneration by the distal airways progenitor cells” into the new definition of COPD, furthering definition of the essential nature of COPD and the drive to search for new drugs.
Analysis of a brief biodata scale as a predictor of job performance and its incremental validity over the Big Five and Dark Tetrad personality traits

The collection of biographical information (biodata) through CVs and application forms has many advantages, namely easiness of collection, acceptable validity, less prone to faking, and the fulfilment of legal requirements. However, its systematic use among practitioners is scarce. Two of the mains reasons is the overlap with other constructs like personality and the lack of validated biodata scales in articles and public repositories. Aimed to fill this gap, García-Izquierdo and colleagues developed an 8-item scale able to generate positive applicant reactions, but they did not provide empirical evidence that their scale is able to predict job performance. The present paper was developed for this purpose, investigating the scale’s relationship with four different dimensions of job performance (i.e., task performance, contextual performance, counterproductive behaviors, and adaptive performance) and its incremental validity with respect to Big Five and Dark Tetrad personality traits. The study comprises 528 employees from different organizations (Mage = 39.51, SD = 14.25; 52.8% women, Mexperience = 17.06, SD = 13.27) which voluntarily agreed to participate filling a questionnaire with the variables of interest. Results provide evidence of the predictive validity of the biodata scale in a multi-occupational sample; identify that these biodata contribute to predicting two specific types of job performance: contextual performance and adaptive performance; shows that a brief job-related biodata scale achieves results comparable to those of most personality traits in predictive models of job performance dimensions; and provide evidence of the incremental predictive validity of biodata over the Big Five and the Dark Tetrad. As a whole, these results provide support for the use of the scale in researcher and applied settings, and contributes to the advance the knowledge of biodata for personnel selection.
Thermodynamics of the Fermi Gas in a Quantum Well

For the ideal Fermi gas that fills a quantum well confined by two parallel planes, there are calculated the thermodynamic characteristics in general form for arbitrary temperatures, namely: the thermodynamic potential, energy, entropy, equations of state, heat capacities and compressibilities. The distance between planes is considered as an additional thermodynamic variable. Owing to the anisotropy, the pressure of the Fermi gas along and transverse to the planes is different, so that the system is characterized by two equations of state and a set of different heat capacities. Limiting cases of low and high temperatures are considered. The temperature dependencies of the entropy and heat capacities at low temperatures remain linear, just as in the volume case, and their dependencies on the chemical potential and density undergo jumps at the beginning of the filling of new discrete levels. It is shown that the behavior of thermodynamic quantities with the distance between plates can be either oscillating or monotonic, depending on what quantity is assumed to be fixed: the volume or surface density. For high temperatures the corrections to thermodynamic quantities are obtained, which are proportional to the ratio of the thermal de Broglie wavelength to the distance between planes.
Studies on regge behaviour and spin-independent and spin-dependent structure functions

Deep Inelastic Scattering (DIS) experiments have provided important information on the structure of hadrons and ultimately the structure of matter and on the nature of interactions between leptons and hadrons, since the discovery of partons. Various high energy deep inelastic interactions lead to different evolution equations from which we obtain various structure functions giving information about the partons i.e. quarks and gluons involved in different scattering processes. Actually structure function is a mathematical picture of the hadron structure in the high energy region. Understanding the behaviour of the structure functions of the nucleon at low-x, where x is the Bjorken variable, is interesting both theoretically and phenomenologically. Structure functions are important inputs in many high energy processes and also important for examination of perturbative quantum chromodynamics (PQCD), the underlying dynamics of quarks and gluons. In PQCD, for high-Q2, where Q2 is the four momentum transfer in a DIS process, the Q2-evolutions of these densities (at fixed-x) are given by mainly Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) Evolution Equations. In this thesis, we solved both spin-independent and spin-dependent DGLAP evolution equations applying Regge behaviour of structure functions at low-x up-to next-next-to-leading order (NNLO) and have got the respective approximate analytical solutions of structure functions.
Fermiophobia in a Higgs Triplet Model

A fermiophobic Higgs boson can arise in models with an extended Higgs sector, such as models with scalars in an isospin triplet representation. In a specific model with a scalar triplet and spontaneous violation of lepton number induced by a scalar singlet field, we show that fermiophobia is not a fine-tuned situation, unlike in two higgs doublet models. We study distinctive signals of fermiophobia which can be probed at the LHC. For the case of a small Higgs mass, a characteristic signal would be a moderate B(H{yields}{gamma}{gamma}) accompanied by a large B(H{yields}JJ) (where J is a Majoron), the latter being an invisible decay. For the case of a large Higgs mass there is the possibility of dominant H{yields}ZZ, WW and suppressed H{yields}JJ decay modes. In this situation, B(H{yields}ZZ) is larger than B(H{yields}WW), which differs from the SM prediction.
The multi-messenger picture of compact binary mergers

In the last decade, enormous progress has been achieved in the understanding of the various facets of coalescing double neutron star and neutron black hole binary systems. One hopes that the mergers of such compact binaries can be routinely detected with the advanced versions of the ground-based gravitational wave detector facilities, maybe as early as in 2016. From the theoretical side, there has also been mounting evidence that compact binary mergers could be major sources of heavy elements and these ideas have gained recent observational support from the detection of an event that has been interpreted as a "macronova", an electromagnetic transient powered by freshly produced, radioactively decaying heavy elements. In addition, compact binaries are the most plausible triggers of short gamma-ray bursts (sGRBs) and the last decade has witnessed the first detection of a sGRB afterglow and subsequent observations have delivered a wealth of information on the environments in which such bursts occur. To date, compact binary mergers can naturally explain most — though not all — of the observed sGRB properties. This paper reviews major recent developments in various areas related to compact binary mergers.
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion

This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion. The proposed system is a score-level fusion of several light convolutional neural network (LCNN) based models. Various front-ends are used as input features, including low-frequency short-time Fourier transform and Constant Q transform. Due to the complex noise and rich synthesis algorithms, it is difficult to obtain the desired performance using the training set directly. Online data augmentation methods effectively improve the robustness of fake audio detection systems. In particular, the reasons for the poor improvement of score fusion are explored through visualization of the score distributions and comparison with score distribution on another dataset. The overfitting of the model to the training set leads to extreme values of the scores and low correlation of the score distributions, which makes score fusion difficult. Fusion with partially fake audio detection system improves system performance further. The submission on track 3.2 obtained the weighted equal error rate (WEER) of 11.04%, which is one of the best performing systems in the challenge.
A Comparative Study on Physical and Perceptual Features for Deepfake Audio Detection

Audio content synthesis has stepped into a new era and brought a great threat to daily life since the development of deep learning techniques. The ASVSpoof Challenge and the ADD Challenge have been launched to motivate the development of Deepfake audio detection algorithms. Currently, the detection models, which consist of front-end feature extractors and back-end classifiers, utilize the physical features mainly, rather than the perceptual features that relate to natural emotions or breathiness. Therefore, we provide a comprehensive study on 16 physical and perceptual features and evaluate their effectiveness in both Track 1 and Track 2 of the ADD Challenge. Based on results, PLP, as a perceptual feature, outperforms the rest of the features in Track 1, while CQCC has the best performance in Track 2. Our experiments demonstrate the significance of perceptual features in detecting Deepfake audios. We also seek to explore the underlying characteristics of features that can distinguish Deepfake audio from a real one. We perform statistical analysis on each feature to show its distribution differences on real and synthesized audios. This paper will provide a potential direction in selecting appropriate feature extraction methods for the future implementation of detection models.
Series of Abelian and non-Abelian states inC>1fractional Chern insulators

We report the observation of a new series of Abelian and non-Abelian topological states in fractional Chern insulators (FCI). The states appear at bosonic filling nu= k/(C+1) (k, C integers) in several lattice models, in fractionally filled bands of Chern numbers C>=1 subject to on-site Hubbard interactions. We show strong evidence that the k=1 series is Abelian while the k>1 series is non-Abelian. The energy spectrum at both groundstate filling and upon the addition of quasiholes shows a low-lying manifold of states whose total degeneracy and counting matches, at the appropriate size, that of the Fractional Quantum Hall (FQH) SU(C) (color) singlet k-clustered states (including Halperin, non-Abelian spin singlet states and their generalizations). The groundstate momenta are correctly predicted by the FQH to FCI lattice folding. However, the counting of FCI states also matches that of a spinless FQH series, preventing a clear identification just from the energy spectrum. The entanglement spectrum lends support to the identification of our states as SU(C) color-singlets but offers new anomalies in the counting for C>1, possibly related to dislocations that call for the development of new counting rules of these topological states.
Learning Sequential Transformation Information of Ingredients for Fine-Grained Cooking Activity Recognition

The goal of our research is to recognize the fine-grained cooking activities (e.g., dicing or mincing in cutting) in the egocentric videos from the sequential transformation of ingredients that are processed by the camera-wearer; these types of activities are classified according to the state of ingredients after processing, and we often utilize the same cooking utensils and similar motions in such activities. Due to the above conditions, the recognition of such activities is a challenging task in computer vision and multimedia analysis. To tackle this problem, we need to perceive the sequential state transformation of ingredients precisely. In this research, to realize this, we propose a new GAN-based network whose characteristic points are 1) we crop images around the ingredient as a preprocessing to remove the environmental information, 2) we generate intermediate images from the past and future images to obtain the sequential information in the generator network, 3) the adversarial network is employed as a discriminator to classify whether the input image is generated one or not, and 4) we employ the temporally coherent network to check the temporal smoothness of input images and to predict cooking activities by comparing the original sequential images and the generated ones. To investigate the effectiveness of our proposed method, for the first step, we especially focus on "\textitcutting activities ". From the experimental results with our originally prepared dataset, in this paper, we report the effectiveness of our proposed method.
IDENTIFICATION OF NEW GAMMA-RAY BLAZAR CANDIDATES WITH MULTIFREQUENCY ARCHIVAL OBSERVATIONS

Blazars are a highly variable, radio-loud subclass of active galactic nuclei. In order to better understand such objects we must be able to easily identify candidate blazars from the growing population of unidentified sources. Working toward this goal, we attempt to identify new gamma-ray blazar candidates from a sample of 102 previously unidentified sources. These sources are selected from The Astronomer's Telegram and the literature on the basis of non-periodic variability and multi-wavelength behavior. We then attempt to associate these objects to an IR counterpart in the Wide-field Infrared Survey Explorer all-sky survey. We are able to identify 16 candidate sources whose IR colors are consistent with those of the blazar population. Of those, 13 sources have IR colors indicative of being gamma-ray emitting blazar candidates. These sources all possess archival multi-wavelength observations that support their blazar-like nature.