Miracle-dz/UniFAR-FTUs huggingface dataset:
EPI-589, a redox-active neuroprotectant, potently protects cultured cells from oxidative stress and alleviates symptomatic and pathological progression of motor neuron disease in the wobbler mouse

Oxidative stress is believed to play a significant role in the pathophysiology of amyotrophic lateral sclerosis (ALS), the most common form of motor neuron disease. The present study aims to firstly investigate the antioxidant activities of EPI-589, a small-molecule quinone derivative, under cell-free or cell culture conditions, and explore the in vivo efficacy of EPI-589 in the wobbler mouse model of human motor neuron disease. The reduced form of EPI-589 showed hydroxyl radical scavenging activities, whereas the oxidized form i.e. EPI-589 did not. In cellular models utilizing ALS patient-derived fibroblasts carrying mutations in the fused in sarcoma (FUS) gene or superoxide dismutase 1 (SOD1) gene, EPI-589 potently protected cells from oxidative stress induced by buthionine sulfoximine and ferric citrate. Protective effect of EPI-589 was also observed in culture of mouse immortalized striatal STHdHQ7/Q7 cells with cystine deprivation. In wobbler mice, oral administration of dietary EPI-589 provided long-lasting amelioration of both of deterioration of the rotarod walking performance and progression of forelimb deformity in wobbler mice throughout the treatment. In separate studies, we found that EPI-589 significantly suppressed changes of pathophysiological markers such as plasma phosphorylated neurofilament heavy chain, urinary 8-hydroxy-2’-deoxyguanosine, and cervical N-acetylaspartate in untreated wobbler mice. Thus, the present study firstly demonstrates that EPI-589 is a highly potent, redox-active neuroprotectant and robustly delays the symptomatic and pathophysiological progression of motor neuron disease in the wobbler mouse, and these findings strongly encourage further exploration of the therapeutic potential of EPI-589 for the treatment of ALS.
Hypoxia-inducible factor 1 signaling drives placental aging and can elicit contractile changes in uterine myocytes

: Most cases of pre-term labor have unknown cause, and the burden of preterm birth is immense. Placental aging has been proposed to promote labor onset, but specific mechanisms remain elusive. We report findings stemming from unbiased transcriptomic analysis of mouse placenta, which revealed that hypoxia-inducible factor 1 (HIF-1) stabilization is a hallmark of advanced gestational timepoints, accompanied by mitochondrial dysfunction and cellular senescence; we validated these findings in human placenta. In parallel in primary mouse trophoblasts and human choriocarcinoma cells, we modeled HIF-1 induction using prolyl hydroxylase inhibitors, and demonstrated resultant mitochondrial dysfunction and cellular senescence. Transcriptomic analysis revealed that HIF-1 stabilization recapitulated gene signatures observed in aged placenta. Further, conditioned media from trophoblasts following HIF-1 induction was sufficient to induce a contractile phenotype in immortalized uterine myocytes, suggesting a mechanism by which the aging placenta may drive the transition from uterine quiescence to contractility at the onset of labor. These results provide clear evidence for placental aging in normal pregnancy. Mechanistic dissection of this phenomenon demonstrates how hypoxia in late gestation may be a causal determinant of the mitochondrial dysfunction and senescence observed within the trophoblast as well as a trigger for uterine contraction. — confirming patterns we discovered through reanalysis of published transcriptomic data. to provide clear evidence for placental aging in normal pregnancy. Mechanistic on (--seqBias and --gcBias options, respectively). Transcript abundances were collated and summarized to gene abundances using the tximport package for R (64). Normalization and differential expression analysis were performed using edgeR (65, 66). For differential gene expression analysis, genes were considered significant if they passed a fold change (FC) cutoff of log2FC > 1 and a false discovery rate (FDR) cutoff of FDR < 0.05. Functional enrichment analyses were performed using g:Profiler (67). WGCNA (68) was performed after removing genes with low expression as previously described (69).
Analysis and Enhancement of Self-sovereign Identity System Properties Compiling Standards and Regulations

: A self-sovereign identity (SSI) system represents a paradigm shift in identity management by leveraging the decentralization inherent in blockchain technology. The fundamental characteristics of the SSI system are constrained by a set of guiding principles and system properties. While knowledgeable scholars and practitioners have proposed such principles and properties, they have not yet been standardized. The SSI community has agreed upon and adheres to the existing proposals when implementing the SSI system. Additionally, the SSI system is used to manage personally identifiable information (PII), and compliance with certain standards and regulations is required. We discovered that while the current proposals do correspond to some extent to those documents, they cannot be characterized as explicitly compliant. We evaluate several well-known standards and regulations as credible sources in this work and compare them to the definitions in current proposals in order to identify inconsistencies. Then, we propose a list of SSI system properties that could be used to improve the security and privacy of the SSI system by addressing the inconsistencies discovered. We assess its applicability in real-world scenarios and its appropriateness from an expert’s perspective. The proposed properties yield meaningful results that may resolve the inconsistencies.
Route-diversity calculation based on space and time correlation of rain attenuation in microwave feeder network

Owing to the increasing number of users who are taking advantage of different services in mobile environment the capacity requirements in the mobile networks are continuously increasing. Owing to the several advantageous attributes of the microwave connections the mobile operators often use point-to-point microwave connections in the transmission access network. However, microwave links above 10 GHz are highly influenced by precipitation especially by rain. To investigate wave propagation phenomena in three European countries with rather different meteorological conditions, the international Millimetre wave Propagation (MilliProp) project was founded in 1997. After a short description of the measurement system, the application possibilities in the network operation are presented where we also outline the applicability of network-operator-assisted route diversity system in cellular networks. From the short-term statistics of the IF power signal, equipment failures caused by ageing are also predictable. For the practical utilization of the measurement system a concept of accessibility of the measurement result is also introduced.
A Survey on Implicit Bias of Gradient Descent

Gradient descent, as an optimization method in training models, is of great appliances due to its low cost and explanatory ability. However, the fact that over-parameterization models prevent itself from overfitting without explicit regularizations remains unsolved. There are a tremendous amount of papers focusing on explaining such implicit bias for different models and spaces. This paper will present a mixed review on most of the current research of such bias. Multiple types of implicit bias are suggested throughout the paper, alongside their behavior analysis. Contents are ordered logistically from how the bias was first discovered, followed by the connection between explicit regularization and implicit bias. Next, the converging behavior of the predictor space in different networks is divided into various cases and detailly explained. There is further discussion on the existence of zero training loss on an over-parameterized model. The paper then develops more generally towards different types of datasets and introduces why some common assumptions about the convergence could be removed. Finally, some discussion about how work can be improved in the future is provided at the end of the paper.
Social Media News Credibility among Students in the Czech Republic

With the development of the social network, a variety of misinformation, disinformation, and fake news has come to be often shared and spread. A clear link exists between this spread and the willingness of users to share news they receive. The reasons may differ-from actual belief in it to finding it humorous. Our article answers the following research questions: How much do students in the Czech Republic share news on social media? How much do they trust the news they read there? And does the attitude of students and older non-students differ? In the autumn of 2020, we conducted a survey at the Prague University of Economics and Business on the willingness to share news on social networks. The survey had 452 respondents, not all of which were students. Although the respondents, on the one hand, claimed they do not share messages they are not convinced are truthful, a sufficient number of likes or level of interestingness of the message often persuade them to do the opposite. We found that women are stricter about not sharing fake news than men. A further comparison between students and non-students demonstrated that students take social media as a source of news and recommend it more often, whereas non-students more often share specific messages.
Deep multi-modal data analysis and fusion for robust scene understanding in CAVs

Deep learning (DL) tends to be the integral part of Autonomous Vehicles (AVs). Therefore the development of scene analysis modules that are robust to various vulnerabilities such as adversarial inputs or cyber-attacks is becoming an imperative need for the future AV perception systems. In this paper, we deal with this issue by exploring the recent progress in Artificial Intelligence (AI) and Machine Learning (ML) to provide holistic situational awareness and eliminate the effect of the previous attacks on the scene analysis modules. We propose novel multi-modal approaches against which achieve robustness to adversarial attacks, by appropriately modifying the analysis Neural networks and by utilizing late fusion methods. More specifically, we propose a holistic approach by adding new layers to a 2D segmentation DL model enhancing its robustness to adversarial noise. Then, a novel late fusion technique has been applied, by extracting direct features from the 3D space and project them into the 2D segmented space for identifying inconsistencies. Extensive evaluation studies using the KITTI odometry dataset provide promising performance results under various types of noise.
Robust Multiseasonal Ice Classification From High-Resolution X-Band SAR

Automated solutions for sea ice-type classification from synthetic aperture radar (SAR) imagery offer an opportunity to monitor sea ice, unimpeded by cloud cover or the arctic night. However, there is a common struggle to obtain accurate classifications year round, particularly in the melt and freeze-up seasons. During these seasons, the radar backscatter signal is affected by wet snow cover, obscuring information about underlying ice types. By using additional spatiotemporal contextual data and a combination of convolutional neural networks and a dense conditional random field, we can mitigate these problems and obtain a single classifier that is able to classify accurately at 3.5-m spatial resolution for five different classes of sea ice surface from October to May. During the near year-long drift of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition, we collected satellite scenes of the same patch of Arctic pack ice with X-band SAR with a revisit time of less than a day on average. Combined with in situ observations of the local ice properties, this offers up the unprecedented opportunity to perform a detailed and quantitative assessment of the robustness of our classifier for level, deformed, and heavily deformed ice. For these three classes, we can perform accurate classification with a probability >95% and calculate a lower bound for the robustness between 85% and 88%.
Forecasting Gold Prices Using Temporal Convolutional Networks

Accurate prediction of the ﬁnancial markets can provide many beneﬁts, of which underlying economic stability is probably the most important. This area has understandably attracted a signiﬁcant amount of interest from the research community, and has inspired a diverse range of approaches with varying degrees of success. Gold is a particular commodity which has attracted considerable attention since it was ﬁrst smelted for ornaments and jewellery by the Egyptians in 3600BC. In uncertain economic times it is regularly used as a safe-haven commodity, and is why there is considerable attention given to enhancing the accuracy of gold prices prediction methods. Previous attempts at gold price prediction have used a variety of econo-metric and machine learning techniques. In particular Long Short-Term Networks (LSTMs) and more recently an ensemble of Convolutional Neural Networks (CNNs) and LSTMs have been found to have had considerable level of success in time series prediction. In this research we have conducted a comparative analysis between ARIMA, CNN, LSTM and CNN-LSTM and a recently introduced structure known as Temporal Convolutional Networks (TCNs) on gold price data spanning 20 years. The results show how TCNs produced a RMSE of 15.26 and outper-formed both CNN-LSTM and LSTM with RMSE scores of 23.53 and 27.39 respectively.
A Lightweight CNN Net for AMD Detection Using OCT Volumes

To popularize age-related macular degeneration diagnosis in rural and remote areas, we proposed a lightweight convolution neural network (CNN) architecture that aims to identify whether the patient has age-related macular degeneration through the optical coherence tomography images. The proposed CNN model achieves 2,322 parameters and 0.0573 GFLOPs, which is only 4.98% of the Mobile net. Besides, the CNN model achieves 98.18% of accuracy. With the fixed-point simulation using 14 bits on weights and 5 bits on input data, the CNN model achieves accuracy of 97.73 %.
Ambisonics domain Singing Voice Separation combining Deep Neural Network and Direction Aware Multichannel NMF

This paper addresses the problem of multichannel source separation in ambisonics signals combining two powerful approaches, multichannel NMF with recent single-channel deep learning (DL) based spectrum inference. Individual source spectra are estimated from the zero-order (omnidirectional) spherical harmonic (SH) signal with a Masker-Denoiser twin network, able to model long-term temporal patterns of a musical piece. The initialized source spectrograms are used within a SHdomain spatial covariance mixing model based on multichannel non-negative matrix factorization (MNMF) that predicts the spatial characteristics of each source in order to refine the network prediction. The proposed framework is evaluated on the task of singing voice separation with a large dataset of simulated ambisonics signals using several setups. Experimental results show that our joint DL+SH-MNMF method outperforms both the individual monophonic DL-based separation, baseline multichannel MNMF and SH domain beamforming classical methods.
A Computational Method for Modeling Spatiotemporal Variability of Hydrodynamic Properties in Sandy Soil Under Drainage and Recharge

This article proposes an analytical strategy that combines X-ray Computed Tomography (CT) and Continuous Wavelet Transform (CWT) analysis as an alternative solution to long-term experiments that seek to investigate spatiotemporal variations in soil hydraulic properties induced by drainage and recharge cycles. We conducted CT scanning on 100-cm-high column filled with two types of sandy soil in a laboratory environment to simulate, over the period of a month, the equivalent of nearly 40 years of drainage/recharge cycles akin to agricultural fields adopting subirrigation as water management practices. We also monitored soil matric potential, water inflow and outflow, as well as movement of tracers. This later consists in zirconium oxide (ZrO2) that we added to the top 20 cm of each soil column. The results revealed that drainage and recharge cycles greatly affect the evolution of soil hydraulic properties at different locations along the soil profile by reducing drainage and capillary capacities. The approach also allowed us to identify each periodic component of drainage and recharge cycles, and thereby calculate the periodic drift over time. The proposed method can be applied to predict soil evolution according to soil texture, drainage system design and water management, thereby offering a potential basis for proposing mitigation measures related to soil hydrodynamics. It may find its application in agricultural farms adopting subirrigation and surface (e.g., drip) irrigation approaches and, in mining and civil engineering.
The Role and Clinical Correlates of Complex Post-traumatic Stress Disorder in People With Psychosis

Traumatic experiences and post-traumatic stress are highly prevalent in people with psychosis, increasing symptom burden, decreasing quality of life and moderating treatment response. A range of post-traumatic sequelae have been found to mediate the relationship between trauma and psychotic experiences, including the “traditional” symptoms of post-traumatic stress disorder (PTSD). The International Classification of Diseases-11th Edition recognizes a more complex post-traumatic presentation, complex PTSD (cPTSD), which captures both the characteristic symptoms of PTSD alongside more pervasive post-traumatic sequelae known as ‘disturbances in self-organization’ (DSOs). The prevalence and impact of cPTSD and DSOs in psychosis remains to be explored. In the first study of this kind, 144 participants with psychosis recruited from North West United Kingdom mental health services completed measures assessing trauma, PTSD and cPTSD symptoms and symptoms of psychosis. Forty-percent of the sample met criteria for cPTSD, compared to 10% who met diagnostic criteria for PTSD. PTSD and DSOs mediated the relationship between trauma and positive symptoms, controlling for dataset membership. Both PTSD and DSOs mediated the relationship between trauma and affective symptoms but did not explain a significant proportion of variance in negative symptoms. Cognitive and excitative symptoms of psychosis did not correlate with trauma, PTSD or DSO scores. These findings indicate the possible value of adjunct therapies to manage cPTSD symptoms in people with psychosis, pending replication in larger epidemiological samples and longitudinal studies.
Development and Preliminary Validation of the Physical Education-Study Process Questionnaire : Insights for Physical Education University Students

Background The Revised Two Factor Study Process Questionnaire (R-SPQ-2F) is used to examine students' study approaches in higher education. The questionnaire is designed to measure two factors: deep and surface approaches. In order to measure these approaches for students in physical education and sport, a new measurement instrument should take into consideration the practical context of this field of education that makes it specific to other fields. Objective The present study aims (a) to develop and empirical test of a new instrument for measuring the study process in physical education and sports students, and (b) to test psychometric properties of the tool. Methods Two exploratory and confirmatory samples of physical education students enrolled in a bachelor's degree program in physical education at the High Institute of Physical Education and Sports of Kef-Tunisia, aged 19–26 years, were recruited online among female students (n = 414) and male students (n = 393). The participants filled in Google Form survey including Physical Education-Study Process Questionnaire (PE-SPQ) and the Arabic version of the Revised Study Process Questionnaire-2 Factors (R-SPQ-2F). Results Exploratory factor analysis showed a suitable four factors solution, which is approved by confirmatory factor analysis indices [χ2 = 466.47, TLI = 0.94, CFI = 0.95; RMSEA = 0.56 IC 90% (0.050–0.062)]. Internal consistency of the PE-SPQ simultaneously checked by McDonald's ω, Cronbach's α and Gutmann's λ6 showed good reliability of the PE-SPQ. Convergent validity examined by Average variance extracted (AVE) was good. The comparison between the AVE root mean square and Pearson correlation coefficients of each factor with his indicators reveals the discriminant validity of the PE-SPQ. Furthermore, Pearson's correlation between the PE-SPQ factors and the R-SPQ-2F establishes the concurrent validity of the new scale. Conclusion The PE-SPQ scale is valid and reliable and can be used to assess study process factors in physical education students.
Transcriptome Sequencing Reveals the Antiviral Innate Immunity by IFN-γ in Chinese Sturgeon Macrophages

To further study the biological function of interferon-gamma (IFN-γ) in the Chinese sturgeon (Acipenser sinensis), we conducted a transcriptome analysis of primary macrophages induced by IFN-γ using Illumina sequencing technology. We obtained 88,879 unigenes, with a total length of 93,919,393 bp, and an average length of 1,057bp. We identified 8,490 differentially expressed genes (DEGs) between the untreated and IFN-γ-treated macrophages, with 4,599 upregulated and 3,891 downregulated. Gene ontology (GO) analysis showed that 4,044 DEGs were enriched in the biological, cellular components, and molecular function categories. Kyoto Encyclopedia of Genes and Genomes (KEGG) identified 278 immunity-related pathways enriched for the DEGs. According to the GO enrichment results, eight key immunity-related genes were screened for verification using qPCR. Results indicate that IFN-γ can activate macrophage Interferon Regulatory Factors (IRFs) and type I interferon (IFN-I), activate RIG-I-like and Toll-like receptor-related pathways, and improve the antiviral ability of macrophages in Chinese sturgeon.
Insulin Resistance and Vitamin D Deficiency: A Link Beyond the Appearances

Vitamin D is a steroid hormone that plays a key role in the regulation of body homeostasis, including cardiovascular function. Although the chronic deficiency of vitamin D is associated with cardiovascular risk factors, as well as with an adverse prognosis, randomized controlled trials have failed in demonstrating that dietary vitamin D supplementation could ameliorate the prognosis of patients with cardiovascular diseases, and suggested that vitamin D deficiency is the expression of the effects of other determinants of cardiovascular risk. Thus, the supplementation of vitamin D is not sufficient to improve the cardiovascular risk profile and prognosis. Insulin resistance is a complex phenomenon that plays a key role in the pathogenesis of conventional cardiovascular risk factors. Interestingly, defects of vitamin D and insulin resistance have a superimposable epidemiological distribution. According to the common view, Insulin resistance is considered the direct or indirect consequence of vitamin D deficiency. However, it is also reasonable to speculate that the deficit or the impaired action of vitamin D, in some circumstances, could be the result of the same pathogenic mechanisms responsible of insulin resistance development. In this case, vitamin D deficiency could be considered an epiphenomenon of insulin resistance. Insulin resistance is a reversible condition, being possibly ameliorated by physical activity and hypocaloric diets. Notably, both physical exercise and energy-restricted dietary regimens are associated with an increase of vitamin D levels. These findings indicate that improving insulin resistance condition is a necessary step to ameliorate vitamin D supplementation-based strategies in cardiovascular prevention.