Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights Gained Into the Treatment of COVID19 by Pulmonary Surfactant and Its Components

Pulmonary surfactant constitutes an important barrier that pathogens must cross to gain access to the rest of the organism via the respiratory surface. The presence of pulmonary surfactant prevents the dissemination of pathogens, modulates immune responses, and optimizes lung biophysical activity. Thus, the application of pulmonary surfactant for the treatment of respiratory diseases provides an effective strategy. Currently, several clinical trials are investigating the use of surfactant preparations to treat patients with coronavirus disease 2019 (COVID-19). Some factors have been considered in the application of pulmonary surfactant for the treatment COVID-19, such as mechanical ventilation strategy, timing of treatment, dose delivered, method of delivery, and preparation utilized. This review supplements this list with two additional factors: accurate measurement of surfactants in patients and proper selection of pulmonary surfactant components. This review provides a reference for ongoing exogenous surfactant trials involving patients with COVID-19 and provides insight for the development of surfactant preparations for the treatment of viral respiratory infections.
Sex-Specific Brain Transcriptional Signatures in Human MDD and Their Correlates in Mouse Models of Depression

Major depressive disorder (MDD) is amongst the most devastating psychiatric conditions affecting several millions of people worldwide every year. Despite the importance of this disease and its impact on modern societies, still very little is known about the etiological mechanisms. Treatment strategies have stagnated over the last decades and very little progress has been made to improve the efficiency of current therapeutic approaches. In order to better understand the disease, it is necessary for researchers to use appropriate animal models that reproduce specific aspects of the complex clinical manifestations at the behavioral and molecular levels. Here, we review the current literature describing the use of mouse models to reproduce specific aspects of MDD and anxiety in males and females. We first describe some of the most commonly used mouse models and their capacity to display unique but also shared features relevant to MDD. We then transition toward an integral description, combined with genome-wide transcriptional strategies. The use of these models reveals crucial insights into the molecular programs underlying the expression of stress susceptibility and resilience in a sex-specific fashion. These studies performed on human and mouse tissues establish correlates into the mechanisms mediating the impact of stress and the extent to which different mouse models of chronic stress recapitulate the molecular changes observed in depressed humans. The focus of this review is specifically to highlight the sex differences revealed from different stress paradigms and transcriptional analyses both in human and animal models.
Adult Vaccination as a Protective Factor for Dementia: A Meta-Analysis and Systematic Review of Population-Based Observational Studies

Background Common vaccinations may have impacts on dementia risk, but current evidence is inconsistent. We therefore investigated the association between vaccinations and dementia risk by systematic review and meta-analysis approach. Methods We conducted an extensive search of PubMed, Embase, Cochrane Library, and Web of Science to identify studies that compared the risk of dementia in vaccinated versus unvaccinated populations. The adjusted hazard ratio (HR) and corresponding 95% confidence intervals (CIs) were pooled as measures. Results Of the 9124 records initially retrieved, 17 studies with 1857134 participants were included in our analysis. The overall pooled results showed that vaccinations were associated with a 35% lower dementia risk (HR=0.65, 95% CI: 0.60-0.71, P overall effect < 0.001; I2 = 91.8%, P heterogeneity<0.001). All types of vaccination were associated with a trend toward reduced dementia risk, with rabies (HR=0.43), tetanus & diphtheria & pertussis (Tdap) (HR=0.69), herpes zoster (HR=0.69), influenza (HR=0.74), hepatitis A (HR=0.78), typhoid (HR=0.80), and hepatitis B (HR=0.82) vaccinations being significant. Individuals with more full vaccination types and more annual influenza vaccinations were less likely to develop dementia. Gender and age had no effect on this association. Conclusion Routine adult vaccinations are associated with a significant reduction in dementia risk and may be an effective strategy for dementia prevention. Further research is needed to elucidate the causal effects of this association and the underlying mechanisms.
Rhizoctonia solani Kühn Pathophysiology: Status and Prospects of Sheath Blight Disease Management in Rice

Sheath blight caused by necrotrophic fungus Rhizoctonia solani Kühn is one of the most serious diseases of rice. Use of high yielding semi dwarf cultivars with dense planting and high dose of nitrogenous fertilizers accentuates the incidence of sheath blight in rice. Its diverse host range and ability to remain dormant under unfavorable conditions make the pathogen more difficult to manage. As there are no sources of complete resistance, management through chemical control has been the most adopted method for sheath blight management. In this review, we provide an up-to-date comprehensive description of host-pathogen interactions, various control measures such as cultural, chemical, and biological as well as utilizing host plant resistance. The section on utilizing host plant resistance includes identification of resistant sources, mapping QTLs and their validation, identification of candidate gene(s) and their introgression through marker-assisted selection. Advances and prospects of sheath blight management through biotechnological approaches such as overexpression of genes and gene silencing for transgenic development against R. solani are also discussed.
Neuroimmune Crosstalk Between the Peripheral and the Central Immune System in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal disease characterized by the degeneration and death of motor neurons. Systemic neuroinflammation contributes to the pathogenesis of ALS. The proinflammatory milieu depends on the continuous crosstalk between the peripheral immune system (PIS) and central immune system (CIS). Central nervous system (CNS) resident immune cells interact with the peripheral immune cells via immune substances. Dysfunctional CNS barriers, including the blood–brain barrier, and blood–spinal cord barrier, accelerate the inflammatory process, leading to a systemic self-destructive cycle. This review focuses on the crosstalk between PIS and CIS in ALS. Firstly, we briefly introduce the cellular compartments of CIS and PIS, respectively, and update some new understanding of changes specifically occurring in ALS. Then, we will review previous studies on the alterations of the CNS barriers, and discuss their crucial role in the crosstalk in ALS. Finally, we will review the moveable compartments of the crosstalk, including cytokines, chemokines, and peripheral immune cells which were found to infiltrate the CNS, highlighting the interaction between PIS and CIS. This review aims to provide new insights into pathogenic mechanisms and innovative therapeutic approaches for ALS.
The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, P obesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, P extremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, P obesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, P extremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.
Alterations of Lysine Acetylation Profile in Murine Skeletal Muscles Upon Exercise

Objective Regular exercise is a powerful tool that enhances skeletal muscle mass and strength. Lysine acetylation is an important post-translational modification (PTM) involved in a broad array of cellular functions. Skeletal muscle protein contains a considerable number of lysine-acetylated (Kac) sites, so we aimed to investigate the effects of exercise-induced lysine acetylation on skeletal muscle proteins. Methods We randomly divided 20 male C57BL/6 mice into exercise and control groups. After 6 weeks of treadmill exercise, a lysine acetylation proteomics analysis of the gastrocnemius muscles of mice was performed. Results A total of 2,254 lysine acetylation sites in 693 protein groups were identified, among which 1,916 sites in 528 proteins were quantified. The enrichment analysis suggested that protein acetylation could influence both structural and functional muscle protein properties. Moreover, molecular docking revealed that mimicking protein deacetylation primarily influenced the interaction between substrates and enzymes. Conclusion Exercise-induced lysine acetylation appears to be a crucial contributor to the alteration of skeletal muscle protein binding free energy, suggesting that its modulation is a potential approach for improving exercise performance.
Differentially Private Empirical Risk Minimization under the Fairness Lens

Differential Privacy (DP) [13] is an important privacy-enhancing technology for private machine learning systems. It allows to measure and bound the risk associated with an individual participation in a computation. However, it was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals [3, 27, 22]. This paper builds on these important observations and sheds light on the causes of the disparate impacts arising in the problem of differentially private empirical risk minimization. It focuses on the accuracy disparity arising among groups of individuals in two well-studied DP learning methods: output perturbation [10] and differentially private stochastic gradient descent [2]. The paper analyzes which data and model properties are responsible for the disproportionate impacts, why these aspects are affecting different groups disproportionately, and proposes guidelines to mitigate these effects. The proposed approach is evaluated on several datasets and settings.
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks

In this paper, we introduce spatiotemporal joint ﬁlter decomposition to decouple spatial and temporal learning, while preserving spatiotemporal dependency in a video. A 3D convolutional ﬁlter is now jointly decomposed over a set of spatial and temporal ﬁlter atoms respectively. In this way, a 3D convolutional layer becomes three: a temporal atom layer, a spatial atom layer, and a joint coefﬁcient layer, all three remaining convolutional. One obvious arithmetic manipulation allowed in our joint decomposition is to swap spatial or temporal atoms with a set of atoms that have the same number but different sizes, while keeping the remaining unchanged. For example, as shown later, we can now achieve tempo-invariance by simply dilating temporal atoms only. To illustrate this useful atom-swapping property, we further demonstrate how such a decomposition permits the direct learning of 3D CNNs with full-size videos through iterations of two consecutive sub-stages of learning: In the temporal stage, full-temporal downsampled-spatial data are used to learn temporal atoms and joint coefﬁcients while ﬁxing spatial atoms. In the spatial stage, full-spatial downsampled-temporal data are used for spatial atoms and joint coefﬁcients while ﬁxing temporal atoms. We show empirically on multiple action recognition datasets that, the decoupled spatiotemporal learning signiﬁcantly reduces the model memory footprints, and allows deep 3D CNNs to model high-spatial long-temporal dependency with limited computational resources while delivering comparable performance.
Faster Non-asymptotic Convergence for Double Q-learning

Double Q-learning (Hasselt, 2010) has gained signiﬁcant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning. However, the theoretical understanding of double Q-learning is rather limited. The only existing ﬁnite-time analysis was recently established in (Xiong et al., 2020), where the polynomial learning rate adopted in the analysis typically yields a slower convergence rate. This paper tackles the more challenging case of a constant learning rate, and develops new analytical tools that improve the existing convergence rate by orders of magnitude. Speciﬁcally, we show that synchronous double Q-learning attains an (cid:15) -accurate global optimum with a time complexity of ˜Ω (cid:16) ln D
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices

Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily ﬁne-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the ﬁrst place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be made much smaller and still perform well. Here, we focus on factorized representations of matrices that underpin dense, embedding, and self-attention layers. We use low-rank factorized representation of a reshaped and rearranged original matrix to achieve space efﬁcient and expressive linear layers. We prove that stacking such low-rank layers increases their expressiveness, providing theoretical understanding for their effectiveness in deep networks. In Transformer models, our approach leads to more than ten-fold reduction in the number of total trainable parameters, including embedding, attention, and feed-forward layers, with little degradation in on-task performance. The approach operates out-of-the-box, replacing each parameter matrix with its compact equivalent while maintaining the architecture of the network.
Posterior Collapse and Latent Variable Non-identifiability

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.
Design and Manufacture of an Ultrasonic Transducer for Long-term High Temperature Operation

A novel design of ultrasonic transducers suitable for high temperatures is introduced. The transducers are targeted for trouble-free continuous operation at 700-800 °C for at least two years, with sufficiently wide bandwidth to yield good temporal resolution. The design and manufacturing procedures were formatted as a multistep sequence, adaptable to a variety of transducer specifications within a wide range of signal center frequencies (500 kHz to 10 MHz) and a wide range of signal bandwidths. Two transducer designs were pursued based on two different piezoelectric crystals as the active element: gallium phosphate and lithium niobate. A high Curie temperature of 1200 °C for lithium niobate and a high phase transition temperature of 970 °C for gallium phosphate, make the two piezoelements suitable for our application. A one-dimensional transducer model was used to model the transducer when used as a transmitter-receiver. The optimized acoustic impedance of the transducer backing element was determined to obtain desired signal center frequency of 3 MHz with 3 dB bandwidth of 90-95%. The concept of porous ceramics as a new generation of high
Priddis low-frequency seismometer test, part 2

CREWES acquired a low-frequency sensor comparison test dataset in 2009. The dataset was acquired using a weight drop trailer and an IVI Minivibe (2-10 Hz and 2-100 Hz linear sweeps with different sweep lengths and peak force levels) at two source points, offset by 50 meters from the ends of the north-south receiver lines. Sensors on the ground included accelerometers (DSU3 and VectorSeis), geophones (SM-24) and broadband seismometers (Trillium 240). Near-offset (50 m) traces for a single 2-100 Hz linear sweep are visually compared as 1) uncorrected, uncorrelated data, 2) converted to common units and corrected for geophone and seismometer instrument response in the velocity and acceleration of ground motion domains. Quantitative least-squaressubtraction-scalar results are also presented. System electrical noise appears to dominate signal below about 7 Hz for all systems, likely due to the long taper used for the low frequencies (< 10 Hz) and the low power used for the sweep in order to not damage the Minivibe. We appear to have a close match (other than a multiplicative term) down to at least 5 Hz for all recording systems and sensors.
Optimum Design of Control Devices for Safe Seepage under Hydraulic Structures

The research aimed at determining the optimal control device in order to decrease seepage under hydraulic structures. The finite-element method is used to analyze seepage through porous media below hydraulic structures with blanket, cutoff, or filter trench as seepage control devices. The effect of length and location of the control devices is investigated. The formulated optimization model is applied to a hypothetical case study. The optimization problem is solved by the Lagrange-multiplier method to find the minimum costs of control devices with safe exit gradient and uplift pressure. For a hydraulic structure with different control devices, the minimum total cost has been achieved when using a filter trench, while the maximum total cost was when a downstream cut-off is used. خلا ـ صلا ــــ ة مادختسا ىرج . ةيكيلورديهلا تآشنملا تحت حشرلا ليلقتل ىلثملا ةرطيسلا ةليسو ديدحت ىلإ ثحبلا فدهي رادج ،ةناطب دوجوب ةيكيلورديهلا تآشنملا تحت ةيماسملا طاسولأا للاخ حشرلا ليلحتل ةددحملا رصانعلا ةقيرط ّمتو ،حشرلا ىلع ةرطيسلل ةليسوك حشِرم قدنخ وأ ،عطاق لوطلا ديدحتل لئاسولا كلت عقومو لوط ريثؤت يرّحت . نيلثملأا عقوملاو لماعم ةقيرطب ةلؤسملا لّح ىرجو ،ةيضارتفا ةيقيبطت ةلاح ىلع غاصمُلا ةيلثملأا جذومن قيبطت ّمت دقل جرخملا رادحنا نم لك ىلع ةظفاحملا عم ةرطيسلا ةليسول ىندلأا ةفلكلا داجيلإ جناركلا Exit gradient و عفرلا طغض Uplift pressure . ةنملآا دودحلا نمض رادج لامعتسا دنع ىلعلأا تناك امنيب حشِرم قدنخ لامعتساب نوكت ىندلأا ةيّلكلا ةفلكلا نأ ثحبلا نيّب دقل . رخإملا دنع عطاق
Molecular detection of E.coli O157

This study was based on the importance of the pathogens that cause diarrhea in sheep and their impact on livestock, E.coli O157:H7 which are considered one of the most important pathogens, were studied and isolated with sensitivity test during the period (October 2021 to December 2021), samples were taken from infected sheep in fields around Baghdad (Abu-ghraib , AL-mahmoudia and AL-yosifiya ), All samples were cultured on MacConkey agar , Eosine Methylene Blue and Sorbitol MacConkey agar for E.coli isolation according the isolates were identified by biochemical tests.out of 101 diarrhea samples, 100 isolates gave positive E.coli results and was taken three isolates appeared more resistance to antibiotics , and one isolate very sensitive to antibiotics. These isolates were selected to be confirmed by using molecular detection PCR. Among the three isolates most resistant to antibiotics showed a molecular diagnosis two belonging to the E.coli O157-H7 , in addition to the isolate most sensitive to antibiotics , which gave a positive E.coli O157-H7 result, but isolates are more resistance antibiotics appeared similar to the result of isolate appear more sensitive antibiotics because bacteria can evolve rapidly to adapt to environmental change. , this evolution can turn harmless bacteria into harmful bacteria.