Miracle-dz/UniFAR-FTUs huggingface dataset:
Respiratory Syncytial Virus Matrix Protein Is Sufficient and Necessary to Remodel Host Mitochondria in Infection

Although respiratory syncytial virus (RSV) is the most common cause of respiratory infection in infants, immunosuppressed adults and the elderly worldwide, there is no licensed RSV vaccine or widely applicable antiviral therapeutics We previously reported a staged redistribution of mitochondria with compromised respiratory activities and increased reactive oxygen species (ROS) generation during RSV infection. Here, we show for the first time that the RSV matrix protein (M) is sufficient and necessary to induce these effects. Ectopically expressed M, but not other RSV proteins, was able to induce mitochondrial perinuclear clustering, inhibition of mitochondrial respiration, loss of mitochondrial membrane potential (Δψm), and enhanced generation of mitochondrial ROS (mtROS) in infection. Truncation and mutagenic analysis revealed that the central nucleic acid-binding domain of M is essential for the effects on host mitochondria, with arginine/lysine residues 170/172 being critically important. Recombinant RSV carrying the arginine/lysine mutations in M was unable to elicit effects on host mitochondria. Further, wild-type but not mutant RSV was found to inhibit the mRNA expression of genes encoding mitochondrial proteins, including Complex I subunits. Importantly, the RSV mutant was impaired in virus production, underlining the importance of M-dependent effects on mitochondria to RSV infection. Together, our results highlight M’s unique ability to remodel host cell mitochondria and its critical role in RSV infection, representing a novel, potential target for future anti-RSV strategies.
Comparison of spirometric parameters in athletes engaged in aerobic and anaerobic sports: a cross-sectional study

Background: Pulmonary function tests are better among athletes than non-athletes. The spirometry parameters may vary among athletes involved in aerobic and anaerobic sports. This study aims to study the difference in spirometry parameters among aerobic and anaerobic sports athletes. Materials and methods: This cross-sectional study was conducted among 131 professional athletes. A convenience sampling technique was used. The data were collected from April to November 2022 using a proforma form, entered into Microsoft Excel, and analyzed using Statistical Package for Social Sciences version 16. Results: Out of 131 total participants, 65 were involved in aerobic sports, while 66 were in anaerobic sports. The mean age of the participants was 27.34±5 years, the majority being male (n=104). Aerobic athletes had higher differences in forced vital capacity (1.19%, P=0.726) and forced expiratory volume in the first second (3.08%, P=0.315), the difference was not statistically significant. Statistically significantly higher difference in forced expiratory flow at 25–75% (13.42%, P=0.001), forced expiratory flow at 0.2–1.2 l/s (7.47%, P=0.035), and maximum voluntary ventilation (8.58%, P=0.023) values were observed among aerobic athletes. Conclusion: Aerobic and anaerobic athletes had no statistically significant difference in forced vital capacity and forced expiratory volume in the first second; however, other spirometry parameters were comparatively better in aerobic athletes.
Evaluation of the prognostic value of negative to positive lymph node ratio in gastric cancer: results from multi-institutional cohorts from western and eastern datasets – Cohort study

Introduction: Lymph node (LN) stage is important for prognosis evaluation of gastric cancer (GC) patients. This study aimed to evaluate the prognostic value of the ratio of negative to positive LNs (Rnp) in GC. Methods: The authors evaluated the clinical significance of the Rnp stage in 7660 GC patients from three high-volume institutions in China. Meanwhile, the authors verified the value of the Rnp stage in 11 234 GC patients from the Surveillance, Epidemiology, and End Results (SEER) database. Results: The patients were stratified into different subgroups based on the N stage of the eighth edition of the TNM staging system, the ratio of positive to detected LNs (Rpd) and Rnp. The survival analysis showed clear differences between the three LN stages in both the China and Surveillance, Epidemiology, and End Results cohorts. In univariate and multivariate analyses, the Rnp stage provided smaller Akaike information criterion or Bayesian information criterion values and a larger likelihood ratio χ2 than the N or Rpd stages in both two cohorts. For patients with inadequate examined LNs (<16), the Rnp stage showed better prognostic evaluation performance than the other two stages. In addition, the 5-year disease-specific survival of GC patients showed a slight variation with increasing LNs in the same subgroup classified by the Rnp or Rpd stages compared to the N stage. Conclusions: Along with the higher prognostic value, the Rnp stage has excellent universality with GC patients compared to the N or Rpd stages. Studies with larger sample sizes are needed to predict the prognosis and provide more precise treatment for GC patients.
Learning Properties of Holomorphic Neural Networks of Dual Variables

Artificial neural networks have become an inseparable element of human life. Researches do not stop at the current progress and try to improve neural networks and expand fields of applications. The most widespread way to make models better consists in generalization of existing methods and approaches. In this paper, we make a step in an unusual direction: we propose to use neural networks based on dual numbers. We develop a special subclass of dual-valued operators, which satisfy the equivalent of the Cauchy-Riemann equations for the dual domain. We also propose a new type of preprocessing and batch normalization, relying on peculiarities of dual numbers. We test deep holomorphic dual-valued models on music transcription and gravitational wave detection tasks and show that our holomorphic dual-valued networks achieve better inference time compared to the dual-valued models and are better than their real-valued counter-parts in sense of metrics.
1α,25-Dihydroxyvitamin D3 Improves Follicular Development and Steroid Hormone Biosynthesis by Regulating Vitamin D Receptor in the Layers Model

1α,25-Dihydroxyvitamin D3 (VitD3) is the active form of vitamin D, and it regulates gene expression and protein synthesis in mammalian follicle development. However, the function of VitD3 in the follicular development of layers remains unclear. This study investigated, through in vivo and in vitro experiments, the effects of VitD3 on follicle development and steroid hormone biosynthesis in young layers. In vivo, ninety 18-week-old Hy-Line Brown laying hens were randomly divided into three groups for different treatments of VitD3 (0, 10, and 100 μg/kg). VitD3 supplementation promoted follicle development, increasing the number of small yellow follicles (SYFs) and large yellow follicles (LYFs) and the thickness of the granulosa layer (GL) of SYFs. Transcriptome analysis revealed that VitD3 supplementation altered gene expression in the ovarian steroidogenesis, cholesterol metabolism, and glycerolipid metabolism signaling pathways. Steroid hormone-targeted metabolomics profiling identified 20 steroid hormones altered by VitD3 treatment, with 5 being significantly different among the groups. In vitro, it was found that VitD3 increased cell proliferation, promoted cell-cycle progression, regulated the expression of cell-cycle-related genes, and inhibited the apoptosis of granulosa cells from pre-hierarchical follicles (phGCs) and theca cells from prehierarchical follicles (phTCs). In addition, the steroid hormone biosynthesis-related genes, estradiol (E2) and progesterone (P4) concentrations, and vitamin D receptor (VDR) expression level was significantly altered by VitD3. Our findings identified that VitD3 altered the gene expression related to steroid metabolism and the production of testosterone, estradiol, and progesterone in the pre-hierarchical follicles (PHFs), resulting in positive effects on poultry follicular development.
A CNN-Transformer Network Combining CBAM for Change Detection in High-Resolution Remote Sensing Images

Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating pseudo changes. Transformers have an efficient global spatio-temporal modelling capability, which is beneficial for the feature representation of changes of interest. However, the lack of detailed information may cause the transformer to locate the boundaries of changed regions inaccurately. Therefore, in this article, a hybrid CNN-transformer architecture named CTCANet, combining the strengths of convolutional networks, transformer, and attention mechanisms, is proposed for high-resolution bi-temporal remote sensing image change detection. To obtain high-level feature representations that reveal changes of interest, CTCANet utilizes tokenizer to embed the features of each image extracted by convolutional network into a sequence of tokens, and the transformer module to model global spatio-temporal context in token space. The optimal bi-temporal information fusion approach is explored here. Subsequently, the reconstructed features carrying deep abstract information are fed to the cascaded decoder to aggregate with features containing shallow fine-grained information, through skip connections. Such an aggregation empowers our model to maintain the completeness of changes and accurately locate small targets. Moreover, the integration of the convolutional block attention module enables the smoothing of semantic gaps between heterogeneous features and the accentuation of relevant changes in both the channel and spatial domains, resulting in more impressive outcomes. The performance of the proposed CTCANet surpasses that of recent certain state-of-the-art methods, as evidenced by experimental results on two publicly accessible datasets, LEVIR-CD and SYSU-CD.
Depth Analysis of Anesthesia Using EEG Signals via Time Series Feature Extraction and Machine Learning

The term “anesthetic depth” refers to the extent to which a general anesthetic agent sedates the central nervous system with specific strength concentration at which it is delivered. The depth level of anesthesia plays a crucial role in determining surgical complications, and it is imperative to keep the depth levels of anesthesia under control to perform a successful surgery. This study used electroencephalography (EEG) signals to predict the depth levels of anesthesia. Traditional preprocessing methods such as signal decomposition and model building using deep learning were used to classify anesthetic depth levels. This paper proposed a novel approach to classify the anesthesia levels based on the concept of time series feature extraction, by finding out the relation between EEG signals and the bi-spectral Index over a period of time. Time series feature extraction on basis of scalable hypothesis tests were performed to extract features by analyzing the relation between the EEG signals and Bi-Spectral Index, and machine learning models such as support vector classifier, XG boost classifier, gradient boost classifier, decision trees and random forest classifier are used to train the features and predict the depth level of anesthesia. The best-trained model was random forest, which gives an accuracy of 83%. This provides a platform to further research and dig into time series-based feature extraction in this area.
Improving the Stochastic Gradient Descent’s Test Accuracy by Manipulating the ℓ∞ Norm of its Gradient Approximation

The stochastic gradient descent (SGD) is a simple yet very influential algorithm used to find the minimum of a loss (cost) function which is dependent on datasets with large cardinality, such in cases typically associated with deep learning (DL). There exists several variants/improvements over the "vanilla" SGD, which from a high-level perspective, may be understood as using an adaptive element-wise step-size (SS). Moreover, from an algorithmic point of view, there is a clear "incremental improvement path" which relates all of them, i.e. from simple alternative such SG Clipping (SGC) to the well-known variance correction (Adagrad), follow by an (EMA) exponential moving average (RMSprop) to alternative furtherance such Newton (AdaDelta) or bias correction along with different EMA options for the gradient itself (Adam, AdaMAx, AdaBelief, etc.). In this paper, inspired by previous non-stochastic results on how to avoid divergence for ill chosen SS (for the accelerated proximal gradient algorithm), instead of directly using the standard SGD gradient’s EMA ${{\mathbf{\bar g}}_k}$, we propose to modify its entries so as to force $\left\{ {{{\left\| {{{{\mathbf{\bar g}}}_k}} \right\|}_\infty }} \right\}{\text{'s}}$ moving average to be non-increasing. Our reproducible computational results compare our proposed algorithm, called SGD-ℓ∞, with several optimizers (such Adam, AdaMax, SGC, etc.); while, as expected, SGD-ℓ∞ allows us to use larger SS without divergence problems, (i) it also matches a well-tuned Adam’s performance (superior to "default parameters" Adam), and (ii) heuristically, its convergence properties (rate, oscillations, etc.) are superior when compared to other well-known algorithms.
A new efficient algorithm for computing a few DFT points

The authors describe an efficient method for computing the discrete Fourier transform (DFT) when only a few output points are needed. The method is shown to be more efficient than either Goertzel's method or pruning, and it allows any band in the output to be computed. It is based on a novel factorization of the DFT, where one part is computed using standard power-of-two FFTs (fast Fourier transforms) and the other uses a technique similar to the Goertzel algorithm. <<ETX>>
Combination of Niclosamide and Pirfenidone Alleviates Pulmonary Fibrosis by Inhibiting Oxidative Stress and MAPK/Nf-κB and STATs Regulated Genes

The pathogenesis of pulmonary fibrosis (PF) is extremely complex and involves numerous intersecting pathways. The successful management of PF may require combining multiple agents. There is a growing body of evidence that suggests the potential benefits of niclosamide (NCL), an FDA-approved anthelminthic drug, in targeting different fibrogenesis molecules. This study aimed at investigating the anti-fibrotic potential of NCL alone and in combination with pirfenidone (PRF), an approved drug for PF, in a bleomycin (BLM) induced PF experimental model. PF was induced in rats by intratracheal BLM administration. The effect of NCL and PRF individually and in combination on different histological and biochemical parameters of fibrosis was investigated. Results revealed that NCL and PRF individually and in combination alleviated the histopathological changes, extracellular matrix deposition and myofibroblastic activation induced by BLM. NCL and PRF either individually or in combination inhibited the oxidative stress and subsequent pathways. They modulated the process of fibrogenesis by inhibiting MAPK/NF-κB and downstream cytokines. They inhibited STATs and downstream survival-related genes including BCL-2, VEGF, HIF-α and IL-6. Combining both drugs showed significant improvement in the tested markers in comparison to the monotherapy. NCL, therefore, has a potential synergistic effect with PRF in reducing the severity of PF.
Interrelationship of Extended Spectrum Beta-Lactamase Producers and Biofilm Formation among the Gram-Negative Bacteria from Tabuk, KSA

AIM: The present study investigates the production of extended-spectrum beta-lactamases (ESBL) and the formation of biofilm among different bacterial pathogens. METHODS: The study conducted prospective analysis on bacteria isolates (Gram-negative) from patients who have diagnosed with infections with bacteria between October 2020 and January 2022. RESULTS: The results showed that there were 53 biofilm producers in Escherichia coli. In contrast, Pseudomonas aeruginosa was observed to have the highest percentage, with 32/40 (80%) isolates being biofilm producers. The least number of isolates were Morganella morganii (n = 2) with two (100%) biofilm producers. The resistance in the biofilm positive isolates was high compared with biofilm negative. About 88% of phenotypic ESBL-positive isolates were biofilm producers, and 97% of cefotaxime-resistant biofilm-positive isolates were genotypic positive for CTX-M, TEM, and SHV genes. CONCLUSION: The present study has shown that protection against antibiotics through mucus production is possible due to bacteria’s reduced metabolic activity and diffusion of antibiotics across the biofilm matrix. In this study, all the bacterial strains of E. coli and Klebsiella pneumoniae were reported to be MDR and competent for establishing biofilm.
Design Choices for Learning Embeddings from Auxiliary Tasks for Domain Generalization in Anomalous Sound Detection

Emitted machine sounds can change drastically due to a change in settings of machines or varying noise conditions resulting in false alarms when monitoring machine conditions with a trained anomalous sound detection (ASD) system. In this work, a conceptually simple state-of-the-art ASD system based on embeddings learned through auxiliary tasks generalizing to multiple data domains is presented. In experiments conducted on the DCASE 2022 ASD dataset, particular design choices such as preventing trivial projections, combining multiple input representations and choosing a suitable back-end are shown to significantly improve the ASD performance.
Uneventful Coadministration of Seasonal Influenza and COVID-19 BNT162b2 Vaccines Two Weeks Post-Influenza Vaccination in an Egg-Allergic Subject: A Case Report

The COVID-19 pandemic took the world by storm, and although it has taken the world’s attention, it did not stop the spread of other communicable diseases. Seasonal influenza is a viral infection that could cause severe disease; therefore, annual influenza vaccination is highly recommended, especially among patients with a weakened immune system. However, such vaccination is contraindicated for people with hypersensitivity to the vaccine or any of its components, e.g., eggs. This paper describes a case of an egg-allergic individual who received an influenza vaccine containing egg protein, which only caused mild tenderness at the site of injection. Two weeks later, the subject received a double vaccination of a second booster dose of Pfizer-BioNTech and the seasonal influenza vaccine. The patient reported no local or systemic adverse reactions to the vaccine. This case report suggests vaccination safety for subjects with mild allergies to vaccine components.
Comparative Analysis of Human Hepatic Lesions in Dengue, Yellow Fever, and Chikungunya: Revisiting Histopathological Changes in the Light of Modern Knowledge of Cell Pathology

Arboviruses, such as yellow fever virus (YFV), dengue virus (DENV), and chikungunya virus (CHIKV), present wide global dissemination and a pathogenic profile developed in infected individuals, from non-specific clinical conditions to severe forms, characterised by the promotion of significant lesions in different organs of the harbourer, culminating in multiple organ dysfunction. An analytical cross-sectional study was carried out via the histopathological analysis of 70 samples of liver patients, collected between 2000 and 2017, with confirmed laboratory diagnoses, who died due to infection and complications due to yellow fever (YF), dengue fever (DF), and chikungunya fever (CF), to characterise, quantify, and compare the patterns of histopathological alterations in the liver between the samples. Of the histopathological findings in the human liver samples, there was a significant difference between the control and infection groups, with a predominance of alterations in the midzonal area of the three cases analysed. Hepatic involvement in cases of YF showed a greater intensity of histopathological changes. Among the alterations evaluated, cell swelling, microvesicular steatosis, and apoptosis were classified according to the degree of tissue damage from severe to very severe. Pathological abnormalities associated with YFV, DENV, and CHIKV infections showed a predominance of changes in the midzonal area. We also noted that, among the arboviruses studied, liver involvement in cases of YFV infection was more intense.
Computational Modeling of the Feasibility of Substituted [1.1.1]Propellane Formation from Anionic Bridgehead Bromide Precursors

[1.1. 1]Propellane, a compound whose structure includes two saturated carbons in which all four bonds are directed into a single hemisphere, is of theoretical interest, but has also seen recent practical applications. Mono-, di-, and trisubstituted derivatives of this propellane (by substitution of its CH2 bridges with O, S, NH, CF2, CO, SO, and SO2) remain unknown despite several computational studies that have suggested some may be stable. In this study, we show that, in several cases, substituted propellanes are spontaneously formed upon the attempted computational optimization of the geometries of anionic bridgehead bromide precursors using the ωB97X-D/aug-cc-pVDZ DFT method. Spontaneous formation suggests that these propellanes are at lower energy relative to the precursors and, therefore, are promising synthetic targets. The success or failure to spontaneously form the propellane is considered in relation to the length and strain energy of the central bridgehead-bridgehead bond, as well as the total strain energy of each propellane.
Strong self-focusing of a 7.2 MeV electron beam striking an aluminized mylar target

A substantial reduction in the spot size of a 7.2 MeV, 3 kA electron beam illuminating a thin aluminized mylar target has been observed during a 60 ns pulse. We attribute this to the emission of positive ions from the target, under the influence of the high energy flux of the beam, which perforates the target, These ions are then accelerated upstream and focused along the axis by the electron beam, where they partially neutralize the space-charge of the beam and produce a sharp pinch. After the pinch, the beam expands and forms filaments at the end of the pulse. By varying the initial beam transverse size, we have studied the influence of the beam density on the effect. A simulation of the effect with a particle-in-cell code provides a qualitative description and suggests that the ions responsible for the effect are Al/sup +/ and O/sup +/.