Miracle-dz/UniFAR-FTUs huggingface dataset:
CD73 Attenuates Alcohol-Induced Liver Injury and Inflammation via Blocking TLR4/MyD88/NF-κB Signaling Pathway

Background Alcoholic liver disease (ALD) is liver damage caused by long-term drinking. Inflammation plays a central role in the progression of ALD. CD73 is a ubiquitously expressed glycosylphosphatidylinositol-anchored glycoprotein that is a key enzyme that converts ATP into adenosine. Evidence has shown that CD73 plays an important role in many diseases, but the role and mechanism of CD73 in alcohol-induced liver injury and inflammation is still unclear. Methods The alcohol-induced liver injury and inflammation mouse model was established. The rAAV9-CD73 was used to overexpress CD73. Isolation of primary macrophages (MΦ) from the liver was conducted. The effects of CD73 on alcohol-induced liver injury and inflammation were evaluated by quantitative real‑time PCR, Western blotting, ELISA, and immunohistochemical assay. Flow cytometry was used to detect the cell cycle and apoptosis. Results Our results showed that overexpression of CD73 can reduce alcohol-induced liver damage, lipid accumulation, and the secretion of inflammatory cytokines. pEX3-CD73 can promote RAW264.7 cells proliferation and inhibit apoptosis via suppressing the activation of TLR4/MyD88/NF-κB signaling pathway. Inhibition of TLR4 further enhanced the anti-inflammatory effect of overexpression of CD73. Conclusion Overexpression of CD73 can reduce alcohol-induced liver injury and inflammation. CD73 may serve as a potential therapeutic target for ALD.
Bioinformatics Analysis for the Roles of long non-coding RNAs in the Immune Responses of Mouse Peripheral Blood Mononuclear Cells Exposed to Low-Dose Ionizing Radiation

Recent advances in sequencing technologies enabling more in-depth genomic and transcriptomic analyses have revealed that many long non-coding RNAs (lncRNAs) are functionally associated with human diseases. However, the expression levels of lncRNAs are low in abundance, which poses several challenges for their identification and analysis. We read with great interest the article by Qi and colleagues. The authors revealed for the first time the profile of differentially expressed lncRNAs involved in the responses of the single and chronic low-dose ionizing radiation (LDIR) through the integrated analysis of microarray data which is profound. The microarray data analysis strategy of this study should be carefully considered and further clarified. According to the authors’ description, they seem to use unadjusted P values and fold change of expression values when defining significantly differentially expressed lncRNAs. However, due to the characteristics of lncRNAs and high false positives caused by multiple comparisons, it seems that a specialized high-level microarray analysis method may be more suitable, especially when the number of detected lncRNAs is large. Only accurate screening of differential genes can ensure the reliability of pathway enrichment analysis results. A study comparing several data analysis schemes revealed that linear modeling with empirical Bayes moderation showed good control of the false discovery rate and reasonable sensitivity when defining significantly differentially expressed lncRNAs. In our opinion, the authors might consider using linear models for microarray analysis (Limma), an R/Bioconductor software package that analyzes microarray data using linear models, to test the results. Referring to Limma results, choosing appropriate expression fold changes and false discovery rate < . 05 as the cutoff is a robust method to analyze the changes of gene expression. We agree on the importance of comprehensive identification of lncRNAs in single and chronic LDIR responses. More rigorous microarray analysis strategies will be required for insights into these complicated data sets.
Factors affecting land value in an Indian city

ABSTRACT Land valuation is the process of determining the value of the landed property, excluding all human-made improvements. This paper presents a comprehensive assessment and modelling of the land value and its determinants. Thiruvananthapuram, the capital city of Kerala state, is selected as the study area. Land value modelling applied in this study is divided into three stages. The first stage uses a standard hedonic price model for land value estimation and spatial autocorrelation diagnostics. The spatial error model is appropriate for variable selection and modelling based on Moran’s I and Lagrange Multiplier test statistics in the second stage. The last stage of the study uses the spatial error model for land value analysis, and the results are compared with OLS regression. Proximity to major highways, disaster history, concentration of commercial establishments, and permissible FAR are the major factors affecting land value in the study area. Few other parameters like water frontage and noise pollution have a reverse relationship with similar studies in developed countries. The results indicate that factors influencing land value in Indian cities are different from the European and American cities. The study provides critical insights into the land price variation of an Indian city at a micro-level.
Evaluation of a Telenutrition Weight Loss Intervention Among Middle-Aged and Older Men in Appalachia

Evaluation of a Telenutrition Weight Loss Intervention Among Middle-Aged and Older Men in Appalachia Afnan Majed Alamoudi Obesity rates among middle-aged and older men in WV are among the highest in the nation. Understanding baseline and intervention factors contributing to losing clinically significant weight are essential to developing effective interventions in target groups. Thus, the purpose of this study is to determine factors associated with losing ≥ 5% of baseline weight among participants of a 12week pilot randomized controlled telenutrition trial for middle-aged and older men in WV. The study analyzed data from 56 participants who completed the trial. Participants were classified as losing ≥ 5 % or < 5 % of baseline body weight. Diet quality was assessed using 4-day diet records and the Healthy Eating Index (HEI)-2015. Baseline factors and behavioral strategies (i.e., selfmonitoring, self-efficacy, and social support) were assessed via survey. Inverse prediction analysis demonstrated that participants who lost ≥ 3.5% of baseline weight at 6 weeks were at least 75% likely to lose ≥ 5% weight at 12 weeks. Predictive models were developed using stepwise logistic regression to assess multiple predictors of losing ≥ 5% of baseline weight at 12 weeks and ≥ 3.5% of baseline at 6 weeks. Weight loss at 6 weeks was a strong predictor of weight loss at 12 weeks, in addition to previous weight loss attempts and weekly self-weight-monitoring (p<0.05). Specifically, a greater probability of losing ≥ 5% of baseline weight at 12 weeks was estimated for participants who: always selfmonitored weekly weight (odds ratio (OR) 34), did not attempt other weight loss programs in the past (OR 64), and had lost weight at 6 weeks (OR 5 for one percent increase in weight loss at 6 weeks). Analysis of factors leading to the ≥ 3.5% baseline weight loss at 6 weeks detected that increasing HEI change score at 6 weeks (OR 1.1 for each one-unit increase of HEI), and report of a family starting to eat better (OR 5) were influential. These findings suggest that for middle-aged men with obesity, early weight loss is important to losing ≥ 5% of baseline weight at the end of the intervention. The results support the use of behavioral strategies such as increased self-monitoring of weekly weight and focusing on healthier eating for the whole family, as these factors were related to better weight loss outcomes at the end of the intervention. Participants should be encouraged to self-monitor their weekly weights and dietary intake and be provided strategies to help them adhere to a high-quality diet in future interventions.
Life cycle and ecology of mayflies from Sierra Nevada (Spain), IV

The life cycle, distribution and autoecology of Ephemerella ignita (PODA, 1761), Ephemera danica MULLER, 1764, Caenis luctuosa BURMEISTER, 1839, Epeorus sylvicola ( P I ~ T , 1865) and E. torrentium EATON, 1881 are presented as a result of monthly samples taken from six streams in the Sierra Nevada mountains (southern Spain).
An updated checklist of YOY fish occurrence in the shallow perimetral areas of the Mar Menor (Western Mediterranean Sea)

An updated checklist of YOY fish in shallow areas of the Mar Menor (Western Mediterranean) The Mar Menor is one of the largest coastal lagoons in the Mediterranean basin. This study provides an updated checklist of the fish species inhabiting its shallow areas as young of the year in the period 2018-2019. A total of 43 taxa in 19 families were detected. This information should be useful to establish environmental monitoring programmes, as well as the implementation of management actions.
Effects of lactic acid-producing bacteria as direct-fed microbials on the ruminal microbiome.

The objective of this study was to evaluate ruminal microbiome changes associated with feeding Lactobacillus plantarum GB-LP1 as direct-fed microbials (DFM) in high-producing dairy cow diets. A dual-flow continuous culture system was used in a replicated 4 × 4 Latin square design. A basal diet was formulated to meet the requirements of a cow producing 45 kg of milk per day (16% crude protein and 28% starch). There were 4 experimental treatments: the basal diet without any DFM (CTRL); a mixture of Lactobacillus acidophilus, 1 × 109 cfu/g, and Propionibacterium freudenreichii, 2 × 109 cfu/g [MLP = 0.01% of diet dry matter (DM)]; and 2 different levels of L. plantarum, 1.35 × 109 cfu/g (L1 = 0.05% and L2 = 0.10% of diet DM). Bacterial samples were collected from the fluid and particulate effluents before feeding and at 2, 4, 6, and 8 h after feeding; a composite of all time points was made for each fermentor within their respective fractionations. Bacterial community composition was analyzed through sequencing the V4 region of the 16S rRNA gene using the Illumina MiSeq platform. Sequenced data were analyzed on DADA2, and statistical analyses were performed in R (RStudio 3.0.1, https://www.r-project.org/) and SAS 9.4 (SAS Institute Inc.); orthogonal contrasts were used to compare treatments. Different than in other fermentation scenarios (e.g., silage or beef cattle high-grain diets), treatments did not affect pH or lactic acid concentration. Effects were mainly from overall DFM inclusion, and they were mostly observed in the fluid phase. The relative abundance of the phylum Firmicutes, family Lachnospiraceae, and 6 genera decreased with DFM inclusion, with emphasis on Butyrivibrio_2, Saccharofermentans, and Ruminococcus_1 that are fibrolytic and may display peptidase activity during fermentation. Lachnospiraceae_AC2044_group and Lachnospiraceae_XPB1014_group also decreased in the fluid phase, and their relative abundances were positively correlated with NH3-N daily outflow from the fermenters. Specific effects of MLP and L. plantarum were mostly in specific bacteria associated with proteolytic and fibrolytic functions in the rumen. These findings help to explain why, in the previous results from this study, DFM inclusion decreased NH3-N concentration without altering pH and lactic acid concentration.
Noziplastischer Schmerz

Noziplastische Schmerzen sind eine dritte mechanistische Schmerzkategorie. Anders als bei nozizeptiven oder neuropathischen Schmerzen findet sich weder eine Gewebeschädigung, noch eine Läsion im somatosensorischen Nervensystem. Die IASP-Definition erklärt die Pathophysiologie als veränderte Nozizeption infolge einer Modulation der Reizverarbeitung. Die Breite der Definition und ihre nur entfernte Verknüpfung mit neurophysiologischen Mechanismen machen sie allerdings potentiell zum Sammelbecken unspezifischer und unklarer Befunde.
Power System Transient Stability Assessment and Stability Margin Prediction Based on Compound Neural Network

A method based on compound artificial neural network(ANN)to predict critical clearing time(CCT)margin and to do transient stability assessment is derived in this paper.It consists of probabilistic neural network(PNN)and radial basic function(RBF)network to integrate their advantages.PNN network is first used to classify sampling data and it can consider the overlapping influence in adjacent samples in the classification.Then RBF network is used to forecast the CCT margin based on the classification results.A self-checking procedure and an amending procedure are added to improve the prediction accuracy.New England 39 bus system is used to do the validation.Numerical studies reveal that the proposed method is better than the traditional BP and RBF methods not only on predictive accuracy but also on calculation speed.So the given method is helpful to power system transient stability assessment.
An Overview of Alkylresorcinols Biological Properties and Effects

The investigation of alkylresorcinols has drawn an increasing interest recently. Alkylresorcinols (ARs) are natural chemical compounds synthesized by bacteria, fungi, sponges, and higher plants, possessing a lipophilic polyphenol structures and a myriad of biological properties. Human takes ARs as a component of a whole grain diet (from whole grain rye, wheat, and barley products), and thus, alkylresorcinols are frequently used as whole grain intake markers. Besides, ARs are considered as promising bioregulators of metabolic and immune processes, as well as adjuvant therapeutic agents for antimicrobial and anticancer treatment. In this review, we attempted to systematize the accumulated information concerning ARs origin, metabolism, biological properties, and their effect on human health.
Research on Iterative Learning Control to Long Stroke Linear Motor

An nm-level positioning precision is required in the process of scanning and exposing the wafer stage of lithography.A six degree of freedom(DOF)complex movement system can meet the requirement by using rough movement of long stroke linear motor and tiny movement of short stroke Lorentz motor.In order to reduce the movement scope and acceleration of Lorentz motor,the tracking precision of linear motor must be improved.An open-closed-loop D-type iterative learning controller(ILC)is proposed to improve trajectory-tracking performance of permanent magnet linear synchronous motors(PMLSM). The controller consists of three terms:a PID controller which enhances robustness to disturbances and system parameter variability,a feed-forward compensator which improves the real-time tracking ability and an ILC which generally improves the performance in the repetitive tasks by using the information from previous iterations.The experimental results show that tracking precision of PMLSM can be effectively improved by the scheme.
Exact Static Plane Symmetric Soliton-Like Solutions to the Nonlinear Interacting Electromagnetic and Scalar Field Equations in General Relativity

This research work is related to soliton solutions considered as models that can describe the complex configuration of elementary particles from the study of the interactions of their fields. It is interested in the interaction of fields between two different elementary particles by expressing their physical properties. For that, we have obtained, exact static plane symmetric soli-ton-like solutions to the nonlinear equations of interacting electromagnetic and scalar fields taking into account the own gravitational field of elementary particles using the calibrated invariance function ( ) P I . It has been proved that all solutions of the Einstein, nonlinear electromagnetic and scalar field equations are regular with the localized energy density. Moreover, the total charge of particles is finite and the total energy of the interaction fields is bounded. It have been emphasized the importance to the own gravitational field of elementary particles and the role of the nonlinearity of fields in the determination of these solutions. In flat space-time, soliton-like solutions ex-ist but the total energy of the interaction fields is equal to zero. We have also shown that in the linear case, soliton-like solutions are absent.
Overexpression of miR-375 reverses the effects of dexamethasone on the viability, migration, invasion and apoptosis of human airway epithelial cells by targeting DUSP6

Airway epithelial cell (AEC) dysfunction has been proven to be involved in the pathogenesis of asthma, which may be induced by the use of dexamethasone (Dex). The altered expression of microRNAs (miRNAs/miRs) has been found in asthma. However, the detailed mechanisms responsible for the effects of miR-375 on Dex-induced AEC dysfunction remain elusive. Thus, the present study aimed to elucidate these mechanisms. Following treatment with Dex for 0, 6, 12 and 24 h, AEC viability, migration, invasion and apoptosis were examined using Cell Counting Kit-8 (CCK-8), wound healing and Transwell assays, and flow cytometry, respectively. The expression levels of miR-375, dual specificity phosphatase 6 (DUSP6) and apoptosis-related proteins (Bcl-2, Bax, cleaved caspase-3) were measured using reverse transcription-quantitative polymerase chain reaction and western blot analysis. The target genes and potential binding sites of miR-375 and DUSP6 were predicted using TargetScan and confirmed using dual-luciferase reporter assay. The viability, migration, invasion and apoptosis of Dex-treated AECs were further assessed with or without miR-375 and DUSP6. In the AECs (9HTE cells), Dex treatment suppressed cell viability and miR-375 expression, whereas it promoted cell apoptosis and the expression of DUSP6, the target gene of miR-375. The overexpression of miR-375 reversed the effects of Dex treatment on miR-375 expression, cell viability, migration and invasion, and apoptosis-related protein expression; in turn, these effects were reversed by the overexpression of DUSP6, with the exception of miR-375 expression. On the whole, the present study demonstrates that the overexpression of miR-375 counteracts the effects of Dex treatment on AEC viability, migration, invasion and apoptosis by targeting DUSP6. Thus, it was suggested that the downregulated expression of miR-375 may be a therapeutic target for AEC dysfunction.
Poorly Differentiated Large Cell Neuroendocrine Carcinoma of the Colon: A Case Report

Colon cancer is one of the most common diagnoses of cancer and a leading cause of death in America. Large cell neuroendocrine tumors are a very uncommon type of colon cancer that tends to have a poor prognosis. Usually, these tumors are only found at the time of metastasis making them even more difficult to treat. A 65-year-old female presented with worsened generalized abdominal pain associated with abdominal distention. She had not had a bowel movement in over a week and did not have any flatulence. She had a colonoscopy four years prior that was normal. Physical examination was significant for abdominal distention and a large right-sided palpable mass in her abdomen with generalized tenderness. A CT scan showed a large irregular mass at least 9.8 x 10.5 cm at the mid to distal ascending colon resulting in significant colonic narrowing significant for a large bowel obstruction. The CT also demonstrated suspicious nodules in the lung, lesions in the liver, and lymphadenopathy. She had an exploratory laparotomy with an extended hemicolectomy to remove the mass. Pathology revealed the mass was neuroendocrine carcinoma, a large cell subtype, that was poorly differentiated with involvement of at least 32 of 34 lymph nodes. This tumor was positive for AE1/AE3, CEA, CK20, and synaptophysin. Ki-67 showed 70% positivity. TTF1 was negative and ruled out a primary lung tumor. Microsatellite immunostains were positive for MLH-1, MSH-2, MSH-6, and PMS2. The patient was started on Carboplatin AUC6 and Etoposide 100mg/m2 in three-week intervals. Pegfilgrastim was also added to her treatment plan every 21 days. This is a review of a female who presented with colonic obstruction that was found to be poorly differentiated large cell neuroendocrine carcinoma after a previous negative colonoscopy.
Selective Cancellable Multi-Biometric Template Generation Scheme Based on Multi-Exposure Feature Fusion

This article introduces a new cancellable multi-biometric system based on the combination of a selective encryption method and a deep-learning-based fusion technology. The biometric face image is treated with an automatic face segmentation algorithm (Viola-Jones), and the image of the selected eye is XORed with a PRNG (Pseudo Random Number Generator) matrix. The output array is used to create a primary biometric template. This process changes the histogram of the selected eye image. Arnold’s Cat Map is used to superimpose the PRN pixels only on the pixels of the primary image. Arnold’s cat map deformed eyes are encrypted using the Advanced Encryption Standard (AES) to encrypt the biometric data stored in the database. In addition, the AES master key is used for the same person in the identity verification process to verify the biometric identity. It is created from the fingers of the right hand, and the right eye is integrated into this process using deep learning technology. The deep learning fusion process can prevent attacks on the biometric system as a whole. In order to avoid damage to the eye or fingerprint images, the design considers the other eye and fingerprint images.
Research on Improved Microsource Droop Control Method

The conventional microsource wireless droop control base on "power-voltage-current" close loop is widely used. The power sharing performance of this method is significantly affected by the resistive line impedance. This paper analyzes the effectiveness of two existing solutions: the adjustment method by choosing proper control parameters has limited regulating range, which means a poor performance; the virtual impedance control method has a wide range. But it has the problem of voltage sag, this paper provides an improved method with no Q-V power close loop. This method simulates the virtual impedance as the virtual synchronous generator inductance; uses the virtual impedance voltage to realize the microsource Q-V voltage droop characteristic, which can save the microsource voltage sag amplitude. Finally, simulation results and experimental results on the parallel inverter are provided to prove the performance of this new control method.
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction. However, existing shallow GNNs are insufficient to capture the global structure of compounds. Besides, the interpretability of the graph-based DTA models highly relies on the graph attention mechanism, which can not reveal the global relationship between each atom of a molecule. In this study, we proposed a deep multiscale graph neural network based on chemical intuition for DTA prediction (MGraphDTA). We introduced a dense connection into the GNN and built a super-deep GNN with 27 graph convolutional layers to capture the local and global structure of the compound simultaneously. We also developed a novel visual explanation method, gradient-weighted affinity activation mapping (Grad-AAM), to analyze a deep learning model from the chemical perspective. We evaluated our approach using seven benchmark datasets and compared the proposed method to the state-of-the-art deep learning (DL) models. MGraphDTA outperforms other DL-based approaches significantly on various datasets. Moreover, we show that Grad-AAM creates explanations that are consistent with pharmacologists, which may help us gain chemical insights directly from data beyond human perception. These advantages demonstrate that the proposed method improves the generalization and interpretation capability of DTA prediction modeling.