Miracle-dz/UniFAR-FTUs huggingface dataset:
Sodium houttuyfonate effectively treats acute pulmonary infection of Pseudomonas aeruginosa by affecting immunity and intestinal flora in mice

Introduction Pseudomonas aeruginosa is a major nosocomial pathogen that frequently causes ventilator-associated pneumonia in specific populations. Sodium houttuyfonate (SH) has shown mild antibacterial activity against P. aeruginosa in vitro, but the mechanism of potent antimicrobial activity of SH against P. aeruginosa infection in vivo remains unclear. Methods Here, using the mouse pneumonia model induced by P. aeruginosa nasal drip to explore the therapeutic effects of SH. Results We found that SH exhibits dose-dependent therapeutic effects of reducing P. aeruginosa burden and systemic inflammation in pneumonia mice. SH ameliorates inflammatory gene expression and production of inflammatory proteins, such as interleukin-6 (IL-6), nuclear factor kappa-B (NF-κB) and toll-like receptor 4 (TLR4), associated with the TLR4/NF-κB pathway in mice with P. aeruginosa pneumonia. Furthermore, we analyzed the intestinal flora of mice and found that compared with the model group, the abundance and diversity of beneficial bacterial flora of SH treatment groups increased significantly, suggesting that SH can improve the intestinal flora disorder caused by inflammation. In addition, SH improves alpha and beta diversity index and reduces species abundance differences of intestinal flora in pneumonia mice. Discussion Taken together, our presented results indicate that SH may effectively alleviate the acute pulmonary infection induced by P. aeruginosa by reducing the disturbance of regulating immunity and intestinal flora in mice.
New methods for the diagnosis and monitoring of cognitive function in patients with type 2 diabetes

The presence of type 2 diabetes acts as an accelerator of cognitive impairment (mild cognitive impairment and later dementia), with a significant impact on the management of the disease and its complications. Therefore, it is recommended to perform an annual evaluation of cognitive function in patients with diabetes older than 65 years. Current guidelines still recommend the use of the Minimental State Evaluation Test (MMSE) as screening test, but it has a modest sensitivity and specificity for identifying mild cognitive impairment. This represents an important gap because patients with mild cognitive impairment are at risk of progressing to dementia. The neurocognitive diagnosis is based on complex neuropsychological tests, which require specifically trained personnel and are time consuming, making its routine incorporation into daily clinical practice unfeasible. Therefore, at present there are no reliable biomarkers to identify patients with type 2 diabetes at increased risk of developing cognitive impairment. Since the brain and the retina have a common embryological origin, our Research Group, has worked over the last 10 years evaluating the usefulness of the retina as a “window” to the brain. We provided evidence that retinal microperimetry is a simple, feasible and useful tool for screening and monitoring cognitive function in patients with type 2 diabetes. We propose a review of actual tests recommended for screening of cognitive impairment as well as an update of new emerging methods, such as retinal microperimetry.
Doxorubicin aqueous systems at low concentrations: Interconnection between self-organization, fluorescent and physicochemical properties, and action on hydrobionts

Doxorubicin (Dox) is a highly effective cytostatic antibiotic that exhibits activity against a wide range of malignant neoplasms and is often used as the basis of various anti-tumor compositions. However, the use of Dox in therapeutic doses is associated with high systemic toxicity, which makes it urgent to find ways to reduce therapeutic concentrations, which is necessary primarily to minimize the side effects on the patient’s body, as well as to reduce the harmful effects on aquatic ecosystems, commonly polluted by toxic pharmaceuticals. Studying the self-organization, physicochemical and spectral patterns, and their relation to bioeffects of Dox solutions in the range of low concentrations can reveal useful insights into the unknown effects of Dox as a cytostatic and potential pollutant of ecosystems. The self-organization in solutions and on substrates, physicochemical and spectral properties, and action of Dox solutions on hydrobionts were studied in the range of calculated concentrations from 1·10−20 to 1·10−4 M by methods of dynamic and electrophoretic light scattering (DLS and ELS), scanning electron microscopy (SEM), scanning probe microscopy (SPM), fluorescence spectroscopy, UV absorption spectroscopy, conductometry, tensiometry, pH-metry. Certified techniques for monitoring the toxicity of natural water and wastewater were used to establish the interconnection between these phenomena. It was shown that aqueous solutions of Dox are dispersed systems which rearrange their dispersed phase measuring hundreds of nm in size (nanoassociates) at dilution, followed by concerted changes in nanoassociates’ parameters (size and ζ-potential) and properties of systems, as well as their bioassay results. SPM and SEM results confirm and complement the DLS and ELS data indicating the existence of nanoassociates in dilute Dox solutions.
Host autophagy limits Toxoplasma gondii proliferation in the absence of IFN-γ by affecting the hijack of Rab11A-positive vesicles

Introduction Autophagy has been recognized as a bona fide immunological process. Evidence has shown that this process in IFN-γ stimulated cells controls Toxoplasma gondii proliferation or eliminates its infection. However, little is known about the effect of T. gondii infection on the host cell autophagy in the absence of IFN-γ. Methods Multiple autophagy detection methods and CRISPR/CAS9 technology were used to study T. gondii-induced autophagy in HeLa and several other mammalian cell lines. Results Here, we report increased LC3 II, autophagosome-like membrane structures, enhanced autophagic flux, and decreased lysosomes in a range of mammalian cell lines without IFN-γ treatment after T. gondii infection. Specifically, disruption of host atg5 (a necessary gene for autophagy) in HeLa cells promoted the intracellular replication of T. gondii, with the transcript level of rab11a increased, compared with that in wild-type cells. Further, after T. gondii infection, the abundance of Rab11A remained stable in wild-type HeLa cells but decreased in atg5−/− mutant. Disruption of rab11a in the HeLa cells compromised the proliferation of T. gondii, and increased the transcription of gra2 in the parasite. Compared to the T. gondii wild-type RH∆ku80 strain, the ∆gra2 mutant induces enhanced host autophagy in HeLa cells, and results in slower replication of the parasite. Discussion Collectively, these results indicate that host cell autophagy can limit T. gondii proliferation in an IFN-γ independent manner, possibly by affecting the hijack of host Rab11A-positive vesicles by the parasite which involved TgGRA2. The findings provide novel insights into T. gondii infection in host cells and toxoplasmosis research.
The distinction between state and trait anxiety levels in patients with BPPV in comparison with healthy controls

Introduction An association exists between vestibular dysfunction and anxiety, yet a distinction between state and trait anxiety in patients with Benign Paroxysmal Positional Vertigo (BPPV) in comparison with healthy subjects has not been well established. The aim of this study is to assess both state and trait anxiety levels in patients with BPPV in comparison with healthy controls, and their relations with cardiac autonomic parameters. Materials and methods A case–control study, that included patients with BPPV (N = 18) referred to vestibular physiotherapy and gender and age matched healthy controls (N = 18). All participants completed the State–Trait Anxiety Inventory (STAI) questionnaire, while heart-rate (HR) and heart-rate variability (HRV) measures were recorded. Results Patients with BPPV (age range 32–66 years; 12 women and 6 men) showed a higher state anxiety level (10 points median difference, p = 0.001) in comparison with healthy controls (age range 34–66 years; 12 women and 6 men), yet no differences were found in trait anxiety score or total STAI score. Only among patients with BPPV, a positive-moderate correlation was found between state anxiety and HR (r = 0.53, p < 0.05), and a negative moderate-strong correlation was found between state anxiety and HRV (r = −0.67, p < 0.01). Discussion Patients with BPPV do not differ from healthy subjects in terms of predisposition to feel anxious, and only their temporary state anxiety levels are higher in comparison with healthy controls. We recommend researchers and clinicians who assess anxiety levels in patients with BPPV to distinguish between anxiety as a temporary state (state anxiety) and anxiety as a personality trait (trait anxiety).
Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.
First detection and molecular characteristics of bopivirus from goats in China

A metavirome analysis was performed and detected bopivirus in the diarrhoeal fecal samples of goats in China. A total of 136 fecal samples were collected from yeanlings between the dates of June 2021 and January 2022 in Sichuan province, China. Moreover, “Bopivirus B” strains were detected by a specific RT-PCR targeting the 3D gene of the virus. The results showed that the overall detection rate of “Bopivirus B” was 19.12% (26/136). Additionally, there was a higher detection rate (24.05%, 19/79) in the fecal samples collected from yeanlings with diarrhea compared to those from asymptomatic animals (12.28%, 7/57). In these samples, no other common diarrhea-causing pathogens were detected except for three enteric viruses, namely caprine enterovirus, caprine kobuvirus and caprine hunnivirus (with detection rates of 13.97, 13.97, and 8.82%, respectively). Subsequently, full-length VP4, VP2, VP3, and VP1 genes from “Bopivirus B”-positive samples were amplified, cloned, sequenced, and analyzed. The phylogenetic analysis performed on the VP1 genes revealed that the identified bopivirus belonged to genotype B1 (seven strains) and B2 (three strains) and presented a high genetic diversity. Furthermore, a complete genome sequence of a “Bopivirus B” strain (SWUN/B1/2022) was obtained using PCR from fecal sample of a diarrhoeal yeanling. The complete genome was 7,309 nucleotides in length with a standard picornavirus genome organization, and shares 93.10% and 91.10% nucleotide similarity with bopivirus B1 genotype strain ovine/TB14/2010-HUN and bopivirus B2 genotype strain goat/AGK16/2020-HUN, respectively. According to the species classification criteria put forward by the International Committee on Taxonomy of Viruses and VP1 genotype, the strain SWUN/B1/2022 belongs to the bopivirus B1. This strain has unique amino acid substitutions in the VP4, VP2, VP3, and VP1 genes. Moreover, genomic recombination analysis revealed that this strain may be a minor parental strain of bopivirus B1 ovine/TB14/2010-HUN. Evolutionary analysis based on the 2C and 3CD genes revealed that the new bopivirus B1 strain SWUN/B1/2022 presents a unique evolutionary pattern. This study provided evidence to suggest that “Bopivirus B” is circulating with substantial genetic diversity in goats in China at present, and the mixed infection of “Bopivirus B” with other enteric viruses should be considered to be a composite factor in the occurrence of viral diarrhea in goats.
Whole-transcriptome analyses of sheep embryonic testicular cells infected with the bluetongue virus

Introduction bluetongue virus (BTV) infection triggers dramatic and complex changes in the host's transcriptional profile to favor its own survival and reproduction. However, there is no whole-transcriptome study of susceptible animal cells with BTV infection, which impedes the in-depth and systematical understanding of the comprehensive characterization of BTV-host interactome, as well as BTV infection and pathogenic mechanisms. Methods to systematically understand these changes, we performed whole-transcriptome sequencing in BTV serotype 1 (BTV-1)-infected and mock-infected sheep embryonic testicular cells, and subsequently conducted bioinformatics differential analyses. Results there were 1504 differentially expressed mRNAs, 78 differentially expressed microRNAs, 872 differentially expressed long non-coding RNAs, and 59 differentially expressed circular RNAs identified in total. Annotation from the Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of competing endogenous RNA networks revealed differentially expressed RNAs primarily related to virus-sensing and signaling transduction pathways, antiviral and immune responses, inflammation, and development and metabolism related pathways. Furthermore, a protein-protein interaction network analysis found that BTV may contribute to abnormal spermatogenesis by reducing steroid biosynthesis. Finally, real-time quantitative PCR and western blotting results showed that the expression trends of differentially expressed RNAs were consistent with the whole-transcriptome sequencing data. Discussion this study provides more insights of comprehensive characterization of BTV-host interactome, and BTV infection and pathogenic mechanisms.
Application of Support Vector Machine Classifier for Transformer Winding Faults Diagnosis

Diagnosing faults based on artificial intelligence is one of the modern and effective methods, so it has become necessary to apply it to power transformers. This paper proposes a new methodology for detecting the type, location, and severity of power transformer faults, based on the analysis of frequency responses (FRA) and support vector machines (SVM). The method was tested on two faults that were simulated on a transformer winding model, where databases were formed to train the support vector machine (SVM) by collecting (FRA) signals in defective and healthy conditions and analyzing them by statistical indicators. The obtained results confirm the effectiveness of the proposed method in determining the type, location, and extent of faults with high accuracy, and its ability to contribute to the development of the application of machine learning in power transformers.
Improving Persona Understanding for Persona-based Dialogue Generation with Diverse Knowledge Selection

A significant goal in an open-domain dialogue system is to make chatbots generate more persona coherent responses given a context. To achieve this goal, some researchers attempt to introduce persona information into neural dialogue models. However, these neural dialogue models describe excessively persona traits during the conversation, which still suffer from the problem of generating boring and meaningful responses. In this paper, we divide the open-domain personalized dialogue generation task into two processes, persona recognition and persona fusion. In the persona recognition process, we use the pre-training model to encode the personas and conversation history independently, which is beneficial to the persona information fusion. Then, we design a dynamic persona fusion mechanism to effectively mine the relevance of dialogue context and persona information, and dynamically predict whether to incorporate persona features in the process of the dialogues. Our model outperforms with 1.23% in Acc. , 0.36% in BLEU, 0.92% in F1, and 0.036% in Distinct than baseline models. The experimental results on the ConvAI2 dataset illustrate that the proposed model is superior to baseline approaches for generating more coherent and persona consistent responses.
Ensemble leaning-CNN for reducing JPEG artifacts

In this paper, we considered the problem of reducing the artifacts that result when images are compressed by the JPEG encoder. In the last years, many deep learning based techniques, for reducing the JPEG artifacts, appeared. None of these techniques gives always the best quality. In addition, the results depend on the image content and the used quantization. In our work, we proposed to improve the results of existing techniques by an ensemble learning method. Our goal is to obtain the best result for most of the images. For this, we have combined three of the state-of-the-art techniques namely DnCNN, MwCNN, and RidNET. The results of artifacts reduction show a high performance of the proposed network, compared to the individual networks. Thus, it improves the quality PSNR with a gain ranging from 0.01 dB up to 0.34 dB.
Anti-disturbance Control for Six Phase Permanent Magnet Motor System in Aerospace Application

In order to develop fault switching performance of fault tolerant permanent magnet synchronous motor (FTPMSM), a novel anti-disturbance speed control based on adaptive robust scheme is proposed in this paper subject to several faults, load disturbances, parameter uncertainties, and input constraints. For a six phase FTPMSM system in electromechanical actuator (EMA) aerospace, apart from external load disturbances and changes in system parameters, the electromagnetic torque fluctuation in the fault transient operation is an unavoidable part of the FTPMSM system that can decrease the functionality of the FTPMSM and potentially create system instability. Furthermore, to improve the effectiveness of the fault transient of FTPMSM for EMA aerospace applications in case of open-circuit(OC) and short-circuit (SC) fault situations, we propose a novel anti-disturbance speed control approach based on adaptive robust under input constraints that includes ripple of electromagnetic torque in the process of fault switch. The suggested control can confirm the uniform boundedness and uniform ultimate boundedness of the FTPMSM irrespective of external disturbances in load, parameter fluctuations, and input constraints which are particularly vital for fault switch operation. The Matlab simulation demonstrates that the proposed control technique for the FTPMSM system provides excellent fault tolerant presentation and stability during the fault transient process under faults of OC and SC, load torque step, and system parameter changes.
Power Control of Repurposing-Battery Modular Multilevel Converter

This paper proposes a power control of the repurposing-battery modular multilevel converter (RBMMC) for hybrid ac/dc power distribution system. All submodules (SMs) of the MMC are constructed by RB-based half-bridge inverters. According to decoupling ac- and dc-circuit models, the RBMMC is able to convert energy from repurposing batteries to ac grid and/or to dc grid in a bidirectional manner. Most importantly, power balancing algorithms between phases and between arms are proposed to extend operational stability considering state-of-charge (SOC) of RBs, which is a significant feature to increase reliability of RBMMC in the power distribution system. Simulation results are provided to verify the control method.
GENERALIZATION OF A RESULT ON “REGRESSIONS, SHORT AND LONG”

This paper is concerned with the problem of combining a data set that identifies the conditional distribution P(y|x) with one that identifies the conditional distribution P(z|x) to identify the regressions E(y|x,·) ≡ [E(y|x,z = j),j ∈ Z] when the conditional distribution P(y|x,z) is unknown. Cross and Manski (2002, Econometrica 70, 357–368) studied this problem and showed that the identification region of E(y|x,·) can be precisely calculated when y has finite support. Here we generalize the result of Cross and Manski, showing that the identification region can be precisely calculated also in the case in which y has infinite support.We are grateful to the co-editor Paolo Paruolo, an anonymous referee, Maria Goltsman, Nick Kiefer, Tymon Tatur, and Tim Vogelsang for useful comments. Any remaining errors are our own responsibility.Financial support from Northwestern University's Dissertation Year Fellowship is gratefully acknowledged.
Learning-based Position Sensorless Control in Low-speed Region for SMPMSM

Many studies have been conducted on position-sensorless control of Permanent Magnet Synchronous Motor(PMSM). However, since there is no salient polarity in the case of a Surface-Mounted Permanent Magnet Synchronous Motor(SMPMSM), low-speed sensorless control through signal injection is inevitable. However, it is hard to implement. Further, it has a noise problem. This paper proposes a low-speed position-sensorless method using machine learning to overcome these problems. Among many machine learning models, denoising autoencoder[1] is used. It could restore the noisy input data well. And middle layer could be considered to imply the compressed characteristics of the original data. A neural network module that infers rotor angle is added for using this characteristic. The effectiveness of the proposed model is verified by experimentally showing that it is possible to drive over a wider low-torque and low-speed conditions better than the conventional method using a back Electro-Motive Force(EMF).
Bimodal System for Facial Emotion Recognition Based on Deep Learning Neural Networks

Current paper presents the development of bimodal system for facial emotions recognition. There are different modalities related to the human emotions. Some of them are related to the facial ones, other to the body positions and some are related to the surrounding scene or weather. Current bimodal system is based on two deep learning neural networks /DNN/. First DNN recognizes seven basic facial emotions and second DNN recognizes five weather conditions. The fusion of the results of both networks increases the accuracy of final emotions recognition. Selection of DNN for Facial Emotions Recognition /FER/ has been described in a paper presented on ICAI 2020 and selection of weather condition DNN is based on the comparative analysis of existing weather condition systems, introduced in this paper. The improvement of the emotions recognition is based on possibility matrix combining the predictions of both DNNs.
15kV/50A SiC AC Switch Based On Series Connection of 1.7kV MOSFETs

High frequency power switches with higher bidirectional blocking voltages are needed for medium voltage grid applications such as Solid State Transformer (SST). This paper presents a 15kV/50A SiC AC switch based on 1700V SiC MOSFETs in series connection. The AC switch consists of two identical parts in source-to-source connection and each part includes ten 1700V SiC MOSFETs in series. The ten SiC MOSFETs are driven by a common gate signal. The mismatch of the gate signals is a key factor which affects the voltage sharing across the devices. This mismatch is experimentally studied and is less than 10ns. SIMetrix simulations at 12kV/5A turn-off transient is performed based on the tested mismatch time and dynamic balancing capacitor is then designed to achieve satisfactory voltage balance. 10kV/15A single pulse test (SPT) is carried out to verify the feasibility of the designed AC switch. Actual voltage balancing during turn-on and turn-off are experimentally observed. Other functionalities and qualities of the fabricated AC switch are characterized for future SST applications. Auxiliary gate power supplies with 30kV galvanic isolation capability are designed to support the intended medium voltage applications.