Miracle-dz/UniFAR-FTUs huggingface dataset:
Diet quality, gut microbiota, and microRNAs associated with mild cognitive impairment in middle-aged and elderly Chinese population.

BACKGROUND Diet, the gut microbiota, and microRNAs (miRNAs) have been recognized as factors possibly influencing the pathogenesis of Alzheimer's disease. However, the combined role of diet, the gut microbiota, and miRNAs in mild cognitive impairment (MCI) has not been extensively investigated. OBJECTIVES To examine the associations among diet quality, the gut microbiota, miRNAs, and risks of MCI in middle-aged and elderly Chinese population. METHODS Participants were drawn from the Effects and Mechanism Investigation of Cholesterol and Oxysterol on Alzheimer's disease (EMCOA) study. We included 2239 participants who completed comprehensive cognitive tests and dietary surveys at baseline and follow-up. Chinese Dietary Guidelines Index 2018 (CDGI-2018), Energy-Adjusted Dietary Inflammatory Index (E-DII) and Healthy Lifestyle Score (HLS) values were calculated based on data from a standard questionnaire. Changes of gut microbiota and serum miRNA expression were detected in 75 MCI and 52 healthy participants. The receiver operating characteristic curve was performed based on a logistic regression model distinguishing patients with MCI from healthy controls. RESULTS Higher CDGI-2018 (RR, 0.75; 95% CI: 0.58-0.98) and HLS (RR, 0.54; 95% CI: 0.34-0.84) values and lower E-DII (RR, 1.46; 95% CI: 1.14-1.87) values were significantly associated with a lower risk of MCI. Lower microbial diversity and abundance of Faecalibacterium, unidentified Ruminococcaceae, and Alistipes were found in MCI participants compared with healthy controls. The abundance of ProteobacteriaandGammaproteobacteria was higher in MCI participants and was correlated with hsa-let-7g-5p, hsa-miR-107, and hsa-miR-186-3p. The predictive signatures of diet quality scores, differential gut microbiota, and serum miRNAs had good performance on distinguishing MCI participants from healthy individuals (AUC = 0.91). CONCLUSIONS A better diet quality is associated with a lower risk of MCI, which may be mediated by the microbiota composition and miRNA expression. These findings suggest that diet, the gut microbiota, and miRNAs may serve as a combinatorial biomarker to identify MCI patients and provide new insight for the diagnosis and prevention of MCI. This study was registered at the Chinese Clinical Trial Registry as ChiCTROOC-17011882.
Inferring Adaptive Codon Preference to Understand Sources of Selection Shaping Codon Usage Bias

Abstract Alternative synonymous codons are often used at unequal frequencies. Classically, studies of such codon usage bias (CUB) attempted to separate the impact of neutral from selective forces by assuming that deviations from a predicted neutral equilibrium capture selection. However, GC-biased gene conversion (gBGC) can also cause deviation from a neutral null. Alternatively, selection has been inferred from CUB in highly expressed genes, but the accuracy of this approach has not been extensively tested, and gBGC can interfere with such extrapolations (e.g., if expression and gene conversion rates covary). It is therefore critical to examine deviations from a mutational null in a species with no gBGC. To achieve this goal, we implement such an analysis in the highly AT rich genome of Dictyostelium discoideum, where we find no evidence of gBGC. We infer neutral CUB under mutational equilibrium to quantify “adaptive codon preference,” a nontautologous genome wide quantitative measure of the relative selection strength driving CUB. We observe signatures of purifying selection consistent with selection favoring adaptive codon preference. Preferred codons are not GC rich, underscoring the independence from gBGC. Expression-associated “preference” largely matches adaptive codon preference but does not wholly capture the influence of selection shaping patterns across all genes, suggesting selective constraints associated specifically with high expression. We observe patterns consistent with effects on mRNA translation and stability shaping adaptive codon preference. Thus, our approach to quantifying adaptive codon preference provides a framework for inferring the sources of selection that shape CUB across different contexts within the genome.
Development of asymmetrical flow field-flow fractionation for the characterization of proteins, protein aggregation, and nanoparticles

Field-flow fractionation (FFF) is a family of analytical techniques used to characterize macromolecules and particles from ∼1 nm to >1 μm. Its versatility has allowed a number of analytical challenges to be addressed, but several limitations still exist. Advances in asymmetrical flow field-flow fractionation (AF4) coupled with UV-Vis, multiangle light scattering (MALS), and/or dynamic light scattering (DLS) were made to overcome current limitations in the characterization of proteins, protein aggregates, and nanoparticles. Formation of protein aggregates in protein therapeutics is a major concern due to reduced drug efficacy and potential immunogenicity. A lack of reliable analytical methods that cover the submicron (0.1-1 μm) size range has been a major challenge for protein aggregate characterization. The development of a simple AF4 method with good size selectivity (>0.5) from 1 nm to 1 μm allowed the formation of submicron aggregates to be fitted by the Lumry-Eyring Nucleated Polymerization (LENP) model for the first time. A comparison of aggregation kinetics determined by AF4 and the LENP model before and after centrifugation of aggregate samples showed that this common sample preparation step prior to sized exclusion chromatography (SEC) might influence the experimentally observed kinetic mechanism. The results suggest that AF4 is able to provide more reliable kinetic data for aggregates up to and larger than 100 nm that may not be easily characterized by SEC. AF4 provides a wealth of information including analyte size distributions, but this information can be influenced by the inherent dilution that occurs during separations, especially for weakly bound protein aggregates. Protein aggregate stability at each stage of the AF4 analysis was studied. The choice of carrier fluid played a significant role in aggregate stability while the AF4 focusing step did not have significant impact on aggregate populations. Calculations showed that sample dilution is significantly lower in AF4 than in SEC and that dilution occurred primarily at the channel outlet (not during the separation). This suggests
in COLIEE@ICAIL 2019: Legal Information Retrieval using BM25 and BERT

Natural Language Processing (NLP) and Information Retrieval (IR) in the judicial domain is an essential task. With the advent of availability domain-speciﬁc data in electronic form and aid of diﬀerent Artiﬁcial intelligence (AI) technologies, automated language processing becomes more comfortable, and hence it becomes feasible for researchers and developers to provide various automated tools to the legal community to reduce human burden. The Competition on Legal Information Extraction/Entailment (COLIEE-2019) run in association with the International Conference on Artiﬁcial Intelligence and Law (ICAIL)-2019 has come up with few challenging tasks. The shared deﬁned four sub-tasks (i.e. Task1, Task2, Task3 and Task4), which will be able to provide few automated systems to the judicial system. The paper presents our working note on the experiments carried out as a part of our participation in all the sub-tasks deﬁned in this shared task. We make use of diﬀerent Information Retrieval(IR) and deep learning based approaches to tackle these problems. We obtain encouraging results in all these four sub-tasks.
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data

Semantic segmentation of road scenes is one of the key technologies for realizing autonomous driving scene perception, and the effectiveness of deep Convolutional Neural Networks(CNNs) for this task has been demonstrated. State-of-art CNNs for semantic segmentation suffer from excessive computations as well as large-scale training data requirement. Inspired by the ideas of Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge distillation, we propose a new knowledge distillation method for cross-domain knowledge transference and efficient data-insufficient network training, named Spirit Distillation(SD), which allow the student network to mimic the teacher network to extract general features, so that a compact and accurate student network can be trained for real-time semantic segmentation of road scenes. Then, in order to further alleviate the trouble of insufficient data and improve the robustness of the student, an Enhanced Spirit Distillation (ESD) method is proposed, which commits to exploit a more comprehensive general features extraction capability by considering images from both the target and the proximity domains as input. To our knowledge, this paper is a pioneering work on the application of knowledge distillation to few-shot learning. Persuasive experiments conducted on Cityscapes semantic segmentation with the prior knowledge transferred from COCO2017 and KITTI demonstrate that our methods can train a better student network (mIOU and high-precision accuracy boost by 1.4% and 8.2% respectively, with 78.2% segmentation variance) with only 41.8% FLOPs (see Fig. 1).
Applications and challenges of AI-based algorithms in the COVID-19 pandemic

The COVID-19 pandemic is shifting the digital transformation era into high gear. Artificial intelligence (AI) and, in particular, machine learning (ML) and deep learning (DL) are being applied on multiple fronts to overcome the pandemic. However, many obstacles prevent greater implementation of these innovative technologies in the clinical arena. The goal of this narrative review is to provide clinicians and other readers with an introduction to some of the concepts of AI and to describe how ML and DL algorithms are being used to respond to the COVID-19 pandemic. First, we describe the concept of AI and some of the requisites of ML and DL, including performance metrics of commonly used ML models. Next, we review some of the literature relevant to outbreak detection, contact tracing, forecasting an outbreak, detecting COVID-19 disease on medical imaging, prognostication and drug and vaccine development. Finally, we discuss major limitations and challenges pertaining to the implementation of AI to solve the real-world problem of the COVID-19 pandemic. Equipped with a greater understanding of this technology and AI’s limitations, clinicians may overcome challenges preventing more widespread applications in the clinical management of COVID-19 and future pandemics.
Nybomycin Inhibits both Fluoroquinolone-Sensitive and Fluoroquinolone-Resistant Escherichia coli DNA Gyrase

Bacterial type II topoisomerases, DNA gyrase and topoisomerase IV, are targets of many antibiotics, including fluoroquinolones (FQs). Unfortunately, a number of bacterial species easily acquire resistance to FQs by mutations in either DNA gyrase or topoisomerase IV genes. ABSTRACT Bacterial type II topoisomerases, DNA gyrase and topoisomerase IV, are targets of many antibiotics, including fluoroquinolones (FQs). Unfortunately, a number of bacterial species easily acquire resistance to FQs by mutations in either DNA gyrase or topoisomerase IV genes. The emergence of resistant pathogenic strains is a global problem in health care; therefore, identifying alternative pathways to thwart their persistence is the current frontier in drug discovery. Nybomycins are an attractive class of compounds, reported to be “reverse antibiotics” that selectively inhibit growth of some Gram-positive FQ-resistant bacteria by targeting the mutant form of DNA gyrase while being inactive against wild-type strains with FQ-sensitive gyrases. The strong “reverse” effect was demonstrated only for a few Gram-positive organisms resistant to FQs due to the S83L/I mutation in the GyrA subunit of DNA gyrase. However, the activity of nybomycins has not been extensively explored among Gram-negative species. Here, we observed that in a ΔtolC strain of the Gram-negative Escherichia coli with enhanced permeability, wild-type gyrase and a GyrA S83L mutant, resistant to fluoroquinolones, are similarly sensitive to nybomycin.
Diagnostic of Structural Changes in Ferromagnetic Powders during Milling in Beater Mill

Problem of obtaining fine powders of strontium hexa-ferrite is actual because of its wide applications. The paper provides the results of studies of particle size distribution and structural characteristic changes of strontium hexa-ferrite powder (SrFe12O19) during milling in impact mill and after its consequent annealing. Mechanical processing of coarse particulate system was carried out in the mill for 120 minutes without electromagnetic effect and with creation of magneto fluidized bed, formed by perpendicular constant and alternating magnetic field with induction gradient of 210 mT/m, providing reciprocating motion of particles and aggregates with sizes of 3 – 4 mm. It was shown that milling of coarse strontium hexa-ferrite with average particle size 1558.5 μm and the most possible size 1500 μm in magneto fluidized bed allowed to intensify milling process and to provide a significant increase of powder particle sizes uniformity. It was found out, that milling in magneto fluidized bed leads to a great decrease of coherent scattering regions sizes and an increase of lattice micro-deformations and relative dislocation density. Consequent annealing of the powder for 2 hours at 850°C refined structural characteristics significantly. The carried out research allows to choose the optimal milling duration for solution of practical problems of powder metallurgy.
Synthesis and Study of the Properties of Photocatalytic Compositions Based on Sro/Bi2O3 and (Bio)2CO3 Solid Solutions with Modifying Additives of Transition Metals Cu, Mn, Fe

In this work, we studied the effect of doping with d metals (Me: Mn, Fe, Cu) on the photocatalytic activity and optical properties of the hetero-structured, bismuth-containing Sr1-xMexBi4O7 / (BiO)2CO3 photo-catalyst, where x = 0.01–0.05. The stages of oxidative pyrolysis of precursor organometallic complexes with sorbitol by differential scanning calorimetry have been studied. The first stage of oxidative destruction is induced by the influence of nitrate-ion and terminates at temperatures below . The second stage of pyrolysis is associated with a further thermal degradation of sorbitol to pyrolytic carbon in the temperature range 113 - . The third stage 287–462°C is associated with the combustion of the formed pyrolytic carbon. The final stage of pyrolysis occurs in the temperature range of 462-with the formation of the target synthesis products. It has been established that Cu is the most effective dopant. In contrast to other metals, doping with copper results in the formation of a solid solution in the CuO-SrBi4O7 system. Compared to the initial SrBi4O7, as a result of doping Cu in an amount of 5 at. % the largest change in the band gap from 2.77 to 2.62 eV is achieved. The efficiency of photo-catalysis (estimated by the half-conversion of methylene blue) of the Cu0.05Sr0. 95Bi4O7 / (BiO) 2CO3 composition turned out to be 2.3 times higher than the initial - undoped catalyst; and 3.1 times higher than in the non-catalytic process.
Electromyography Feature Analysis to Recognize the Hand Motion in a Prosthetic Hand Design

The increasing need for prosthetic hands for people with disabilities is one reason for innovation in the field of prosthetic hands to create the best prosthetic hand technology. In the design of EMG-based prosthetic hands, this is determined by several things, among others, the selection of features. The selection of the right features will determine the accuracy of the prosthetic hand Therefore, the purpose of this study is to analysis the time domain feature to obtain the best feature in classifying the hand motion. The contribution of this work is able to detect 4 movements in real time, namely hand close, flexion, extension, and relax. The Electromyograph signal is tapped using an electromyograph (EMG) dry electrode sensor in which there is a circuit of EMG instrumentation amplifier. Furthermore, the analog EMG signal data is processed through the ADC (Analog to Digital Converter) by using MCP3008 device. EMG signal data is processed in Raspberry Pi. A feature extraction process is applied to reduce data and determine the characteristics of each hand movement. Feature extraction used is MAV (mean absolute value), SSI (sign slope integral), VAR (variance), and RMS (root mean square). From the results of the four-time domain feature, then the best feature extraction is determined using scatter plot and Euclidean distance. The results that have been carried out on ten people with each person doing ten sets of movements (hand close, flexion, extension, relax), showing the best Euclidean distance results, is the RMS feature, with a value of 2608.07. This data is the result of the best feature extraction analysis through the method of calculating the distance of feature extraction data using Euclidean distance. This analysis of time domain feature is expected to be useful for further experiment in machine learning implementation so that it can be obtained an effective prosthetic hand.
Nano-Modification of Concrete with Slurries Using Ultra-Jet Technology

The article discusses the problem of increasing the strength of concrete through the use of a suspension based on nanomodified additives with boehmite, graphene and carbon nanotubes. The lack of high performance and efficient dispersion methods limits the possibilities for nanomodification of building materials. Therefore, the research topic associated with assessing the possibility of developing means and methods for dispersing hydrocomposites containing nanoparticles is relevant. The suspensions were treated with ultra-jet treatment. Nano-containing suspensions were studied using a Microtrac Bluewave laser particle size analyzer. The analysis results were the average particle sizes in terms of quantitative and volumetric distributions, as well as the minimum recorded particle size in the samples. The results of compressive strength tests of a batch of concrete samples are presented. It is shown that the use of nanomodified additives leads to an increase in concrete strength.
Integration of patient and clinician narratives into systematic reviews: An applied proof of concept

Abstract Health systems currently underutilize systematic reviews. Here, we describe a proof of concept project designed to augment the standard systematic review process by presenting qualitative information as a companion to a review on deprescribing interventions. We conducted a thematic analysis of semi-structured interviews with Veterans Health Administration clinicians and Veterans to describe first-hand experiences of engaging in the deprescribing process. Qualitative findings were incorporated into an interactive, web-based product designed to supplement the systematic review report. Preliminary evaluation suggests that integration of narratives as a companion to systematic reviews is of interest to frontline clinicians, researchers, and health system administrators.
Speech emotion recognition based on listener-dependent emotion perception models

This paper presents a novel speech emotion recognition scheme that leverages the individuality of emotion perception. Most conventional methods simply poll multiple listeners and directly model the majority decision as the perceived emotion. However, emotion perception varies with the listener, which forces the conventional methods with their single models to create complex mixtures of emotion perception criteria. In order to mitigate this problem, we propose a majority-voted emotion recognition framework that constructs listener-dependent (LD) emotion recognition models. The LD model can estimate not only listener-wise perceived emotion, but also majority decision by averaging the outputs of the multiple LD models. Three LD models, fine-tuning, auxiliary input, and sub-layer weighting, are introduced, all of which are inspired by successful domain-adaptation frameworks in various speech processing tasks. Experiments on two emotional speech datasets demonstrate that the proposed approach outperforms the conventional emotion recognition frameworks in not only majority-voted but also listener-wise perceived emotion recognition.
Age, growth and mortality of the striped seabream Lithognathus mormyrus (Linnaeus, 1758) in the Gulf of Tunis (Central Mediterranean Sea)

Abstract Despite the high commercial value of the striped seabream Lithognathus mormyrus (Linnaeus, 1758) in the Gulf of Tunis, biological data on its age, growth and exploitation rate are lacking. With the aim of estimating growth parameters, 516 individuals, ranging from 6–27.5 cm total length and from 3.5–293.5 g total weight, were collected from the artisanal fishing fleet between February 2014 and July 2016. The somatic growth presented a positive allometry and was described by the equation TW = 6.54 10−3TL3.213. The monthly analysis of the marginal increment of the otoliths revealed that only one annulus was deposited per year. The estimated von Bertalanffy growth parameters were: L∞ = 30.18 cm, k = 0.303 year−1 and t0 = −1.42 years. Total and natural instantaneous rates of mortality were respectively Z = 0.784 year−1 and M = 0.698 year−1. Exploitation rate (E = 0.1) showed that the Gulf of Tunis stock of L. mormyrus is not overexploited. The estimated length class giving highest yield (Lopt) was 17.15 ± 1.71 cm.
Impact of the Healing in Harmony program on women's mental health in a rural area in South Kivu province, Democratic Republic of Congo

Abstract Background To assess whether Healing in Harmony (HiH), a form of music therapy, improved women's mental health following conflict-related trauma and sexual violence in the Democratic Republic of Congo. Methods This study used a step-wedged design and included 167 women, who completed up to two pre-tests, a post-test, and up to two follow-up interviews at 3 and 6 months after completing the program. The Hopkins Symptoms Checklist was used to measure anxiety and depression. The Harvard Trauma Questionnaire was used to measure post-traumatic stress disorder (PTSD). Generalized estimating equations with unstructured covariance were used to estimate mean change in mental health scores and relative risks (RRs) for screening positive. Results Prior to starting the HiH program, 73.9, 84.2, and 68.5% screened positive with median scores being 2.20, 2.70, and 2.06 for depression, anxiety, and PTSD, respectively. The RR for screening positive declined significantly (RR = 0.49 for depression, 0.61 for anxiety, and 0.54 for PTSD) and mean scores declined significantly by −0.54, −0.67, and −0.53 points, respectively, from the pre- to the post-test, declines that were sustained at the 3-month and 6-month follow-up interviews. Conclusion The HiH program was associated with significant improvement in women's mental health that was sustained up to 6 months post completion of the program despite instability in the region and evidence of continued experience of conflict-related trauma during the study. These data support the value of providing psychological care in the context of ongoing humanitarian crises.