Miracle-dz/UniFAR-FTUs huggingface dataset:
Staphylococcus aureus and S. epidermidis in biological systems of hospital environment: Antibiotic resistance patterns in regions of Ukraine

Staphylococcus bacteria are ubiquitous and often circulate in the biological systems of the hospital environment. Staphylococci have developed antibiotic resistance mechanisms resulting in a significant medical and economic burden to the healthcare system. The goal of our research was to conduct a comparative analysis of resistance to antibiotics in S. aureus and S. epidermidis isolates found in surgical hospitals in Kharkiv and Poltava regions. In 2013 through 2019, 151,015 and 98,754 tests were made by disc-diffusion method to identify the sensitivity in the S. aureus strains to antibiotics in Kharkiv and Poltava regions respectively. In 2013–2015, 15,589 tests were made in Kharkiv region to identify antibiotics sensitivity in S. epidermidis strains. Comparison of antibiotic resistance of the S. aureus strains in Kharkiv and Poltava regions was performed using the Pearson Chi-square test (χ2) and Fisher’s exact test. The proportion of S. aureus strains resistant to penicillins, cephalosporins, carbapenems, aminoglycosides, and macrolides was higher in Kharkiv region in terms of statistical validity than in Poltava region. Overall, the proportion of S. aureus strains resistant to lincozamids, tetracycline antibiotics, and fluoroquinolones in Poltava region was higher in terms of statistical validity than in Kharkiv region. An analysis of resistance of S. aureus strains to linezolid demonstrated that in Poltava region the proportion of resistant microorganisms was higher in terms of statistical validity in 2013–2014 and in 2016–2018. In Kharkiv region, in 2013 and in 2014, 96.3% and 89.1% of isolated strains of S. aureus respectively, were resistant to vancomycin. In 2019, more than a quarter of the located isolates (26.6%) in Poltava region were resistant to this antibiotic. The analysis of the dynamic of resistance in S. epidermidis isolates demonstrated that in 2015 nearly half of the isolates located in Kharkiv region were insensitive to penicillin antibiotics. Between 2013 and 2015, the spread of resistance to cephalosporins, aminoglycosides, macrolides, and fluoroquinolones among the S. epidermidis isolates noticeably increased. When S. epidermidis resistance to vancomycin was analyzed, a decrease in the proportion of resistant strains from 88.0% in 2013 to 8.7% in 2015 was noted. A promising direction for further research is the creation of passports of microorganism resistance in the regions and various health-care settings, as well as the creation of a unified national database network on microorganism resistance using modern methodologies for determining the phenotypes and genotypes of microorganisms.
Dyson line and modified Dyson line in the EPR measurements

Abstract The difficulty in determining the electron paramagnetic resonance (EPR) line parameters of ferromagnetic semiconductors has been addressed. For these materials, the resonance line is very broad and lies at low resonance field, so that only a part of the line can be detected experimentally. Moreover, the line is of asymmetric (Dysonian) shape as described by the line shape parameter α. We have compared values of line parameters derived by computer fitting of the whole experimental EPR line to the Dyson function (or modified Dyson function) with the values obtained by applying this procedure to the left and the right half of the line.
Estimating the AUC with a graphical lasso method for high-dimensional biomarkers with LOD

This manuscript estimates the area under the receiver operating characteristic curve (AUC) of combined biomarkers in a high-dimensional setting. We propose a penalization approach to the inference of precision matrices in the presence of the limit of detection. A new version of expectation-maximization algorithm is then proposed for the penalized likelihood, with the use of numerical integration and the graphical lasso method. The estimated precision matrix is then applied to the inference of AUCs. The proposed method outperforms the existing methods in numerical studies. We apply the proposed method to a data set of brain tumor study. The results show a higher accuracy on the estimation of AUC compared with the existing methods.
Visuomotor Transformation with a P300 Brain-Computer Interface Combined with Robotics and Virtual Reality: A Device for Post-Stroke Rehabilitation

Brain-computer interfaces (BCIs) are a promising approach to post-stroke rehabilitation. While such treatments have relied mostly on motor imagery, therapies are needed which engage visuomotor transformations. We hypothesized that a P300-based BCI, where flickering stimuli represent motor targets, could provide an effective therapy. To this end, we engineered a rehabilitation system composed of a virtual reality setup, a P300-BCI for decoding targets from EEG, and a robot for moving the stroke-affected arm. This system operates reliably in healthy subjects and stroke patients. Evidence of clinical benefits has been found. Three groups of patients were examined: the BCI group (N=8) where the BCI triggered robotic-driven arm movements, the NoBCI group (N=7) with the same task instructions but no BCI, and the Control group (N=7) without training. On FMA-UE scale, neurological improvements were 1.4±1.6, 23.1±11.8, and 27.6±13.7 points in the Control, NoBCI and BCI groups, respectively; on ARAT scale they were 0.0±0.0, 14.7±20.6, and 20.6±19.3. Our results support the idea that decoding cortical responses to visual targets and using this information to control an orthotic robot could aid post-stroke rehabilitation. We suggest that this method facilitates a visuomotor transformation that normally engages multiple cortical areas and need to be repaired by neuroplasticity.
A Current-Sharing Compensation Method for High-Power-Medium-Frequency Coils Composed of Multiple Branches Connected in Parallel

In inductive power transfer (IPT) systems, the coils with paralleled multiple branches are commonly used in high power applications where the frequency range is between 20 and 90 kHz. The traditional compensation method takes multiple branches as a whole and the compensation capacitors are connected outside the coils, which suffers from current unbalance among multiple branches, but currently few researchers analyze it. Therefore, this article illustrates the current-unbalance mechanism and proposes a current-sharing compensation method for IPT coils. The current-unbalance characteristics of coils with paralleled multiple branches are illustrated to prove the current-sharing necessity. Then, based on the relation between the impedance of branch and current unbalance, a compensation capacitor with a specially designed value is connected in each branch, so that the current unbalance can be suppressed by the increased branch impedance. Besides, the cost, volume, and weight of IPT systems remain unchanged approximately. Finally, a 30-kW IPT prototype is built up to verify the availability of the proposed method, and the currents and temperatures of multiple branches are equally distributed within wide power range with this method.
WeatherBench Probability: Medium-range weather forecasts with probabilistic machine learning methods.

<p>Because the atmosphere is inherently chaotic, probabilistic weather forecasts are crucial to provide reliable information. In this work, we present an extension to the WeatherBench, a benchmark dataset for medium-range, data-driven weather prediction, which was originally designed for deterministic forecasts. We add a set of commonly used probabilistic verification metrics: the spread-skill ratio, the continuous ranked probability score (CRPS) and rank histograms. Further, we compute baseline scores from the operational IFS ensemble forecast. &#160;</p><p>Then, we compare three different methods of creating probabilistic neural network forecasts: first, using Monte-Carlo dropout during inference with a range of dropout rates; second, parametric forecasts, which optimize for the CRPS; and third, categorical forecasts, in which the probability of occurrence for specific bins is predicted. We show that plain Monto-Carlo dropout does not provide enough spread. The parametric and categorical networks, on the other hand, provide reliable forecasts, with the categorical method being more versatile. </p>
Highly efficient dual-plasmon polymer solar cell incorporating Au@SiO2 core–shell nanorods and Ag nanoparticles

Generally, metallic nanoparticles (NPs) are widely employed in polymer solar cells (PSCs) to enhance power conversion efficiency (PCE) via the localized surface plasmon resonance (LSPR) effect. Herein, a significant performance enhancement is demonstrated in inverted PSCs by incorporating two kinds of metallic NPs on the rear side of photoactive layer. The Au nanorods (NRs) capped with ∼2 nm ultrathin SiO2 shells, with plasmonic absorption peaks of 520 nm for the transverse axis and 615 nm for the longitudinal axis (in deionized water), and 1 nm-ultrathin Ag NPs with an absorption peak of 517 nm, are in sequence spin-coated on a photoactive layer and thermally evaporated onto MoO3. To the best of our knowledge, this is the first time that Au NRs are directly spin-coated on a photoactive layer. The dual-plasmon device exhibits an improved short-circuit current density (JSC) of 10.36 mA cm−2 and an increased fill factor (FF) of 0.64, generating a PCE of 4.11% with a ∼30% enhancement factor compared with that of the reference device. The enhancement is attributed to the light absorption improvement in the photoactive layer over a wide wavelength range of 500–650 nm, induced by the LSPR of Au@SiO2 NRs and Ag NPs. In addition to the excitation of the LSPR effect, the incorporation of Ag NPs into MoO3 also contributes to the increase in FF value through enhancing the electric conductivity of MoO3.
Water Sorption Kinetics of Gluten-added Wheat Noodle

Noodles were prepared using wheat flour with 0 _ 20% (w/w) added powdered gluten, and the transient changes in their moisture content during cooking were measured. Kinetic analysis of the water sorption process during cooking indicated that the process occurred in two distinct stages for all of the noodles. Water diffusion and consequent starch gelatinization predominantly controlled water sorption during the early stage, while relaxation of the gluten network structure was rate-limiting for the water sorption process during the latter stage. The moisture content, at which the rate-limiting factor for the water sorption process changed, was lower for the wheat noodles having a higher gluten content. Thus, it was suggested that the change in the gluten network structure induced by the addition of gluten to wheat flour affected the water sorption behavior of wheat noodles.
2003-2019: explosive spread of enterobacteria producing extended-spectrum beta-lactamases in Bangui Central African Republic

Introduction the spread of enterobacteria producing extended-broad-spectrum beta-lactamases (ESBL) is a global public health-problem. In a study carried in 2003-2005 at the Pasteur Institute in Bangui, 450 enterobacteria were identified in clinical isolates, of which 17 were ESBL (prevalence: 3.78%). The aim of this study was to update this data. Methods from May 2018 to April 2019, a total of 941 enterobacteria were isolated and identified under identical conditions of recruitment and with the same techniques used in the previous study: phenotypic identification using Api 20E strips (bioMérieux SA, Marcy-l'Etoile, France) and antimicrobial drug susceptibility using the disk diffusion method (Bio-Rad antibiotic discs, Marnes la Coquette, France). Resistance genes were identified by polymerase chain reaction (PCR) and sequencing. Results from May 2018 to April 2019, a total of 941 enterobacteria were isolated of which 478 were ESBL, thus amounting to a prevalence of 50.80%. The genetic profiles of the bla CTX-M resistance genes exhibited the emergence of the CTX-M28 variant (CTX-M1 group) and variants of the M2 and M9 groups. There was also a notable increase, from 35 to 64%, in the ESBL with a bla SHV gene. Conclusion this study documents a 13 fold increase in the prevalence of ESBL derived from clinical isolates of the bacteriology laboratory of the Institute Pasteur in Bangui, by comparing its data with that of the publication by Frank et al. 2006. Together with this increase a significant diversification of the circulating CTX-M resistance genes was noticed.
On the role of the Antarctic Slope Front on the occurrence of theWeddell Sea polynya under climate change

<p>This study investigates the occurrence of the Weddell Sea Polynya under an idealized</p><p>climate change scenario by evaluating simulations from climate models of different</p><p>ocean resolutions. The GFDL-CM2.6 climate model, with roughly 3.8 km</p><p>horizontal ocean grid spacing in the high latitudes, forms a Weddell Sea Polynya at</p><p>similar time and duration under idealized climate change forcing as under pre-industrial</p><p>forcing. In contrast, all convective models forming the fifth phase of the Coupled Model</p><p>Intercomparison Project (CMIP5) show either a cessation or a slowdown of Weddell</p><p>Sea Polynya events under climate warming. The representation of the Antarctic Slope</p><p>Current and related Antarctic Slope Front is found to be key in explaining the</p><p>differences between the two categories of models, with these features being more</p><p>realistic in CM2.6 than in CMIP5. In CM2.6, the freshwater input driven by sea ice melt</p><p>and enhanced runoff found under climate warming largely remains on the shelf region</p><p>since the slope front restricts the lateral spread of the freshwater. In contrast, for most</p><p>CMIP5 models, open ocean stratification is enhanced by freshening since the absence</p><p>of a slope front allows coastal freshwater anomalies to spread into the open ocean. </p><p>This enhanced freshening contributes to the slow down the occurrence of Weddell Sea</p><p>Polynyas. Hence, an improved representation of Weddell Sea shelf processes in</p><p>current climate models is desirable to increase our ability to predict the fate of the</p><p>Weddell Sea Polynyas under climate change. </p>
The Benefits and Risks of Probiotic, Prebiotic and Symbiotic interventions in the Care of patients with Diabetes Mellitus

Introduction: Probiotics, prebiotics and synbiotics are thought to affect the pathophysiology of diabetes mellitus including gut dysbiosis, intestinal barrier permeability and modulator of gut-brain axis and oxidative stress. This systematic review examined if their interventions resulted in improved clinical outcomes and were safe to administer. Methods: An electronic search was conducted in August 2020 of CINAHL, EMBASE, MEDLINE, and PUBMED databases as well as using Google Scholar using keyword searches combined in a formal search strategy. The studies extracted were then filtered through an inclusion and exclusion criteria and assessed for risk of bias. Results: Twenty-four studies met the inclusion criteria, with 20 studies involving participants with type 2 diabetes, 1 study a mixed cohort of type 1 and 2, and 3 involving prediabetes participants. Meta-analysis was not appropriate due to the heterogeneity in populations, methods and presented results. One trial was limited due to unclear risk of bias and was excluded. Four key themes were identified across the studies: improvements to glycaemic control; improvements in oxidative stress, inflammation and gut permeability; lipid profile, anthropometric parameters and blood pressure; and adverse events and tolerability. Conclusions: Probiotics improved glycaemic control, oxidative stress, inflammation and gut permeability and lipid profile in T2DM participants. There was no evidence of improvements to T1DM due to lack of studies and insufficient studies on pre-diabetes. Synbiotics are also promising but prebiotics have insufficient evidence.
An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

In recent years, deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields. In the diagnosis of sucker-rod pump working conditions, due to the lack of a large-scale dynamometer card data set, the advantages of a deep convolutional neural network are not well reflected, and its application is limited. Therefore, this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning, which is used to solve the problem of too few samples in a dynamometer card data set. Based on the dynamometer cards measured in oilfields, image classification and preprocessing are conducted, and a dynamometer card data set including 10 typical working conditions is created. On this basis, using a trained deep convolutional neural network learning model, model training and parameter optimization are conducted, and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards. The experimental results show that transfer learning is feasible, and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network. The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.
Novel Metasurface Lens-Based RF Sensor Structure for SAR Microwave Imaging of Layered Media

In this article, a novel low profile wideband highly directive gradient refractive index (GRIN) metasurface (MS) lens is designed to improve the gain of the X-band waveguide-based RF sensor system for the microwave imaging application. The proposed GRIN MS lens consists of several layers comprising the core layers at the centre and the impedance matching layers around both sides of the core layers. The core layers and the impedance matching layers of the proposed lens are based on a novel three-layer unit cell, wherein a single rectangular ring and two C-shaped strips are printed on both sides of the middle layer. The cross-sectional dimensions of the proposed unit cell varying across the lens aperture to realize the refractive index profile gradually varying from 1.5 at both edges to 7 at the centre. After designing the GRIN MS lens, an attractive microwave technique using a combination of piecewise synthetic aperture radar (PW-SAR) scheme and a time-domain algorithm for imaging of layered media is proposed. The use of a noniterative time-domain technique in the present situation basically helps to retrieve the electrical and physical properties of each layer, which are inherently required for the SAR algorithm to obtain a focused image of the target object buried in the layered media. The experimental results show that the integration of the proposed GRIN MS lens with the RF waveguide sensor improves the overall gain by the maximum value of 10.5 dB, thereby enhancing the spatial resolution of the target object in a layered media using the modified SAR imaging scheme.
Admissibility and Design Issues for T-S Fuzzy Descriptor Systems With Perturbed Derivative Matrices in the Rules

This article mainly investigates the admissibility analysis and the parallel distributed compensator (PDC) for fuzzy descriptor systems with perturbed derivative matrices in the rules. The standard Takagi–Sugeno (T–S) fuzzy model is generalized into a fuzzy descriptor model with the perturbed derivative matrices existing in each rule, which can be used to represent a larger class of nonlinear and/or uncertain systems. Based on linear matrix inequalities (LMIs) technique, a new admissibility analysis condition for the considered fuzzy descriptor system is first presented, where the new results involve slack matrices for lessening the conservatism. Furthermore, we further cope with the fuzzy PDC synthesis associated with admissibility and ρ-admissibility assurance for the resulting closed-loop fuzzy descriptor systems. Since all the proposed criteria can be explicitly formulated in terms of LMIs or parametric LMIs, we can handily verify them via current LMI solvers. Finally, illustrative examples are given to demonstrate that the proposed approach is valid and effective.
The comparison of limma and DESeq2 in gene analysis

Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. With the development of techniques, many methods to analyze the differentially expressed (DE) genes have emerged, especially the downstream analysis approaches, such as limma, DESeq2, and edgeR. However, it is unclear whether using different methods leads to different results. This article has compared the results gained from DESeq2 and limma when conducting downstream analysis for RNA sequencing data. Evidently, the number of genes they found is different from each other. DESeq2 found more genes than limma. But more than 90% of the genes detected by the two methods are overlapped, which means both methods are reliable. If precise results are needed, limma has a better ability to find the accurate DE genes. In the end, we analyzed the reason of the difference and summarized when it is better to use limma than DESeq2.
Active learning of optimal controls for pump scheduling optimization

Two approaches are possible in Pump Scheduling Optimization (PSO): explicit and implicit control. The first assumes that decision variables are pump statuses/speeds to be set up at prefixed time. Thus, the problem is to efficiently search among all the possible schedules (i.e., configurations of the decision variables) to optimize the objective function – typically minimization of the energy-related costs – while satisfying hydraulic feasibility. Since both the energy cost and the hydraulic feasibility are black-box, the problem is usually addressed through simulationoptimization, where every schedule is simulated on a “virtual twin” of the real-world water distribution network. A plethora of methods have been proposed such as meta-heuristics, evolutionary and nature-inspired algorithms. However, addressing PSO via explicit control can imply many decision variables for real-world water distribution networks, increasing with the number of pumps and time intervals for actuating the control, requiring a huge number of simulations to obtain a good schedule.