Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and Identification of Multidrug-Resistant Klebsiella pneumoniae Clones from the Hospital Environment

Global dispersion, hospital outbreaks, and lineage relationships between emerging antibiotic-resistant strains such as Klebsiella pneumoniae are of public health interest. This study aimed to isolate and identify K. pneumoniae clones from third-level healthcare hospitals in Mexico to establish their multidrug-resistant phenotype, phylogeny, and prevalence. Biological and abiotic surface samples were used to isolate K. pneumoniae strains and to test their antibiotic susceptibility to classify them. The housekeeping genes: gapA, InfB, mdh, pgi, phoE, ropB, and tonB were used for multilocus sequence typing (MLST). Phylogenetic networks were constructed with 48 strains. Isolated strains (93) were mainly from urine and blood, 96% were resistant to ampicillin as expected, 60% were extended-spectrum β-lactamases (ESBL), 98% were susceptible to ertapenem and meropenem and 99% were susceptible to imipenem, 46% were multi-drug resistant (MDR), 17% were extensively-drug resistant (XDR), 1% were pan-drug resistant (PDR), and 36% were not classified. The tonB, mdh, and phoE genes were the most variable, and the InfB gene showed positive selection. The most prevalent sequence types (STs) were ST551 (six clones), ST405 (six clones), ST1088 (four clones), ST25 (four clones), ST392 (three clones), and ST36 (two clones). ST706 was PDR, and ST1088 clones were MDR; neither of these STs has been reported in Mexico. The strains analyzed were from different hospitals and locations; thus, it is important to maintain antibiotic surveillance and avoid clone dissemination to prevent outbreaks, adaptation to antibiotics, and the transmission of antibiotic resistance.
Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth’s Radiant Energy System Instrument on the Terra Satellite. Part II: Validation

Abstract Errors in top-of-atmosphere (TOA) radiative fluxes from the Clouds and the Earth’s Radiant Energy System (CERES) instrument due to uncertainties in radiance-to-flux conversion from CERES Terra angular distribution models (ADMs) are evaluated through a series of consistency tests. These tests show that the overall bias in regional monthly mean shortwave (SW) TOA flux is less than 0.2 W m−2 and the regional RMS error ranges from 0.70 to 1.4 W m−2. In contrast, SW TOA fluxes inferred using theoretical ADMs that assume clouds are plane parallel are overestimated by 3–4 W m−2 and exhibit a strong latitudinal dependence. In the longwave (LW), the bias error ranges from 0.2 to 0.4 W m−2 and regional RMS errors remain smaller than 0.7 W m−2. Global mean albedos derived from ADMs developed during the Earth Radiation Budget Experiment (ERBE) and applied to CERES measurements show a systematic increase with viewing zenith angle of 4%–8%, while albedos from the CERES Terra ADMs show a smaller increase of 1%–.
Isothermal Storage Delays the Senescence of Post-Harvest Apple Fruit through the Regulation of Antioxidant Activity and Energy Metabolism

The purpose of this work was to elucidate the influence of TF (5 ± 5 °C, and 5 ± 1 °C) and CT (5 ± 0.1 °C served as an isothermal state) storage environment on the antioxidant ability and energy metabolism in post-harvest apple fruit during storage. Specifically, compared with fruit in TFs groups, the quality attributes of apples in the CT group, including firmness, fresh weight, contents of SSC, and TA were maintained at a higher level. In addition, fruit stored in the CT environment revealed a suppressed respiration rate and EL, lower MDA, O2·−, and H2O2 accumulation but increased the activities of SOD, CAT, APX, and GR. At the end of storage, the SOD, CAT, APX, and GR activities of fruit in the CT group were 38.14%,48.04%, 115.29%, and 34.85% higher than that of the TF5 group, respectively. Fruit in the CT environment also revealed higher AsA, GSH, total phenols, and total flavonoid content. In addition, fruit stored in the CT environment maintained higher ATP content, EC, and more active H+-ATPase, Ca2+-ATPase, CCO, and SDH. At the end of storage, the SDH and CCO activities of fruit in the TF0.1 group were 1.74, and 2.59 times higher than that in the TF5 group, respectively. Taken together, we attributed the fact that a constant temperature storage environment can retard the fruit senescence to the enhancement of antioxidant capacities and maintaining of higher energy status in apple fruit.
Stochastic Path Perturbation Approach Applied to Non-Local Non-Linear Equations in Population Dynamics

We described the first passage time distribution associated to the stochastic evolution from an unstable uniform state to a patterned one (attractor of the system), when the time evolution is given by an integro-differential equation describing a population model. In order to obtain analytical results we used the Stochastic Path Perturbation Approach introducing a minimum coupling approximation into the nonlinear dynamics, and a stochastic multiscale perturbation expansion. We show that the stochastic multiscale perturbation is a necessary tool to handle other problems like: nonlinear instabilities and multiplicative stochastic partial differential equations. A small noise parameter was introduced to define the random escape of the stochastic field. We carried out Monte Carlo simulations in a non-local Fisher like equation, to show the agreement with our theoretical predictions.
Polyethylene Glycol-Mediated Directional Conjugation of Biological Molecules for Enhanced Immunoassays at the Point-of-Care

Rapid and reliable point-of-care (POC) diagnostic tests can make a significant impact on global health. One of the most common approaches for developing POC systems is the use of target-specific biomolecules. However, the conjugation of biomolecules can result in decreased activity, which may compromise the analytical performance and accuracy of the developed systems. Here, we present a polymer-based cross-linking protocol for controlled and directed conjugation of biological molecules. Our protocol utilizes a bifunctional thiol-polyethylene glycol (PEG)-hydrazide polymer to enable site-directed conjugation of IgG antibodies to the surface of screen-printed metal electrodes. The metal surface of the electrodes is first modified with thiolated PEG molecules, leaving the hydrazide groups available to react with the aldehyde group in Fc fragments of the oxidized IgG antibodies. Using Klebsiella pneumoniae carbapenemase-2 (KPC2) antibody as a model antibody used for antimicrobial resistance (AMR) testing, our results demonstrate a ~10-fold increase in antibody coupling, compared with the standard N-hydroxysuccinimide (NHS)-based conjugation chemistry, and effective capture (> 94%) of the target KPC2 enzyme antigen on the surface of modified electrodes. This straightforward and easy-to-perform strategy of site-directed antibody conjugation can be engineered for coupling other protein and non-protein-based biological molecules commonly used in POC testing and development, thus enhancing the potential for improved diagnostic accuracy and performance.
GaN-based power high-electron-mobility transistors on Si substrates: from materials to devices

Conventional silicon (Si)-based power devices face physical limitations—such as switching speed and energy efficiency—which can make it difficult to meet the increasing demand for high-power, low-loss, and fast-switching-frequency power devices in power electronic converter systems. Gallium nitride (GaN) is an excellent candidate for next-generation power devices, capable of improving the conversion efficiency of power systems owing to its wide band gap, high mobility, and high electric breakdown field. Apart from their cost effectiveness, GaN-based power high-electron-mobility transistors (HEMTs) on Si substrates exhibit excellent properties—such as low ON-resistance and fast switching—and are used primarily in power electronic applications in the fields of consumer electronics, new energy vehicles, and rail transit, amongst others. During the past decade, GaN-on-Si power HEMTs have made major breakthroughs in the development of GaN-based materials and device fabrication. However, the fabrication of GaN-based HEMTs on Si substrates faces various problems—for example, large lattice and thermal mismatches, as well as ‘melt-back etching’ at high temperatures between GaN and Si, and buffer/surface trapping induced leakage current and current collapse. These problems can lead to difficulties in both material growth and device fabrication. In this review, we focused on the current status and progress of GaN-on-Si power HEMTs in terms of both materials and devices. For the materials, we discuss the epitaxial growth of both a complete multilayer HEMT structure, and each functional layer of a HEMT structure on a Si substrate. For the devices, breakthroughs in critical fabrication technology and the related performances of GaN-based power HEMTs are discussed, and the latest development in GaN-based HEMTs are summarised. Based on recent progress, we speculate on the prospects for further development of GaN-based power HEMTs on Si. This review provides a comprehensive understanding of GaN-based HEMTs on Si, aiming to highlight its development in the fields of microelectronics and integrated circuit technology.
Reservoir Computing in Epidemiological Forecasting: Predicting Chicken Pox Incidence

We examine the applicability of time-series forecasting techniques to model and predict chickenpox incidence rates using publicly available epidemiological data from Rozemberczki et al. [1]. Analyzing data across both time and location is crucial in understanding disease dynamics, allowing for the identification of patterns such as temporal clustering, detection of high-incidence areas, characterization of disease spread, measurement of temporal synchrony, and forecasting future incidence rates. The primary objective of this study is to evaluate the effectiveness of neural networks in addressing this problem. Reservoir Computing, ARIMA, and various types of Recurrent Neural Networks (RNNs) have demonstrated success in tackling complex time-series issues. We assess several models based on different RNN architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BLSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (BGRU), and compare their performance. We use a deep learning approach based on Reservoir Computing to predict chickenpox counts based on past incidence rates. We implement all the aforementioned neural network architectures for fore- casting chickenpox incidence rates and compare their prediction accuracy. Our results indicate that Reservoir Computing prediction models outperform all other models trained on the same data. Furthermore, we demonstrate that Reservoir Computing prediction models are more efficient and quicker to train and deploy in epidemiology.
Estimating the fraction of ‘non‐genuine’ artwork by Henry Moore for sale on eBay: application of latent class screening test methodology

Summary. Although the Internet has provided consumers with new ways to purchase goods, it also has allowed less scrupulous businesses and individuals to offer poor quality or mislabelled items. In particular, the operators of Internet auction sites may not be required to guarantee the quality or genuineness of the items listed. A latent class approach that was originally developed to estimate the accuracy of medical tests and the prevalence of an infection is adapted to provide estimates of the prevalence of ‘questionable’ artwork offered on eBay. The method requires the existence of two subpopulations with a different prevalence of the trait and each subject is classified by two different tests. As Henry Moore produced less expensive lithographs and etchings, in addition to small sculptures and original drawings, two subpopulations of artwork were available. Two knowledgeable individuals independently assessed all the works by Moore offered on eBay for a period of 18 months. After discussing the issue of non‐genuine versions of Moore's art with experts on Henry Moore, a Bayesian approach was adopted. The method estimates that about 90% of the drawings and small sculptures that are listed on eBay are not genuine whereas most, about 90–95%, of signed prints are genuine. Because the methodology does not require a ‘gold standard’ it can be adopted by consumer protection agencies or producers of expensive items to monitor the authenticity of those objects offered to the public on Internet auction sites.
Crystallization behavior of polyvinylidene fluoride (PVDF) in NMP/DMF solvents: a molecular dynamics study

In this study, the crystallization behavior of polyvinylidene fluoride (PVDF) in NMP/DMF solvent at 9 to 67 weight percent (wt%) was analyzed by molecular dynamics (MD) simulation. The PVDF phase did not gradually change with the incremental increase in PVDF wt%, but displayed rapid shifts at 34 and 50 wt% in both solvents. The solvation behavior between the two solvents was quite identical from the similar radial distribution functions. However, PVDFs in DMF solvent showed a higher ratio of β phase crystalline structures than those in NMP solvent. It was found that DMF solvents were more tightly packed near trans state PVDF fluorine compared to NMP solvents. Also, NMP oxygen atoms interacted more favorably with gauche state PVDF hydrogen atoms over DMF oxygen atoms. The evaluation of properties observed in atomic scale interactions, such as trans state inhibition and gauche state preference, can be used as indicators in future solvent research.
The experience of Saudi respiratory therapists dealing with COVID-19 patients: A qualitative study

BACKGROUND: Healthcare professionals have fought hard to restrain the COVID-19 pandemic by providing high-quality care for their infected patients, but in doing so they have developed fears of becoming sick and feelings of isolation and loneliness. The lived experience of respiratory therapists (RTs) in Saudi Arabia who works with these infected patients needs further investigation. The study sought to describe the experiences and coping strategies of Saudi RT managing patients with COVID-19. MATERIALS AND METHODS: The study utilized qualitative research methods, specifically employing a phenomenological research design. A total of 25 Saudi RT (RTs) who were in direct contact with COVID-19 patients were selected after they agreed to participate in this study. The study followed a one-on-one semi-structured interview process using the Zoom platform. This qualitative data collection technique focuses on the participants' lived experiences and feelings to discover shared patterns. The data were analyzed via an inductive approach. RESULTS: Six themes were found in the RT perceptions including stress while treating COVID patients, managing the fear of catching of Covid 19, feelings towards COVID-19 patients, challenges faced by female RTs, workplace experiences, and excessive workload. CONCLUSIONS: RTs feelings dramatically changed throughout the COVID-19 pandemic. All the RTs have developed a self-copying style that has helped them improve their psychosocial behavior to face the pandemic. During the outbreak, frontline RTs' positive and negative emotions intertwined and coexisted. Negative emotions predominated in the beginning, while good feelings emerged gradually. Self-coping methods and psychosocial development were significant factors in RTs mental health while caring for COVID-19 patients.
Trajectory Tracking of Parafoil Using FDO Based Nonlinear Predictive Control

According to the analysis of parafoil homing trajectory,the mathematic description of the trajectory tracking control problem was formulated based on the state space representation of parafoil system.Considering that the parafoil is subject to greater external disturbance and uncertainty when it is in homing flight,the nonlinear predictive control based on Fuzzy Disturbance Observer(FDO) was derived and the parafoil homing system trajectory tracking control law was built up.The simulation result indicates the good robust performance and anti-interference performance of the flight control system.
Using education based on the health belief model to modifications in performance of behaviors related to sexually transmitted infections in vulnerable women

BACKGROUND: Sexually transmitted infections (STIs) have always been considered as a health problem in the world. Also, vulnerable women (addicts, jail breakers, and prostitutes) are at the highest risk. According to the World Health Organization's (WHO) approach, the only effective way to prevent and control this disease is public health education, and high-risk and vulnerable groups should be prioritized in educational programs. The aim of the study was to investigate the effect of health belief model (HBM)-based education on changing the behaviors related to STIs in vulnerable women. MATERIALS AND METHODS: The present study is an intervention (field trial) study in vulnerable women. Convenience sampling method was used in this study and the sample size was determined to be 84 subjects. Through tossing a coin, the social support center was selected as the intervention group and the drop-in center as the control group. Quantitative data analysis was performed, and descriptive and inferential statistical methods were used. RESULTS: The mean scores of perceived threat, perceived benefits, perceived barriers, and perceived self-efficacy and changes in performance between the three measurement stages, as well as changes in the scores of perceived threat, perceived benefits, perceived barriers, and perceived self-efficacy over three measurement times were significantly different between the two groups (interaction) (P < 0.001). The mean score of performance 3 months after the intervention was significantly higher than before the intervention (P = 0.001). CONCLUSIONS: The present study confirmed the effectiveness of the HBM in promoting behavioral modifiers that lead to STIs. Therefore, educational interventions with emphasis on understanding the threats, benefits, barriers, and self-efficacy and, ultimately, performance improvement in relation to STIs are recommended.
Novel Selective Support Vector Machine Ensemble Learning Algorithm

Focusing on the problem that conventional ensemble learning methods may be invalid when support vector machine(SVM) is used as component learner,a new selective SVM ensemble algorithm is proposed. ξα estimator is used to estimate the generalization performance of the component SVM,and negative learning theory is used to introduce diversity among component SVMs.A set of component SVMs with high generalization performance and high diversity is selected during ensemble through recursive elimination algorithm.Experimental results on UCI data sets show that compared with single SVM,conventional Bagging ensemble method and negative learning ensemble method,the classification accuracy of selective SVM ensemble is increased on average by 0.4%,0.24% and 0.16%,respectively.
Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions

Parameter estimation and statistical inference are challenging problems for stochastic volatility (SV) models, especially those driven by pure jump Lévy processes. Maximum likelihood estimation (MLE) is usually preferred when a parametric statistical model is correctly specified, but traditional MLE implementation for SV models is computationally infeasible due to high dimensionality of the integral involved. To overcome this difficulty, we propose a gradient-based simulated MLE method under the hidden Markov structure for SV models, which covers those driven by pure jump Lévy processes. Gradient estimation using characteristic functions and sequential Monte Carlo in the simulation of the hidden states are implemented. Numerical experiments illustrate the efficiency of the proposed method.
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data

Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via standard log-likelihood training, perform poorly as an outlier score. We propose to use an unlabelled auxiliary dataset and a probabilistic outlier score for outlier detection. We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing flow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the contrastive dataset. We show that this is equivalent to learning the normalized positive difference between the in-distribution and the contrastive feature density. We conduct experiments on benchmark datasets and compare to the likelihood, the likelihood ratio and state-of-the-art anomaly detection methods.
Research of clustering algorithms in protein-protein interaction network

Protein-protein interaction network refers a new research area of computer science.The distance metric in such setting is redefined by the network distance,which has to be computed by the expensive shortest path distance over the network.The existing methods are not applicable to such cases.Therefore,by exploiting unique features of networks,a new clustering algorithm is presented,which uses the information of nodes and edges in the network to prune the search space and avoid some unnecessary distance computations.The experimental results indicate that the algorithm achieve high efficiency for clustering nodes in real protein-protein interaction network.
Generation of cold polyatomic molecular ions by ion-atom collisions

atomic molecular ions as reaction products remains limited. Thus, we studied ion-atom reaction collisions in a continuous-wave (CW) laser photoionization of cold atoms in an Rb-Rb + hybrid trap. A series of polyatomic molecular ions was produced, with precise changes in the atomic number of one rubidium atom. Using resonant-excitation mass spectrometry, we directly observed Rb +3 and Rb +4 molecular ions in time-of-ﬂight mass spectrum for the ﬁrst time. The information of the quantum state of these polyatomic molecular ions and the inﬂuence factors was obtained by measuring their lifetimes. The approach is simple, robust, and suitable for all types of laser-coolable elements. Our work paves the way for ultracold ion-atom chemical reactions, introduces the con-cept of polyatomic molecular ion platforms, and deepens the understanding of ion-atom reaction collisions. It has important implications for astronomical sciences, ultracold neutral plasma, cluster physics, and other disciplines.