Whole-Genome Sequencing Reveals Temporal Trends in Antibiotic Resistance Genes in Causing Pediatric Urinary Tract Infections in Central Vietnam.

(1) Background: Pediatric urinary tract infections (UTIs) pose significant challenges due to drug-resistant () strains. This study utilizes whole-genome sequencing to analyze temporal trends in antibiotic resistance genes (ARGs) in clinical isolates from pediatric UTI cases in central Vietnam. (2) Methods: We conducted whole-genome sequencing on 71 isolates collected from pediatric UTI patients between 2018 and 2020.

The Effects of an Oocyte Maturation System and Chlorogenic Acid Supplementation during Embryo Culture on the Development of Porcine Cloned Embryos Derived from Native Vietnamese Ban Pigs.

The aim of this study was to improve the production efficiency of Vietnamese native Ban pig embryos using somatic cell nuclear transfer (SCNT). Fibroblast cells from Ban pigs were injected into the enucleated cytoplasts of crossbred gilts, and the reconstructed embryos were subsequently cultured. In the first experiment, cytoplasts were isolated from oocytes matured in either a defined porcine oocyte medium (POM) or in TCM199 medium supplemented with porcine follicular fluid.

Semi-nested RT-PCR enables sensitive and high-throughput detection of SARS-CoV-2 based on melting analysis.

Background: Asymptomatic transmission was found to be the Achilles' heel of the symptom-based screening strategy, necessitating the implementation of mass testing to efficiently contain the transmission of COVID-19 pandemic. However, the global shortage of molecular reagents and the low throughput of available realtime PCR facilities were major limiting factors.

Methods: A novel semi-nested and heptaplex (7-plex) RT-PCR assay with melting analysis for detection of SARS-CoV-2 RNA has been established for either individual testing or 96-sample pooled testing.

WeDea: A New EEG-Based Framework for Emotion Recognition.

With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiological signals, known as electroencephalography (EEG), have been actively developed and applied to various domains, such as automobiles, robotics, healthcare, and customer-support services. Thus, the demand for acquiring and analyzing EEG signals in real-time is increasing. In this paper, we aimed to acquire a new EEG dataset based on the discrete emotion theory, termed as WeDea (Wireless-based eeg Data for emotion analysis), and propose a new combination for WeDea analysis.

AsEmo: Automatic Approach for EEG-Based Multiple Emotional State Identification.

An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to time as well as spatial-temporal correlations. Therefore, new methods that can clearly distinguish emotional states in EEG data are required.

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure.

Hidden discriminative features extraction for supervised high-order time series modeling.

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step.