Newly detected paediatric melioidosis cases in a single referral children's hospital in Ho Chi Minh City indicate the probable underrecognition of melioidosis in South Vietnam.

BACKGROUND: The epidemiology of melioidosis in Vietnam, a disease caused by the soil bacterium Burkholderia pseudomallei, remains unclear. This study aimed to detect paediatric melioidosis in South Vietnam and describe clinical features and the geographic distribution. METHODS: We introduced a simple laboratory algorithm for detecting B. pseudomallei from clinical samples at Children's Hospital 2 in Ho Chi Minh City in July 2015. A retrospective observational study of children <16 y of age with culture-confirmed melioidosis between July 2015 and August 2019 was undertaken. RESULTS: Thirty-five paediatric cases of melioidosis were detected, with cases originating from 13 of 32 provinces and cities in South Vietnam. The number of paediatric melioidosis cases detected from a certain region correlated with the overall number of inpatients originating from the respective geographic area. Suppurative parotitis (n=15 [42.8%]) was the most common clinical presentation, followed by lung infection (n=10 [28.6%]) and septicaemia (n=7 [20%]). Fourteen (40%) children had disseminated disease, including all cases of lung infection, four cases with central nervous system symptoms and four (11.4%) deaths. CONCLUSIONS: The patients' origin indicates a wide distribution of melioidosis in South Vietnam. It seems probable that cases not only in children, but also in adults, remain grossly undiagnosed. Further awareness raising and laboratory capacity strengthening are needed in this part of the country.

PLI-Based Connectivity in Resting-EEG is a Robust and Generalizable Feature for Detecting MCI and AD: A Validation on a Diverse Multisite Clinical Dataset.

The high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings. The current study aims to reevaluate the effectiveness of EEG-based machine learning model for the detection of AD/MCI in the case of a relatively large and diverse data set. We collected resting-state EEG data from 150 participants across six hospitals and examined the classification performances of Linear Discriminative Analysis (LDA) classifiers on the phase lag index (PLI) feature. We also compared the performance of PLI over the other commonly-used EEG features and other classifiers. The model was first tested on a training set to select the feature subset and then further validated with an independent test set. The results demonstrate that PLI performs the best compared to other features. The LDA classifier trained with the optimal PLI features can provide 82.50% leave-one-participant-out cross-validation (LOPO-CV) accuracy on the training set and maintain a good enough performance with 75.00% accuracy on the test set. Our results suggest that PLI-based functional connectivity could be considered as a reliable bio-maker to detect AD/MCI in the real-world clinical settings.

Geographically distributed data management to support large-scale data analysis.

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers.

Development of taste-masking microcapsules containing azithromycin by fluid bed coating for powder for suspension and in vivo evaluation.

This research aims to develop bitter taste-masking microcapsules containing azithromycin (AZI) by a simpler and familiar method, fluid-bed coating technology, in comparison with Zithromax®. Cores of microcapsules, AZI microparticles, were prepared by fluid-bed granulation, then taste-masking polymer was covered on by fluid-bed coating technique. Eudragit L100, Eudragit RL100, and ethyl cellulose in single and combined with Eudragit L100 and Eudragit E100 were used as taste-masking polymers. The obtained microcapsules were characterised by taste-masking ability, in vitro release, SEM, coating thickness, and coating efficiency. Combination of ethyl cellulose and Eudragit E100 (3:1) in coating thickness of 45.13 ± 2.12% w/w prevents AZI release from microcapsules below bitter taste threshold (1.78 ± 1.17 µg/ml). Bioavailability of powders containing AZI microcapsules and pH modulators (50 mg Na3PO4 and 35 mg Mg(OH)2) was not significantly different from the reference product (Zithromax®, Pfizer, New York, NY) in the rabbit model (p > 0.05). These results support the possibility of developing a generic product containing AZI.

Dynamic shapes of the zygote and two-cell mouse and human.

Mouse zygote morphokinetics were measured during interphase, the mitotic period, cytokinesis, and two-cell stage. Sequences of rounder-distorted-rounder shapes were revealed, as were changing patterns of cross section area. A calcium chelator and an actin-disrupting agent inhibited the area changes that occurred between pronuclear envelope breakdown and cytokinesis. During cell division, two vortices developed in each nascent cell and they rotated in opposite directions at each end of the cell, a pattern that sometimes persisted for up to 10 h. Exchange with the environment may have been promoted by these shape and area cycles and persisting circulation in the cytoplasm may have a similar function between a cell's interior and periphery. Some of these movements were sporadically also seen in human zygotes with abnormal numbers of pronuclei and the two-cell stages that developed from these compromised human zygotes.

Robot-Assisted Gait Self-Training: Assessing the Level Achieved.

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists' and patients' point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients?

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs.

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

Over 30% efficiency bifacial 4-terminal perovskite-heterojunction silicon tandem solar cells with spectral albedo.

We developed and designed a bifacial four-terminal perovskite (PVK)/crystalline silicon (c-Si) heterojunction (HJ) tandem solar cell configuration albedo reflection in which the c-Si HJ bottom sub-cell absorbs the solar spectrum from both the front and rear sides (reflected light from the background such as green grass, white sand, red brick, roofing shingle, snow, etc.). Using the albedo reflection and the subsequent short-circuit current density, the conversion efficiency of the PVK-filtered c-Si HJ bottom sub-cell was improved regardless of the PVK top sub-cell properties. This approach achieved a conversion efficiency exceeding 30%, which is higher than those of both the top and bottom sub-cells. Notably, this efficiency is also greater than the Schockley-Quiesser limit of the c-Si solar cell (approximately 29.43%). The proposed approach has the potential to lower industrial solar cell production costs in the near future.

Activeness and Loyalty Analysis in Event-Based Social Networks.

Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.

Distribution of Serological Response to Burkholderia pseudomallei in Swine from Three Provinces of Vietnam.

(1) Background: Burkholderia pseudomallei is an environmentally mediated saprophytic pathogen that can cause severe disease in humans. It is well known that B. pseudomallei survives in tropical moist soil environments worldwide, but melioidosis is gaining recognition as a public and veterinary health issue in Vietnam. The contribution of animals to human disease is unknown, necessitating further investigation. (2) Methods: Swine sera were collected from two populations, one grazing and one commercially farmed, from three provinces in Vietnam. ELISAs utilizing B. pseudomallei capsular polysaccharide (CPS), outer polysaccharide (OPS), and Hcp1 protein were used to screen serum samples. Positive samples were mapped to the commune level. Seroprevalence calculations and pig population estimates were used to approximate number of swine exposures per commune. (3) Results: Grazing pigs from Hoa Binh had significantly higher seropositivity levels (11.4%, 95% CI: 9.7-13.1) compared to farmed pigs from Ha Tinh and Nghe An (4%, 95% CI: 3.3-4.7). Average swine seropositivity rates were ~6.3% (95% CI: 5-7.9), higher than previously identified in Vietnam (~0.88%). (4) Conclusions: Initial serological sampling identified a significant number of seropositive and potential melioidosis infections occurring in swine in Vietnam. This work is a critical step in understanding the role swine may play in the epidemiology of human melioidosis in Vietnam.

A simple method for detection of a novel coronavirus (SARS-CoV-2) using one-step RT-PCR followed by restriction fragment length polymorphism.

A novel coronavirus associated with acute respiratory disease (named SARS-CoV-2) is recently identified in Wuhan city, China, spread rapidly worldwide. Early identification of this novel coronavirus by molecular tools is critical for surveillance and control of the epidemic outbreak. We aimed to establish a simple method for the detection of SARS-CoV-2 in differentiating with SARS-CoV. Primers of our in-house reverse transcription polymerase chain reaction (RT-PCR) assays were designed to target conserved regions of the RdRP gene and E gene, selected restriction enzymes EcoRI, Tsp45I, and AluI to distinguish between SARS-CoV-2 and SARS-CoV. In this report, a 396-bp fragment of the RdRp gene and 345-bp fragment of the E gene were amplified by one-step RT-PCR. Enzyme Tsp45I cuts the RdRP-amplified product of SARS-CoV-2 generating three fragments of 45, 154, and 197 bp, but it did not cut the amplicon of SARS-CoV. In contrast, the amplified product of SARS-CoV was digested with EcoRI producing two fragments of 76 and 320 bp, whereas the amplicon of SARS-CoV-2 was undigested by Tsp45I help to distinguish clearly SARS-CoV-2 from SARS-CoV on gel electrophoresis. In addition, AluI cut the amplicon of the E gene of SARS-CoV-2 generating two fragments of 248 and 97 bp without cutting to SARS-CoV. The accuracy of the assay was confirmed by sequencing and phylogenetic analysis. When evaluated on clinical samples showed a high sensitivity of 95%, specificity of our assay was 100% and clinical performance for detection of SARS-CoV-2 in comparison with other reference assays. In conclusion, in the present study, we successfully developed a simple method for molecular detection of SARS-CoV-2 in differentiating with SARS-CoV.

New Phosphine Oxides as High Performance Near- UV Type I Photoinitiators of Radical Polymerization.

Carbazole structures are of high interest in photopolymerization due to their enhanced light absorption properties in the near-UV or even visible ranges. Therefore, type I photoinitiators combining the carbazole chromophore to the well-established phosphine-oxides were proposed and studied in this article. The aim of this article was to propose type I photoinitiators that can be more reactive than benchmark phosphine oxides, which are among the more reactive type I photoinitiators for a UV or near-UV light emitting diodes (LED) irradiation. Two molecules were synthesized and their UV-visible light absorption properties as well as the quantum yields of photolysis and photopolymerization performances were measured. Remarkably, the associated absorption was enhanced in the 350-410 nm range compared to benchmark phosphine oxides, and one compound was found to be more reactive in photopolymerization than the commercial photoinitiator TPO-L for an irradiation at 395 nm.

Chitinophaga vietnamensis sp. nov., a multi-drug resistant bacterium infecting humans.

We describe a new multidrug resistant Chitinophaga species that was isolated from patients with type 2 diabetes in Vietnam. Strain BD 01T was cultivated in 2017 from a blood sample of a patient suffering from bacteremia. Strain VP 7442 was isolated in 2018 from a pleural fluid sample of a patient who had presented with lung abscess and pleural effusion. Both strains are aerobic, Gram-negative, non-motile and non-spore-forming. The 16S rRNA gene sequences of both strains are 100 % similar and share a highest 16S sequence identity with Chitinophaga polysaccharea MRP-15T of 97.42 %. Their predominant fatty acid is iso-C15 : 0 (73.8 % for strain BD 01T and 79.8 % for strain VP 7442). The draft genome sizes of strains BD 01T and VP 7442 are 6 308 408 and 6 308 579 bp, respectively. They are resistant to beta-lactams, aminoglycosides, fluoroquinolones, metronidazole, fosfomycin, vancomycin and macrolides, and exhibit 20 and 18 antimicrobial resistance-related genes, respectively. Using the multiphasic taxonogenomic approach, we propose that strains BD 01T (=CSUR P9622=VTCC 70981) and VP 7442 (=CSUR P9623=VTCC 70982) represent a new species, for which we propose the name Chitinophaga vietnamensis sp. nov. Strain BD 01T was chosen as type strain of C. vietnamensis sp. nov.

A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots.

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot's user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.

May I Keep an Eye on Your Training? Gait Assessment Assisted by a Mobile Robot.

A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from physiological gait patterns during training is important. Such immediate feedback also concerns the correct usage of forearm crutches in three-point gait. In the project ROGER, a mobile Socially Assistive Robot (SAR) to support patients after surgery in hip endoprosthetics is going to be developed. The current implementation status of the robotic application developed for the use in a real-world scenario is presented below.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Formulation and biopharmaceutical evaluation of bitter taste masking microparticles containing azithromycin loaded in dispersible tablets.

The objective of this study was to prepare and evaluate some physiochemical and biopharmaceutical properties of bitter taste masking microparticles containing azithromycin loaded in dispersible tablets. In the first stage of the study, the bitter taste masking microparticles were prepared by solvent evaporation and spray drying method. When compared to the bitter threshold (32.43µg/ml) of azithromycin (AZI), the microparticles using AZI:Eudragit L100=1:4 and having a size distribution of 45-212µm did significantly mask the bitter taste of AZI. Fourier transform infrared spectroscopy (FTIR), and proton nuclear magnetic resonance spectroscopy (1H NMR) proved that the taste masking of microparticles resulted from the intermolecular interaction of the amine group in AZI and the carbonyl group in Eudragit L100. Differential scanning calorimeter (DSC) analysis was used to display the amorphous state of AZI in microparticles. Images obtaining from optical microscopy and scanning electron microscopy (SEM) indicated the existence of microparticles in regular cube shape with many layers. In the second stage, dispersible tablets containing microparticles (DTs-MP) were prepared by direct compression technique. Stability study was conducted to screen pH modulators for DTs-MP, and a combination of alkali agents (CaCO3:NaH2PO4, 2:1) was added into DTs-MP to create microenvironment pH of 5.0-6.0 for the tablets. The disintegration time of optimum DTs-MP was 53±5.29s and strongly depended on the kinds of lubricant and diluent. The pharmacokinetic study in the rabbit model using liquid chromatography tandem mass spectrometry showed that the mean relative bioavailability (AUC) and mean maximum concentration (Cmax) of DTs-MP were improved by 2.19 and 2.02 times, respectively, compared to the reference product (Zithromax®, Pfizer).

Torrefaction of empty fruit bunches under biomass combustion gas atmosphere.

Torrefaction of oil palm empty fruit bunches (EFB) under combustion gas atmosphere was conducted in a batch reactor at 473, 523 and 573K in order to investigate the effect of real combustion gas on torrefaction behavior. The solid mass yield of torrefaction in combustion gas was smaller than that of torrefaction in nitrogen. This may be attributed to the decomposition enhancement effect by oxygen and carbon dioxide in combustion gas. Under combustion gas atmosphere, the solid yield for torrefaction of EFB became smaller as the temperature increased. The representative products of combustion gas torrefaction were carbon dioxide and carbon monoxide (gas phase) and water, phenol and acetic acid (liquid phase). By comparing torrefaction in combustion gas with torrefaction in nitrogen gas, it was found that combustion gas can be utilized as torrefaction gas to save energy and inert gas.

Multitarget Quantitative PCR Improves Detection and Predicts Cultivability of the Pathogen Burkholderia pseudomallei.

Burkholderia pseudomallei is present in the environment in many parts of the world and causes the often-fatal disease melioidosis. The sensitive detection and quantification of B. pseudomallei in the environment are a prerequisite for assessing the risk of infection. We recently reported the direct detection of B. pseudomallei in soil samples using a quantitative PCR (qPCR) targeting a single type three secretion system 1 (TTSS1) gene. Here, we extend the qPCR-based analysis of B. pseudomallei in soil by validating novel qPCR gene targets selected from a comparative genomic analysis. Two hundred soil samples from two rice paddies in northeast Thailand were evaluated, of which 47% (94/200) were B. pseudomallei culture positive. The TTSS1 qPCR and two novel qPCR assays that targeted open reading frames (ORFs) BPSS0087 and BPSS0745 exhibited detection rates of 76.5% (153/200), 34.5% (69/200), and 74.5% (150/200), respectively. The combination of TTSS1 and BPSS0745 qPCR increased the detection rate to 90% (180/200). Combining the results of the three qPCR assays and the BPSS1187 nested PCR previously published, all 200 samples were positive by at least one PCR assay. Samples positive by either TTSS1 (n = 153) or BPSS0745 (n = 150) qPCR were more likely to be direct-culture positive, with odds ratios of 4.0 (95% confidence interval [CI], 1.7 to 9.5; P < 0.001) and 9.0 (95% CI, 3.1 to 26.4; P < 0.001), respectively. High B. pseudomallei genome equivalents correlated with high CFU counts by culture. In conclusion, multitarget qPCR improved the B. pseudomallei detection rate in soil samples and predicted culture positivity. This approach has the potential for use as a sensitive environmental screening method for B. pseudomalleiIMPORTANCE The worldwide environmental distribution of the soil bacterium Burkholderia pseudomallei remains to be determined. So far, most environmental studies have relied on culture-based approaches to detect this pathogen. Since current culture methods are laborious, are time consuming, and have limited sensitivity, culture-independent and more sensitive methods are needed. In this study, we show that a B. pseudomallei-specific qPCR approach can detect significantly higher numbers of B. pseudomallei-positive soil samples from areas where it is endemic compared with that from culture. The use of multiple independent B. pseudomallei-specific qPCR targets further increased the detection rate of B. pseudomallei compared with that from single targets. Samples with a high molecular B. pseudomallei load were more likely to be culture positive. We conclude that our quantitative multitarget approach might be useful in defining areas where there is a risk of B. pseudomallei infections in different parts of the world.