A cross-sectional study of the antibiotic resistant prevalence of ESBL-producing Enterobacteriaceae in Vietnam

Introduction: Antibiotic resistance in Enterobacteriaceae producing extended spectrum beta-lactamase (ESBL) is increasing. Accurate evaluation of antibiotic resistance rates in various categories of bacteria assists medical physicians in recommending suitable indications for their medical problems, improving treatment efficiency, and minimizing dangers for patients. As a result, we undertook this research to assess the prevalence of ESBL-producing Enterobacteriaceae as well as the rate of antibiotic resistance in ESBL-producing Enterobacteriaceae. Methods: A cross-sectional study was conducted on 2716 patients at the An Giang Central General Hospital from June 2020 to June 2021. Data collection was based on interviews and used SPSS 18.0 and GraphPad Prism 9 for data analysis and presentation. Samples included urine, blood, sputum, and pus. Samples were treated with the Phoenix 100 automated machine to separate and identify samples. Results: The highest rate was 64.8% for Escherichia coli, followed by 30.2% for Klebsiella pneumoniae. Proteus mirabilis and Klebsiella oxytoca were found in 4.5% and 0.6% of the samples, respectively. Ampicillin resistance was greatest in E. coli (96.5%), K. pneumonia (92.4%), and K oxytoca (83.3%). The frequency of resistance to the other antibiotics was likewise extremely high, approaching 60%. Tobramycin, Amoxicillin/Clavulanate, Cefoxitin, and Nitrofurantoin were totally resistant to K. oxytoca in the ESBL-producing group. E. coli and K. pneumoniae with ESBL-producing genes also have a high antibiotic resistance rate of more than 50%. Conclusion: E. coli was the most common pathogenic bacteria. Most of the species of bacteria resisted Ampicillin

Comparative population genetics of swimming crab host (Portunus pelagicus) and common symbiotic barnacle (Octolasmis angulata) in Vietnam

Background By comparing spatial geographical structures of host populations with that of their symbionts light can be shed on their biological interactions, and the degree of congruence between host and symbiont phylogeographies should reflect their life histories and especially dispersal mechanisms. Methods Here, we analyzed the genetic diversity and structure of a host, the blue swimming crab, Portunus pelagicus, and its symbiotic pedunculate barnacle Octolasmis angulata from six location sites representing three geographic regions (north, central and south) along the Vietnam coastline. High levels of congruence in their phylogeographic patterns were expected as they both undergo planktonic larval stages. Results Based on the COI mtDNA markers, O. angulata populations showed higher genetic diversity in comparison with their host P. pelagicus (number of haplotype/individuals, haplotype and nucleotide diversity are 119/192, 0.991 ± 0.002 and 0.02; and 89/160, 0.913 ± 0.02 and 0.015, respectively). Pairwise Fst and AMOVA analyses showed a more pronounced population structure in the symbiotic barnacle than in its crab host. The DAPC analyses identified three genetic clusters. However, both haplotype networks and scatter plots supported connectivity of the host and the symbiotic barnacle throughout their distribution range, except for low subdivision of southern population. Isolation by distance were detected only for the symbiont O. angulata (R2 = 0.332, P = 0.05), while dbMEM supported spatial structure of both partners, but only at MEM-1 (Obs. 0.2686, P < 0.01 and Obs. 0.2096, P < 0.01, respectively).publishedVersio

Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding

Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the triplet loss or pairwise loss which requires the mining of positive and negative samples w.r.t. anchor data points. This is, however, challenging in an unsupervised setting as the label information is not available. In this paper, we propose a new UDML method that overcomes that challenge. In particular, we propose to use a deep clustering loss to learn centroids, i.e., pseudo labels, that represent semantic classes. During learning, these centroids are also used to reconstruct the input samples. It hence ensures the representativeness of centroids - each centroid represents visually similar samples. Therefore, the centroids give information about positive (visually similar) and negative (visually dissimilar) samples. Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space. Experimental results on benchmarking datasets show that the proposed approach outperforms other UDML methods.Comment: Accepted in BMVC 202

Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology

Federated learning is an active research topic since it enables several participants to jointly train a model without sharing local data. Currently, cross-silo federated learning is a popular training setting that utilizes a few hundred reliable data silos with high-speed access links to training a model. While this approach has been widely applied in real-world scenarios, designing a robust topology to reduce the training time remains an open problem. In this paper, we present a new multigraph topology for cross-silo federated learning. We first construct the multigraph using the overlay graph. We then parse this multigraph into different simple graphs with isolated nodes. The existence of isolated nodes allows us to perform model aggregation without waiting for other nodes, hence effectively reducing the training time. Intensive experiments on three public datasets show that our proposed method significantly reduces the training time compared with recent state-of-the-art topologies while maintaining the accuracy of the learned model. Our code can be found at https://github.com/aioz-ai/MultigraphFLComment: accepted in ICCV 202

Students’ Perceived Well-Being and Online Preference : Evidence from Two Universities in Vietnam during COVID-19

University education is still being impacted two years after the COVID-19 outbreak. We performed a rapid survey in February 2022 at two public universities in Vietnam to examine the effects of the pandemic on well-being and the factors that may associate with online class preference among university students as well as to investigate the need for support to improve resilience. A web-based survey included 1589 undergraduate students in total. Both quantitative and qualitative data analysis was carried out. Overall, approximately a quarter of respondents said that they perceived an influence on their health, 42.9% expressed stress, and more than 70% reported worrying about the future. In total, 61.9% of the respondents reported having satisfaction with online classes, while over half of them preferred a program of 50% online classes. Students who live in an urban area, are female, have had pre-COVID-19 campus life experience, have decreased income, and/or experience low online satisfaction and over-information may be in need of more support. The results show implications for universities to consider policies addressing well-being and post-pandemic online education. Providing support to university students to improve their resilience against the impact on their studying, campus life, health, and well-being should be prioritized during and post-pandemic

A hierarchical architecture for increasing efficiency of large photovoltaic plants under non-homogeneous solar irradiation

Under non-homogeneous solar irradiation, photovoltaic (PV) panels receive different solar irradiance, resulting in a decrease in efficiency of the PV generation system. There are a few technical options to fix this issue that goes under the name of mismatch. One of these is the reconfiguration of the PV generation system, namely changing the connections of the PV panels from the initial configuration to the optimal one. Such technique has been widely considered for small systems, due to the excessive number of required switches. In this paper, the authors propose a new method for increasing the efficiency of large PV systems under non-homogeneous solar irradiation using Series-Parallel (SP) topology. In the first part of the paper, the authors propose a method containing two key points: a switching matrix to change the connection of PV panels based on SP topology and the proof that the SP-based reconfiguration method can increase the efficiency of the photovoltaic system up to 50%. In the second part, the authors propose the extension of the method proposed in the first part to improve the efficiency of large solar generation systems by means of a two-levels architecture to minimize the cost of fabrication of the switching matrix

The role of reservoirs under the impacts of climate change on the Srepok River basin, Central Highlands of Vietnam

Forecasting streamflow is important for managing future water resources and environmental needs under the impacts of climate change. Moreover, quantifying the combined effects of future climate variations and human-made infrastructures, e.g., dams and reservoirs, poses a significant challenge. In this study, we used the Soil and Water Assessment Tool (SWAT) for a case study in the Srepok River Basin—a tributary of the Mekong River Basin. Here, we aim to reveal the impacts of various climate change scenarios and the effects of reservoir operations in this region. Our findings indicate that 1) the projected annual streamflow is anticipated to increase by a minimum of 9.2% (2046–2065) and could peak at an increase of 14.9% (2080–2099) under the highest greenhouse gas emissions, 2) Srepok 4, Srepok 3, and Buon Kuop demonstrate a higher capability for mitigating flood peaks and managing seasonal flow in the downstream floodplain, whereas Buon Tua Srah shows the least performance, and 3) reservoirs operated with annual regulation have more pronounced impacts than those regulated on a daily schedule. Our work provides i) a scientific foundation for regional stakeholders and decision-makers to develop sustainable strategies that address the combined effects of reservoir operation and future climate, and ii) it supports national authorities and officials in resolving conflicts related to transboundary rivers within the Mekong River Basin

MemoriEase at the NTCIR-17 Lifelog-5 Task

We present the MemoriEase retrieval system used for our participation in the NTCIR Lifelog-5 Task. We report our method to address the lifelog retrieval problem and discuss our official results of the MemoriEase at Lifelog-5 task. We originally introduced the MemoriEase system for the Lifelog Search Challenge (LSC) as an interactive lifelog retrieval system. We have modified it to an automatic retrieval system to address the NTCIR Lifelog-5 Task. We propose the BLIP-2 model as the core embedding model to retrieve lifelog images from textual queries. The open-sourced Elasticsearch search engine serves as the main engine in the MemoriEase system. Some pre-processing and post-processing techniques are applied to adapt this system to an automatic version and improve the accuracy of retrieval results. Finally, we discuss the results of the system on the task, some limitations of the system, and lessons learned from participating in the Lifelog-5 task for further improvements for the system in the future

Tax corruption and private sector development in Vietnam

This article aims to examine the impact of tax corruption on private sector development in Vietnam. It is motivated by two separate but related considerations. First, despite the seriousness of the phenomenon of corruption, there is a paucity of rigorous empirical research of corruption, particularly tax corruption, in Vietnam. Secondly, ineffective control of corruption is viewed as a cause of Vietnam’s recent total factor productivity (TFP) slowdown or its poor industrial policy, both of which may hamper Vietnam’s progress as a low middle-income country. Without some understanding on the impact of tax corruption on the economy, it may not be possible to devise the most effective anti-corruption policy and measures. After a brief literature review that focuses on tax corruption, various conceptual issues relating to tax corruption are discussed and clarified. The extent of petty tax corruption in Vietnam is then discussed, followed by a review of findings and implications of recent studies on how tax corruption impacts on private sector development in Vietnam. Despite perceptions and evidence of widespread petty tax corruption, Vietnam ranks very highly both in terms of tax collection and tax effort.Not unexpectedly, the impact of tax corruption is mixed in the sense that empirical evidence lends credence to both 'sanding the wheels' and 'greasing the wheels' hypotheses. Finally, some broad policy recommendations for combating tax corruption are offered