Predicting Cardiotoxicity of Molecules Using Attention-Based Graph Neural Networks.

In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.

Mechanistic and Kinetic Insights into OH-Initiated Atmospheric Oxidation of Hymexazol: A Computational Study.

Hymexazol is a volatile fungicide widely used in agriculture, causing its abundance in the atmosphere; thus, its atmospheric fate and conversion are of great importance when assessing its environmental impacts. Herein, we report a theoretical kinetic mechanism for the oxidation of hymexazol by OH radicals, as well as the subsequent reactions of its main products with O2 and then with NO by using the Rice-Ramsperger-Kassel-Marcus-based Master equation kinetic model on the potential energy surface explored at the ROCBS-QB3//M06-2X/aug-cc-pVTZ level. The predicted total rate constants ktotal(T, P) for the reaction between hymexazol and OH radicals show excellent agreement with scarcely available experimental values (e.g., 3.6 × 10-12 vs (4.4 ± 0.8) × 10-12 cm3/molecule/s at T = 300 K and P = 760 Torr); thus, the calculated kinetic parameters can be confidently used for modeling/simulation of N-heterocycle-related applications under atmospheric and even combustion conditions. The model shows that 3,4-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-5-yl (IM2), 3,5-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-4-yl (IM3), and (3-hydroxy-1,2-oxazol-5-yl)methyl (P8) are the main primary intermediates, which form the main secondary species of (3,4-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-5-yl)dioxidanyl (IM4), (3,5-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-4-yl)dioxidanyl (IM7), and ([(3-hydroxy-1,2-oxazol-5-yl)methyl]dioxidanyl (IM11), respectively, through the reactions with O2. The main secondary species then can react with NO to form the main tertiary species, namely, (3,4-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-5-yl)oxidanyl (P19), (3,5-dihydroxy-5-methyl-4,5-dihydro-1,2-oxazol-4-yl)oxidanyl (P21), and [(3-hydroxy-1,2-oxazol-5-yl)methyl]oxidanyl (P23), respectively, together with NO2. Besides, hymexazol could be a persistent organic pollutant in the troposphere due to its calculated half-life τ1/2 of 13.7-68.1 h, depending on the altitude.

OH-initiated oxidation of vinyl butyrate: ab initio insights.

The widespread use of vinyl butyrate (CH2CHOC(O)CH2CH2CH3 or VB) in the polymer industry and daily-life materials inevitably results in its emission into the atmosphere. Therefore, understanding the mechanism and kinetics of the VB conversion is critical for evaluating its fate and environmental impacts. Herein, we theoretically investigate the chemical transformation of VB initiated by OH radicals in the atmosphere using the stochastic Rice-Ramsperger-Kassel-Marcus (RRKM)-based master equation kinetic model on the potential energy surface explored at the M06-2X/aug-cc-pVTZ level of theory. Showing excellent agreement with limited experimental kinetic data, the VB + OH kinetic model reveals that H-abstraction from Cß (i.e., -CßH2CH3) prevails over the OH-addition to the double bond (CC), even at low temperatures. The detailed analyses, including those of the time-resolved species profiles, reaction rate, and reaction flux, reveal the reaction mechanism shift with temperature (causing the U-shaped temperature dependence of k(T, P)) and the noticeable pressure dependence of k(T, P) at low temperatures. The secondary chemistry under atmospheric conditions (namely, the reaction of the main product with O2 and its subsequent reactions with NO) was then characterized within the same framework to reveal the detailed kinetic mechanism (e.g., [4-(ethenyloxy)-4-oxobutan-2-yl]oxidanyl (IM12) + NO2 is the dominant channel under atmospheric conditions), suggesting VB is not a persistent organic pollutant and a new environmental concern regarding the formed NO2. Also, the kinetic behaviors of vinyl butyrate and its oxidation products were extended from atmospheric to combustion conditions for further applications. Moreover, through TD-DFT calculations, it is shown that several related important species (i.e., 1-(ethenyloxy)-1-oxobutan-2-yl (P4), [4-(ethenyloxy)-4-oxobutan-2-yl]dioxidanyl (IM7), and IM12) can potentially undergo photolysis in the atmosphere.

Ab initio kinetics of the CH3NH + NO2 reaction: formation of nitramines and N-alkyl nitroxides.

This study provides a detailed understanding of how the reaction between CH3NH, one of the primary products of the CH3NH2 + OH/Cl reactions, and NOx occurs in the atmosphere since the reaction is expected to be a dominant sink for the tropospheric CH3NH radical. First, we focus on the reaction of the aminyl radical CH3NH with NO2, complementing the known reaction between CH3NH and NO, to provide the overall picture of the CH3NH + NOx system. The reaction was meticulously examined across the extended range of temperature (298-2000 K) and pressure (0.76-76 000 torr) using quantum chemistry calculations and kinetic modeling based on the framework of the Rice-Ramsperger-Kassel-Marcus (RRKM)-based master equation. Highly correlated electronic structure calculations unveil that the intricate reaction mechanism of the CH3NH + NO2 reaction, which can proceed through O-addition or N-addition to form NO2, encompasses numerous steps, channels, and various intermediates and products. The temperature-/pressure-dependent kinetic behaviors and product distribution of the CH3NH + NO2 reaction are revealed under atmospheric and combustion conditions. The main products under atmospheric conditions are found to be CH3NHO and NO, as well as CH3NHNO2, while under combustion conditions, the primary products are only CH3NHO and NO. Given its stability under ambient conditions, CH3NHNO2, a nitramine, is believed to have the potential to induce DNA damage, which can ultimately result in severe cancers. Secondly, by building upon prior research on the CH3NH + NO system, this study shows that the reaction of CH3NH with NOx holds greater importance in urban areas with elevated NOx emissions than other oxidants like O2. Furthermore, this reaction occurs swiftly and results in the creation of various compounds, such as the carcinogenic nitrosamine (CH3NHNO), carcinogenic nitramine (CH3NHNO2), CH3NNOH, (CH3NN + H2O) and (CH3NHO + NO).

Bi-decadal trend of atmospheric emissions from thermal power plants in Mainland Southeast Asia: Implications on acid deposition and climate change Mitigation.

Steady increase in electricity generation and heavy reliance on coal in Mainland Southeast Asia (M-SEA) create huge pressure on the environment. This study used information collected from individual thermal power plants (TPPs) in M-SEA to calculate emissions of air pollutants and greenhouse gases (GHG) for 2010, 2015 and 2019. The emissions were projected to 2030 following the latest national Power Development Plans. The emission results were analyzed in relation to the power development by country and fuel type, and environmental impacts. The region collective annual TPP emissions in 2019, in Gg/yr, were 27 PM2.5, 77 PM10, 0.7 BC, 4.9 OC, 255 SO2, 451 NOx, 91 CO, 12 NMVOC, 0.4 NH3, 260 CO2, 13 CH4, and 26 N2O. Coal-fired TPPs dominated the emissions of most species while NG-fired contributed the largest amounts of NH3 and CH4. Bi-decadal increase in energy production from TPPs of nearly 3 times is accompanied by 2.7 times increase in emissions. The 2010-2019 period saw average emissions increase by 1.9 times (TPPs' energy production increased 1.6 times), slightly higher than the rate of 1.4 times projected for 2019-2030 (double TPPs' energy production). The current intrusion rate of renewable energy accompanied by phasing-out of old TPPs are still by far insufficient to reverse the emission trend. Aggressive power development in Vietnam with its heavy coal reliance made it the largest emitter in 2019 and the projected for 2030, followed by Thailand. Spatially, higher emissions are seen over locations of large coal-fired TPPs in Vietnam and Thailand. Available rainwater composition monitoring data showed higher deposition amounts of sulfate and nitrate in areas located near or downwind of large TPPs. Significant GHG emissions projected for TPPs in 2030 indicated that TPPs should be the priority for emission reduction to achieve Nationally Determined Contribution targets. Emission database produced by this study can be used in dispersion modeling studies to assess impacts of TPPs on air quality, health, and acid deposition.

iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.

BACKGROUND: Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec - an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. RESULTS: The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. CONCLUSIONS: iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec .

iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation.

Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synthesized chemicals, a number of natural compounds have also been proven to be as effective in their antimalarial properties. Besides experimental approaches to investigate antimalarial activities in natural products, computational methods have been developed with satisfactory outcomes obtained. In this study, we propose a novel molecular encoding scheme based on Bidirectional Encoder Representations from Transformers and used our pretrained encoding model called NPBERT with four machine learning algorithms, including k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), and Random Forest (RF), to develop various prediction models to identify antimalarial natural products. The results show that SVM models are the best-performing classifiers, followed by the XGB, k-NN, and RF models. Additionally, comparative analysis between our proposed molecular encoding scheme and existing state-of-the-art methods indicates that NPBERT is more effective compared to the others. Moreover, the deployment of transformers in constructing molecular encoders is not limited to this study but can be utilized for other biomedical applications.

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding.

The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Alongside experimental approaches, numerous computational models have been proposed to address this biochemical issue. In this study, we introduce iCYP-MFE, a computational framework for virtual screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms. iCYP-MFE contains a set of five robust, stable, and effective prediction models developed using multitask learning incorporated with molecular fingerprint-embedded features. The results show that multitask learning can remarkably leverage useful information from related tasks to promote global performance. Comparative analysis indicates that iCYP-MFE achieves three predominant tasks, one equivalent task, and one less effective task compared to state-of-the-art methods. The area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) were two decisive metrics used for model evaluation. The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC value of 0.93 while the prediction task for CYP1A2-inhibition obtains the highest AUC-PR value of 0.92. The substructural analysis preliminarily explains the nature of the CYP-inhibitory activity of compounds. An online web server for iCYP-MFE with a user-friendly interface was also deployed to support scientific communities in identifying CYP inhibitors.

Effect of blanching pretreatment and microwave-vacuum drying on drying kinetics and physicochemical properties of purple-fleshed sweet potato.

In this study, drying kinetics and quality of purple-fleshed sweet potato (PFSP) subjected to microwave-vacuum drying were investigated. The effects of hot water and steam blanching pretreatment on physicochemical characteristics of the dried products were also considered. The samples were dehydrated in a custom-made microwave-vacuum system at different power levels including 450, 600 and 850 W. Hot air drying at 70 °C was also conducted for comparison. The results showed that drying time of PFSP under microwave-vacuum conditions ranged from 6 to 12 min, significantly reduced as compared to that of hot air drying (600 min). The improvement of drying rate was also evidenced by increased effective moisture diffusivity (2.22 × 10-7-4.05 × 10-7 m2/s) of the samples. Drying kinetics of PFSP was best fitted by Page and logarithmic model with R2 ranging from 0.991 to 0.998, and RMSE from 0.016 to 0.030. PFSP dried under microwave-vacuum condition had lower water absorption index and swelling capacity than hot air drying. Color, antioxidant activity and total phenolic content of dried PFSP were also improved under microwave-vacuum drying. The effects of blanching pretreatment on quality of dried PFSP were more dominant in hot air than microwave-vacuum dried samples.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery.

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

Mapping quantitative trait loci for root development under hypoxia conditions in soybean (Glycine max L. Merr.).

KEY MESSAGE: Greatest potential, QTLs for hypoxia and waterlogging tolerance in soybean roots were detected using a new phenotypic evaluation method. Waterlogging is a major environmental stress limiting soybean yield in wet parts of the world. Root development is an important indicator of hypoxia tolerance in soybean. However, little is known about the genetic control of root development under hypoxia. This study was conducted to identify quantitative trait loci (QTLs) responsible for root development under hypoxia. Recombinant inbred lines (RILs) developed from a cross between a hypoxia-sensitive cultivar, Tachinagaha, and a tolerant landrace, Iyodaizu, were used. Seedlings were subjected to hypoxia, and root development was evaluated with the value change in root traits between after and before treatments. We found 230 polymorphic markers spanning 2519.2 cM distributed on all 20 chromosomes (Chrs.). Using these, we found 11 QTLs for root length (RL), root length development (RLD), root surface area (RSA), root surface area development (RSAD), root diameter (RD), and change in average root diameter (CARD) on Chrs. 11, 12, 13 and 14, and 7 QTLs for hypoxia tolerance of these root traits. These included QTLs for RLD and RSAD between markers Satt052 and Satt302 on Chr. 12, which are important markers of hypoxia tolerance in soybean; those QTLs were stable between 2 years. To validate the QTLs, we developed a near-isogenic line with the QTL region derived from Iyodaizu. The line performed well under both hypoxia and waterlogging, suggesting that the region contains one or more genes with large effects on root development. These findings may be useful for fine mapping and positional cloning of gene responsible for root development under hypoxia.

Increased invasive potential of non-native Phragmites australis: elevated CO2 and temperature alleviate salinity effects on photosynthesis and growth.

The prospective rise in atmospheric CO2 and temperature may change the distribution and invasive potential of a species; and intraspecific invasive lineages may respond differently to climate change. In this study, we simulated a future climate scenario with simultaneously elevated atmospheric CO2 and temperature, and investigated its interaction with soil salinity, to assess the effects of global change on the ecophysiology of two competing haplotypes of the wetland grass Phragmites australis, that are invasive in the coastal marshes of North America. The two haplotypes with the phenotypes 'EU-type' (Eurasian haplotype) and 'Delta-type' (Mediterranean haplotype), were grown at 0‰ and 20‰ soil salinity, and at ambient or elevated climatic conditions (700 ppm CO2, +5 °C) in a phytotron system. The aboveground growth of both phenotypes was highest at the elevated climatic conditions. Growth at 20‰ salinity resulted in declined aboveground growth, lower transpiration rates (E), stomata conductance (gs), specific leaf area, photosynthetic pigment concentrations, and a reduced photosynthetic performance. The negative effects of salinity were, however, significantly less severe at elevated CO2 and temperature than at the ambient climatic conditions. The Delta-type P. australis had higher shoot elongation rates than the EU-type P. australis, particularly at high salinity. The Delta-type also had higher maximum light-saturated rates of photosynthesis (Asat), maximum carboxylation rates of Rubisco (Vcmax), maximum electron transport rates (Jmax), triose phosphate utilization rates (Tp), stomata conductance (gs), as well as higher Rubisco carboxylation-limited, RuBP regeneration-limited and Tp-regeneration limited CO2 assimilation rates than the EU-type under all growth conditions. Our results suggest that the EU-type will not become dominant over the Delta-type, since the Delta-type has superior ecophysiological traits. However, the projected rise in atmospheric CO2 and temperature will alleviate the effects of salinity on both phenotypes and facilitate their expansion into more saline areas.

A public health response to data interoperability to prevent child maltreatment.

The sharing of data, particularly health data, has been an important tool for the public health community, especially in terms of data sharing across systems (i.e., interoperability). Child maltreatment is a serious public health issue that could be better mitigated if there were interoperability. There are challenges to addressing child maltreatment interoperability that include the current lack of data sharing among systems, the lack of laws that promote interoperability to address child maltreatment, and the lack of data sharing at the individual level. There are waivers in federal law that allow for interoperability to prevent communicable diseases at the individual level. Child maltreatment has a greater long-term impact than a number of communicable diseases combined, and interoperability should be leveraged to maximize public health strategies to prevent child maltreatment.

Introducing a Competency Framework for Educational Researchers: The Case of Vietnam.

In this paper, based on the established Vitae Researcher Development Framework (RDF), we introduce a new framework, tailor-made specifically for Vietnamese educational researchers, namely, Vietnam's Framework for Educational Researchers (VFER). VFER is expected to serve as a tool for Vietnamese educational researchers to self-evaluate their skills and support them in developing their career qualities from junior to senior career ladders. The framework includes four domains with ten subdomains and 28 indicators. To date, VFER has been implemented in some Vietnamese universities of pedagogical education. We expect that other research fields will look to VFER as a reference to build their own research capacity framework.

Strengthening the Application of the DAIDS GCLP Guidelines: The Implementation of an Integrated Laboratory Oversight Framework.

The Division of AIDS (DAIDS) Good Clinical Laboratory Practice (GCLP) Guidelines establish a framework to guide the oversight of laboratories supporting DAIDS-sponsored clinical research or trials. Compliance with these guidelines promotes data reliability, validity, and safety of the clinical research or trial participants and laboratory staff and ensures adherence to regulatory requirements. Acknowledgment and adoption of the DAIDS GCLP Guidelines are critical in building laboratory capacity and preparedness for conducting clinical trials. In collaboration with DAIDS, laboratory experts support the implementation of the DAIDS Integrated Laboratory Oversight Framework activities. This article describes the implementation of the GCLP Guidelines, the Integrated Laboratory Oversight Framework activities, and the coordinated efforts to strengthen laboratory performance. The Framework activities include four components: Quality Assurance Oversight, GCLP Audits, GCLP Training, and Laboratory Quality Improvement. Comparison of GCLP Guidelines with other regulations or standards, including US Clinical Laboratory Improvement Amendments (CLIA) regulation 42 CFR 493, College of American Pathologists (CAP), World Health Organization (WHO) GCLP, and International Organization for Standardization, ISO 15189:2012 standards, highlighted the differences and similarities to guide integration and harmonization efforts. Processes related to the Framework activities are outlined in detail, including key data derived from the managed activities of over 175 laboratories worldwide. Via the evolution of the DAIDS GCLP Guidelines and laboratory oversight workflows, the laboratories participating in DAIDS-sponsored clinical research and trials have successfully participated in internal and external regulatory audits. The collaborative and integrated oversight approach promotes knowledge-sharing and accountability to support the implementation of the DAIDS GCLP guidelines and compliance monitoring. Lessons learned have helped with the implementation of the DAIDS integrated laboratory oversight approach and quality oversight programs at multiple laboratories worldwide.

Characterizing Neisseria meningitidis in Southern Vietnam between 2012 and 2021: A predominance of the chloramphenicol-resistant ST-1576 lineage.

Objectives: Our goal was to describe Invasive Meningococcal Disease (IMD) in Southern Vietnam over the last 10 years. We characterized 109 Neisseria meningitidis strains in Southern Vietnam isolated between 1980s to 2021, that were collected from IMD (n = 44), sexually transmitted infections (n = 2), and healthy carriage (n = 63). Methods: IMD were confirmed by bacterial culture and/or real-time polymerase chain reaction at the national reference laboratory in Pasteur Institute of Ho Chi Minh City (PIHCM). Antimicrobial resistance was determined on 31 IMD and two sexually transmitted infection isolates with E-test for chloramphenicol (CHL), penicillin (PEN), ciprofloxacin (CIP), ceftriaxone (CRO), and rifampicin (RIF). Sequencing was performed for analyzing of multilocus-sequence-typing (MLST), porA, fetA, and antibiotic resistance genes, including gyrA, penA, and rpoB. Results: The incidence rate during this period was 0.02 per 100,000 persons/year. Serogroup B accounted for over 90% of cases (50/54). ST-1576 were mainly responsible for IMD, 27/42 MLST profiles, and associated with CHL resistance. Resistance was prevalent among IMD isolates. Thirteen were resistant to CHL (minimum inhibitory concentration [MIC] ≥16 mg/l), 12 were intermediate to PEN (MIC between 0.19 and 0.5 mg/l), and five were CIP-resistant (MIC between 0.19 and 0.5 mg/l). Particularly, one was non-susceptible to CRO (MIC at 0.125 mg/l), belonging to ST-5571 lineage. The resistance was due to carrying resistant alleles of penA and gyrA genes, and catP gene. Notably, seven isolates were resistant/non-susceptible to two or more antibiotics. Conclusion: Our results suggest the persistence of the circulating ST-1576 in Southern Vietnam, with a spread of antimicrobial resistance across the community.

Genome sequence of Xenia2 a DV cluster phage that infects Gordonia rubripertincta.

Xenia2 is a DV cluster actinobacteriophage that infects Gordonia rubripertincta NRRL B-16540. The genome is 68,135bp, has a GC content of 57.9% and 98 predicted protein-coding genes, 33 of which have a predicted function. Xenia2 has a lysis cassette with an endolysin (lysin A) and four different holin-like transmembrane proteins.

An investigation into EFL pre-service teachers' academic writing strategies.

Recent research has shown writing strategies to have a substantial impact on language learners' writing performance but little is known about what strategies EFL learners use and how they use them in writing academic texts such as reports, final assignments, and project papers. The study reported in this paper extends this line of research by investigating the strategies Vietnamese EFL pre-service teachers use in academic writing. Data included document analysis of 17 pre-service teachers' final assignment papers (one paper per teacher) and individual semi-structured interviews with ten teachers. The study adopted a content-based approach to qualitative data analysis with reference to a comprehensive research-based taxonomy for L2 academic writing strategies, including rhetorical, metacognitive, cognitive, and social affective strategies. The results show that rhetorical, metacognitive, and cognitive strategies were most frequently used by the teacher participants. The results further show that self-efficacy and self-regulation determined the teachers' use of strategies during the writing process. Implications for the L2 writing classroom focused on academic writing strategies to enhance pre-service teachers' writing quality will be discussed.

Preparation and characterization of cellulose nanocrystals from corncob via ionic liquid [Bmim][HSO4] hydrolysis: effects of major process conditions on dimensions of the product.

In this study, cellulose nanocrystals were prepared via the hydrolysis of corncob (CC) biomass using Brønsted acid ionic liquid 1-butyl-3-methylimidazolium hydrogen sulfate [Bmim][HSO4]. The corncob was subjected to alkaline pretreatment, and was then hydrolysed by [Bmim][HSO4], which acted as both solvent and catalyst. The effects of process conditions, including mass percent of CC (1.0-10.0%), reaction temperature (46-110 °C), and reaction time (1.2-2.8 h) on the size of cellulose nanocrystals (IL-CCCNC) were investigated by response surface methodology-central composite design. The obtained IL-CCCNC was characterized by Fourier transforms infrared spectroscopy, zeta sizer, scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and thermogravimetry. The results showed that the dimensions of the nanocellulose products were affected by the mass percent of CC and the reaction temperature, but were not significantly influenced by the reaction time under the studied conditions. The optimal conditions, estimated by the developed model, were a mass percent of 2.49%, reaction temperature of 100 °C, and reaction time of 1.5 h. The process successfully produced IL-CCCNC with a yield of 40.13%, average size of 166 nm, and crystallinity index (CrI) of 62.5%. The morphology, chemical fingerprints, and thermal properties of the obtained IL-CCCNC were comparable to those extracted by alkaline and acid hydrolysis. After the reaction, [Bmim][HSO4] could be recovered with a yield of 88.32%, making it a viable green catalyst for the hydrolysis of CC cellulose. The findings are of direct industrial relevance as optimal processes can be developed to produce nanocellulose crystals with desirable size and physicochemical characteristics.