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 Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects.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.

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.

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.

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).

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.

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.

Integration of genome-wide association studies reveal loci associated with salt tolerance score of rice at the seedling stage.

Salt threatens rice cultivation in many countries. Hence, breeding new varieties with high salt tolerance is important.A panel of 2,391 rice accessions from the 3 K Rice Genome Project was selected to evaluate salt tolerance via the standard evaluation score (SES) in hydroponics under 60 mM NaCl at the seedling stage. Three sub-population panels including 1,332, 628, and 386 accessions from the original 2,391 ones were constructed based on low relatedness revealed by a phylogenetic tree generated by Archaeopteryx Tree. A genome-wide association study (GWAS) was conducted on the entire and sub-population panels using SES data and a selection of 5, 10, 20, and 40% of SNPs selected from the original 1,011,601 SNPs by filtering minor allele frequency > 5% and missing rate < 5%. To perform GWAS, three methods implemented in three different software packages were utilized.Using the integration of GWAS programs, a total of four QTLs associated with SES scores were identified in different panels. Some QTLs co-located with previously detected QTL-related traits. qSES1.1 was detected in three panels, qSES1.3 and qSES2.1 in two panels, and qSES3.1 in one panel through GWAS by all three methods used and selected SNPs. These four QTLs were selected to detect candidate genes. Combining gene-based association study plus haplotype analysis in the entire population and the three sub-populations let us shortlist three candidate genes, viz. LOC_Os01g23640 and LOC_Os01g23680 for qSES1.1, and LOC_Os01g71240 for qSES1.3 region affecting salt tolerance. The identified QTLs and candidate genes provided useful materials and genetic information for future functional characterization and genetic improvement of salt tolerance in rice.

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.

Childhood Bacterial Meningitis Surveillance in Southern Vietnam: Trends and Vaccination Implications From 2012 to 2021.

Background: This retrospective hospital-based surveillance aimed to assess the epidemiology, causative pathogens trend, and serotypes distribution of pneumococcal meningitis among children aged under 5 years with bacterial meningitis in Southern Vietnam after the introduction of pentavalent vaccine in the Expanded Program on Immunization (EPI). Methods: From 2012 to 2021, cerebrospinal fluid samples were collected from children aged under 5 years with suspected bacterial meningitis at Children's Hospitals 1 and 2 in Ho Chi Minh City. Probable bacterial meningitis (PBM) cases were identified using biochemistry and cytology. Real-time polymerase chain reaction was used to confirm cases of confirmed bacterial meningitis (CBM) caused by Streptococcus pneumoniae, Haemophilus influenzae, or Neisseria meningitidis. Streptococcus pneumoniae serotyping was performed. Results: Of the 2560 PBM cases, 158 (6.2%) were laboratory-confirmed. The CBM proportion decreased during the 10-year study and was associated with age, seasonality, and permanent residence. Streptococcus pneumoniae was the most common pathogen causing bacterial meningitis (86.1%), followed by H influenzae (7.6%) and N meningitidis (6.3%). The case-fatality rate was 8.2% (95% confidence interval, 4.2%-12.2%). Pneumococcal serotypes 6A/B, 19F, 14, and 23F were the most prevalent, and the proportion of pneumococcal meningitis cases caused by the 10-valent pneumococcal conjugate vaccine (PCV) serotypes decreased from 96.2% to 57.1% during the PCV eras. Conclusions: Streptococcus pneumoniae is the most frequent causative agent of bacterial meningitis in children aged under 5 years in Southern Vietnam over the last decade. Policymakers may need to consider introducing PCVs into the EPI to effectively prevent and control bacterial meningitis.

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.

i4mC-GRU: Identifying DNA N4-Methylcytosine sites in mouse genomes using bidirectional gated recurrent unit and sequence-embedded features.

N4-methylcytosine (4mC) is one of the most common DNA methylation modifications found in both prokaryotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps reveal unexplored physiological and pathological pathways. In this study, we propose an effective computational method called i4mC-GRU using a gated recurrent unit and duplet sequence-embedded features to predict potential 4mC sites in mouse (Mus musculus) genomes. To fairly assess the performance of the model, we compared our method with several state-of-the-art methods using two different benchmark datasets. Our results showed that i4mC-GRU achieved area under the receiver operating characteristic curve values of 0.97 and 0.89 and area under the precision-recall curve values of 0.98 and 0.90 on the first and second benchmark datasets, respectively. Briefly, our method outperformed existing methods in predicting 4mC sites in mouse genomes. Also, we deployed i4mC-GRU as an online web server, supporting users in genomics studies.

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.

Relevance of 18F-DOPA visual and semi-quantitative PET metrics for the diagnostic of Parkinson disease in clinical practice: a machine learning-based inference study.

PURPOSE: To decipher the relevance of visual and semi-quantitative 6-fluoro-(18F)-L-DOPA (18F-DOPA) interpretation methods for the diagnostic of idiopathic Parkinson disease (IPD) in hybrid positron emission tomography (PET) and magnetic resonance imaging. MATERIAL AND METHODS: A total of 110 consecutive patients (48 IPD and 62 controls) with 11 months of median clinical follow-up (reference standard) were included. A composite visual assessment from five independent nuclear imaging readers, together with striatal standard uptake value (SUV) to occipital SUV ratio, striatal gradients and putamen asymmetry-based semi-quantitative PET metrics automatically extracted used to train machine learning models to classify IPD versus controls. Using a ratio of 70/30 for training and testing sets, respectively, five classification models-k-NN, LogRegression, support vector machine, random forest and gradient boosting-were trained by using 100 times repeated nested cross-validation procedures. From the best model on average, the contribution of PET parameters was deciphered using the Shapley additive explanations method (SHAP). Cross-validated receiver operating characteristic curves (cv-ROC) of the most contributive PET parameters were finally estimated and compared. RESULTS: The best machine learning model (k-NN) provided final cv-ROC of 0.81. According to SHAP analyses, visual PET metric was the most important contributor to the model overall performance, followed by the minimum between left and right striatal to occipital SUV ratio. The 10-time cv-ROC curves of visual, min SUVr or both showed quite similar performance (mean area under the ROC of 0.81, 0.81 and 0.79, respectively, for visual, min SUVr or both). CONCLUSION: Visual expert analysis remains the most relevant parameter to predict IPD diagnosis at 11 months of median clinical follow-up in 18F-FDOPA. The min SUV ratio appears interesting in the perspective of simple semi-automated diagnostic workflows.

Head-to-Head Comparison Between Rabbit Sign and EANM/SNMMI Criteria for the 18F-DOPA Visual Assessment of Parkinsonian Syndromes in PET/MRI: A Multiple Expert-Based and Blinded Controlled Study.

PURPOSE: The aim of this study was to compare the diagnostic performance of the rabbit visual pattern versus the one endorsed by the EANM/SNMMI for the diagnosis of parkinsonian syndromes in PET/MRI. PATIENTS AND METHODS: The 18F-DOPA PET images of 129 consecutive patients (65 Park+ and 64 controls) with 1 year of clinical follow-up were reviewed independently by 5 experienced readers on the same imaging workstation, blinded to the final clinical diagnosis. Two visual methods were assessed independently, with several days to months of interval: the criteria endorsed by EANM/SNMMI and the "rabbit" shape of the striate assessed on 3D MIP images. The sensitivities, specificities, likelihood ratios, and predictive values of the 2 diagnostic tests were estimated simultaneously by using the "comparison of 2 binary diagnostic tests to a paired design" method. RESULTS: The estimated 95% confidence interval (CI) of sensitivities and specificities ranged from 49.4% to 76.5% and from 83.2% to 97.7%, respectively. The 95% CI estimates of positive and negative likelihood ratios ranged from 3.8 to 26.7 and from 0.26 to 0.56, respectively. The 95% CI estimates of the positive and negative predictive values ranged from 78.1% to 96.7% and from 60.3% to 81.4%, respectively. For all the parameters, no statistical difference was observed between the 2 methods (P > 0.05). The rabbit sign reduced the readers' discrepancies by 25%, while maintaining the same performance. CONCLUSIONS: The rabbit visual pattern appears at least comparable to the current EANM/SNMMI reference procedure for the assessment of parkinsonian syndromes in daily clinical practice, without the need of any image postprocessing. Further multicenter prospective studies would be of relevance to validate these findings.

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.

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 .

Electrocatalytic CO2 Reduction by [Re(CO)3Cl(3-(pyridin-2-yl)-5-phenyl-1,2,4-triazole)] and [Re(CO)3Cl(3-(2-pyridyl)-1,2,4-triazole)].

The exploration of novel electrocatalysts for CO2 reduction is necessary to overcome global warming and the depletion of fossil fuels. In the current study, the electrocatalytic CO2 reduction of [Re(CO)3Cl(N-N)], where N-N represents 3-(2-pyridyl)-1,2,4-triazole (Hpy), 3-(pyridin-2-yl)-5-phenyl-l,2,4-triazole (Hph), and 2,2'-bipyridine-4,4' dicarboxylic acidic (bpy-COOH) ligands, was investigated. In CO2-saturated electrolytes, cyclic voltammograms showed an enhancement of the current at the second reduction wave for all complexes. In the presence of triethanolamine (TEOA), the currents of Re(Hpy), Re(Hph), and Re(bpy-COOH) enhanced significantly by approximately 4-, 2-, and 5-fold at peak potentials of -1.60, -150, and -1.69 VAg/Ag+, respectively (in comparison to without TEOA). The reduction potential of Re(Hph) was less negative than those of Re(Hpy) and Re(COOH), which was suggested to cause its least efficiency for CO2 reduction. Chronoamperometry measurements showed the stability of the cathodic current at the second reduction wave for at least 300 s, and Re(COOH) was the most stable in the CO2-catalyzed reduction. The appearance and disappearance of the absorption band in the UV/vis spectra indicated the reaction of the catalyst with molecular CO2 and its conversion to new species, which were proposed to be Re-DMF + and Re-TEOA and were supposed to react with CO2 molecules. The CO2 molecules were claimed to be captured and inserted into the oxygen bond of Re-TEOA, resulting in the enhancement of the CO2 reduction efficiency. The results indicate a new way of using these complexes in electrocatalytic CO2 reduction.

The impact of the resumption of in-person school attendance on COVID-affected child abuse and neglect trends in Florida.

BACKGROUND: The COVID-19 pandemic has led to fewer child abuse and neglect (CAN) hotline calls, CAN investigations, and foster care entries across the U.S. OBJECTIVE: To determine if there were decreases in CAN hotline calls, CAN investigations, foster care entries, and foster care exits in Florida (the largest among the few states that publishes monthly public data on the four areas) after the pandemic began, and to determine if there was any amelioration of these trends in Florida once schools had reopened for in-person learning. METHODS: Secondary data analyses of administrative child welfare data from January 2010 to June 2021 from Florida were used. Spline regression equations were calculated for CAN hotline calls, CAN investigations, foster care entries, and foster care exits during three periods: 1) before the COVID-19 pandemic (January 2010 to February 2020); 2) after the pandemic, but before schools reopened (March 2020 to October 2020); and 3) after the pandemic and after schools reopened (November 2020 to June 2021). RESULTS: Prior to the pandemic, there was a non-statistically significant increase of 1.35 hotline calls per 100,000 children per month (p = .478), a statistically significant increase of 0.01 investigations per 100,000 children per month (p < .001), a non-statistically significant increase of 0.01 foster care entries per 100,000 children per month (p = .415), and a statistically significant increase of 0.03 foster care exits per 100,000 children per month (p < .05). Once the pandemic started in March 2020, there were statistically significant decreases of 136.02 hotline calls per 100,000 children per month (p < .001), 102.84 investigations per 100,000 children per month (p < .001), 6.32 foster care entries per 100,000 children per month (p < .001) and 5.75 foster care exits per 100,000 children per month (p < .01). Once all schools reopened for in-person learning in November 2020, there continued to be statistically significant decreases of 47.86 hotline calls per 100,000 children per month (p < .05), 6.38 foster care entries per 100,000 children per month (p < .001) and 6.53 foster care exits per 100,000 children per month (p < .001). This suggests that there were an estimated 34,374 fewer CAN hotline calls, 2338 children who did not enter foster care, and 2587 youth residing in foster care (YRFC) whose foster care exits were delayed. The delay in foster care exits suggests that YRFC in Florida had stayed a combined cumulative equivalent of 477.1 years longer in care. CONCLUSION: With the COVID-19 variants like Omicron continuing to wreak havoc in Florida, there will be a continued trend of decreasing CAN hotline calls, foster care entries and foster care exits even with in-person learning in all Florida schools. A real-time interoperable data system utilizing real-time predictive analytics must be developed in concert with the development of leaders and executives with advanced degrees in child welfare organizations who are able to maximize information from such systems.

Insights into the gene and protein structures of the CaSWEET family members in chickpea (Cicer arietinum), and their gene expression patterns in different organs under various stress and abscisic acid treatments.

'Sugars Will Eventually be Exported Transporters' (SWEETs) are a group of sugar transporters that play crucial roles in various biological processes, particularly plant stress responses. However, no information is available yet for the CaSWEET family in chickpea. Here, we identified all putative CaSWEET members in chickpea, and obtained their major characteristics, including physicochemical patterns, chromosomal distribution, subcellular localization, gene organization, conserved motifs and three-dimensional protein structures. Subsequently, we explored available transcriptome data to compare spatiotemporal transcript abundance of CaSWEET genes in various major organs. Finally, we studied the changes in their transcript levels in leaves and/or roots following dehydration and exogenous abscisic acid treatments using RT-qPCR to obtain valuable information underlying their potential roles in chickpea responses to water-stress conditions. Our results provide the first insights into the characteristics of the CaSWEET family members and a foundation for further functional characterizations of selected candidate genes for genetic engineering of chickpea.

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.