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Trang Thi Hanh Do,1 Quyen Thi Tu Bui,2 Bui Thi Thu Ha,3 Thi Minh Le,3 Vui Thi Le,3 Quynh-Chi Thai Nguyen,3 Kimberly Joyce Lakin,4 Tung Thanh Dang,5 Loi Van Bui,5 Thien Cong Le,5 An Thi Ha Tran,5 Hien Thi Thu Pham,5 Tuan Van Nguyen5 1Faculty of Environmental and Occupational Health, Hanoi University of Public Health, Hanoi, Vietnam; 2Faculty and Fundamental Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 3Faculty of Social and Behavioral Sciences, Hanoi University of Public Health, Hanoi, Vietnam; 4Nossal Institute for Global Health, Melbourne School of Population and Global Health, Melbourne, Victoria, Australia; 5The National Institute of Mental Health, Bach Mai Hospital, Hanoi, VietnamCorrespondence: Quyen Thi Tu Bui, Department of Biostatistics, Faculty of Fundamental Sciences, Hanoi University of Public Health, 1A Duc Thang Street, Bac Tu Liem District, Hanoi, Vietnam, Tel +84 912 225 245, Fax +84 24 6266 2385, Email btq@huph.edu.vnPurpose: Detection of antenatal common mental disorders in low-resource settings like Vietnam is important and requires a reliable, valid and practical screening tool. Currently, there is no such tool validated for use among pregnant women in Vietnam. This study aims to assess the validity of the Vietnamese version of the 20-item Self Reporting Questionnaire (SRQ-20) by evaluating its reliability, factorial structure, and performance in detecting common mental disorder (CMD) symptoms, thereby identifying the optimum cut-off score for CMD screening among pregnant women in Vietnam.Participants and Methods: A total of 210 pregnant women from four rural communes participated in a face-to-face interview using the Vietnamese version of the SRQ-20, followed by a clinical diagnostic interview based on ICD-10 diagnostic criteria of CMDs. The reliability of the SRQ-20 was assessed by calculating the scale’s Cronbach’s alpha to measure internal consistency. Factor analyses were undertaken to examine the factor structure of the instrument. The Receiver Operating Characteristic (ROC) curve analysis was performed to assess the performance of the SRQ-20 against the clinical diagnosis and to identify the optimum cut-off score.Results: Internal consistency was good, with a Cronbach’s alpha of 0.87. Factor analyses resulted in a 4-factor solution. The area under the ROC curve (AUC) for detection of CMDs was 0.90. The optimum cut-off score of the SRQ-20 for detection of CMD symptoms among Vietnamese pregnant women was 5/6.Conclusion: The Vietnamese version of the SRQ-20 has the capacity to detect CMDs among pregnant women effectively and is recommended for use as a screening tool for CMDs in antenatal care settings in Vietnam.Keywords: SRQ-20, screening, common mental disorders, pregnant women, Vietnam

Tu C Le,1 Jiali Zhai,2 Wei-Hsun Chiu,3 Phong A Tran,3,4 Nhiem Tran21School of Engineering, RMIT University, Melbourne, VIC 3001, Australia; 2School of Science, RMIT University, Melbourne, VIC 3001, Australia; 3School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4000, Australia; 4Interface Science and Materials Engineering group, School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, QLD 4000, AustraliaCorrespondence: Nhiem TranSchool of Science, RMIT University, 124 La Trobe Street, Melbourne VIC 3000, AustraliaTel +61 3 9925 2131Email Nhiem.tran@rmit.edu.auAbstract: Janus particles, which are named after the two-faced Roman god Janus, have two distinct sides with different surface features, structures, and compositions. This asymmetric structure enables the combination of different or even incompatible physical, chemical, and mechanical properties within a single particle. Much effort has been focused on the preparation of Janus particles with high homogeneity, tunable size and shape, combined functionalities, and scalability. With their unique features, Janus particles have attracted attention in a wide range of applications such as in optics, catalysis, and biomedicine. As a biomedical device, Janus particles offer opportunities to incorporate therapeutics, imaging, or sensing modalities in independent compartments of a single particle in a spatially controlled manner. This may result in synergistic actions of combined therapies and multi-level targeting not possible in isotropic systems. In this review, we summarize the latest advances in employing Janus particles as therapeutic delivery carriers, in vivo imaging probes, and biosensors. Challenges and future opportunities for these particles will also be discussed.Keywords: Janus particles, therapeutics, theranostics, imaging, sensing

A fluorinated thiol was systematically investigated against a diverse set of –ene- and –yne-bearing imidazole and imidazolium substrates in the pursuit of new cation-fluorinated, omniphobic (hydrophobic and oleophobic) ionic liquids for aerospace applications. Compounds were prepared using UV-initiated click chemistry at quantitative conversion and purified with relative ease. Seven ionic liquids were synthesized and have isolated yields ranging from 74% to >99%. Five of the new cation-fluorinated ionic liquids are hydrophobic and two are liquids at room temperature. In addition to hydrophobicity, the seven ionic liquids’ solubilities were qualitatively examined across a wide spectrum of conventional organic solvents. Of note, the majority of the prepared ionic liquids are insoluble in water and hexanes yet soluble in methanol, acetone, acetonitrile, and dichloromethane. In addition to intriguing solubility properties, the ionic liquids in this study have high densities without the incorporation of heavy atoms, ranging from 1.596 to 1.924 g cm−3. The fluorinated salts also show good short-term thermal stability in air, with decomposition temperatures ranging from 215 °C to 320 °C. Other relevant characterization methods include spectroscopy, thermal analysis, and physical property measurements where appropriate. The X-ray crystal structure of a divalent, cation-fluorinated ionic liquid from this study was also obtained and shows unique fluorous domains in the extended structure, providing valuable insight into the materials’ physical properties. This work highlights a family of compounds moving towards omniphobic ionic liquids yielding potential possibilities for future applications.

The influence of machine learning (ML) on scientific domains continues to grow, and the number of publications at the intersection of ML, CO 2 capture, and material science is growing rapidly. Approaches for building ML models vary in both objectives and the methods through which materials are represented (i.e., featurised). Featurisation based on descriptors, being a crucial step in building ML models, is the focus of this review. Metal organic frameworks, ionic liquids, and other materials are discussed in this paper with a focus on the descriptors used in the representation of CO 2 -capturing materials. It is shown that operating conditions must be included in ML models in which multiple temperatures and/or pressures are used. Material descriptors can be used to differentiate the CO 2 capture candidates through descriptors falling under the broad categories of charge and orbital, thermodynamic, structural, and chemical composition-based descriptors. Depending on the application, dataset, and ML model used, these descriptors carry varying degrees of importance in the predictions made. Design strategies can then be derived based on a selection of important features. Overall, this review predicts that ML will play an even greater role in future innovations in CO 2 capture.

Objective: Dietary supplements (DSs) are commonly used supplements among children to support disease treatment, however, overuse and inappropriate prescription of outpatient DS can negatively affect health and treatment costs. This study aimed to evaluate DS use in outpatient treatment in children., Methods: The present study used a cross-sectional descriptive retrospective method based on data from 188,579 outpatient prescriptions of children for 1 year., Results: DSs were commonly prescribed in pediatric patients ≤5 years old (63.48 %) with diagnosing respiratory, infectious, and gastrointestinal diseases (>50 %). DS costs account for a high proportion (24.86 %) of total outpatient prescription costs. In addition, pediatric prescriptions containing one DS product (88.16 %) showed a higher rate than prescriptions containing two DS products (11.84 %) with the main ingredients being vitamin D 3 (80.55 %), vitamin B 6 (63.01 %), vitamin B 1 (60.22 %), and vitamin B 2 (59.04 %), and calcium (51.0 %). The majority of DS products contained multivitamins (69.52 %) with DS containing ≥5 vitamins of 59.04 % and DS containing 2-4 vitamins of 10.48 %. Besides, non-compliance with DS prescribing guidelines for age (21.38 %), dose (14.13 %), and both age and dose (5.54 %) was found primarily in highly prescribed DSs (vitamin-amino acid-mineral ingredients), pediatric patients <2 years old, prescriptions containing one DS product or multivitamins, and respiratory disease group (J and R04-09 ICD). The univariate and multivariate regression analyses showed that vitamin-mineral products and groups <2 years old were significantly associated with guideline-adherent DS prescribing for age and/or dose. Meanwhile, foreign DSs and vitamin-mineral ingredients of prescription showed significant associations with guideline-adherent DS prescribing for age or dose., Conclusion: Instructions for DS use regarding recommended age and maximum dosage may not be carefully considered by doctors due to belonging to an over-the-counter medicine group. Therefore, there is a strong need for a national public policy to prevent and control overprescribing and inappropriate prescribing of DS., Competing Interests: Declaration of competing interest The authors stated that there was no conflict of interest associated with the publication and no financial support, which could have influenced the outcome., (Copyright © 2024 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)

3,4-methylenedioxymethamphetamine (MDMA) or publicly known as "ecstasy" is a drug abuse substance. Since antibodies that detect MDMA typically also recognize its chemical analogue, methamphetamine (METH), we identified antibodies specifically recognizing MDMA, but not METH, named 1bB11 and 1bF12, using phage display. The crystal structure of 1bB11 in complex with MDMA was determined at 3.2 Å resolution. Key interactions were found between the epoxide moiety of MDMA and S34 and Y36 of the light chain. Additional interaction with E33 of the heavy chain contributes to anchoring MDMA. Mutagenesis-based biochemical analysis confirmed the importance of these residues in MDMA binding. Comparing the structure of 1bB11 to a scFv6H4, which binds both METH and MDMA, revealed opposite binding orientations. Taken together, our data provides a structural framework for selective binding to MDMA by the 1bB11 antibody, paving a way to develop a highly specific antibody for diagnosis., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Geonho Cheon, Dahyun Hwang, Truc Chi Le and Sangho Lee have patent #10-2022-0184221 pending to Sungkyunkwan University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Significance: Functional near-infrared spectroscopy (fNIRS) is a promising alternative to functional magnetic resonance imaging for measuring brain activity, but the presence of hair reduces data quality., Aim: To improve research efficiency and promote wider subject inclusivity, we developed the "Mini Comb"-an attachment for commercial fNIRS head caps that can rapidly clear hair with a simple twisting motion., Approach: To test the utility of the Mini Comb on different hair types, we measured the clearance achieved with eight unique sliding leg extrusions on 10 wigged mannequins of various hair characteristics. Following mannequin testing, we recruited a total of 15 participants to evaluate the performance of the Mini Comb as pertains to the time needed to create clearance and the signal quality captured., Results: The Mini Comb achieves comparable signal-to-noise ratios (SNRs) as standard hair clearance procedures while reducing hair clearance time by nearly 50%. Importantly, group analysis revealed better SNR results when the Mini Comb sliding leg design is matched to hair type, suggesting that consideration of hair type is important when conducting fNIRS studies., Conclusions: The Mini Comb thus opens the door for the deployment of fNIRS for more widespread, inclusive, and comprehensive neuroimaging studies., (© 2024 The Authors.)

Phase change materials (PCMs) are materials that exhibit thermal response characteristics, allowing them to be utilized in the biological field for precise and controllable temperature regulation. Due to considerations of biosafety and the spatial limitations within human tissue, the amount of PCMs used in medical applications is relatively small. Therefore, researchers often augment PCMs with various materials to enhance their performance and increase their practical value. The dispersion of nanoparticles to modify the thermophysical properties of PCMs has emerged as a mature concept. This paper aims to elucidate the role of nanomaterials in addressing deficiencies and enhancing the performance of PCMs. Specifically, it discusses the dispersion methods and stabilization mechanisms of nanoparticles within PCMs, as well as their effects on thermophysical properties such as thermal conductivity, latent heat, and specific heat capacity. Furthermore, it explores how various nano-additives contribute to improved thermal conductivity and the mechanisms underlying enhanced latent heat and specific heat. Additionally, the potential applications of PCMs in biomedical fields are proposed. Finally, this paper provides a comprehensive analysis and offers suggestions for future research to maximize the utilization of nanomaterials in enhancing the thermophysical properties of PCMs for biomedical applications.

The application of statistical models has excellent potential to provide crucial information for mitigating the challenging issue of ozone (O 3 ) pollution by capturing its associations with explanatory variables, including reactive precursors (VOCs and NO X ) and meteorology. Considering the large contribution of O 3 in degrading the air quality of western Taiwan, three-year (2019-2021) hourly concentration data of VOC, NO X and O 3 from 4 monitoring stations of western Taiwan: Tucheng (TC), Zhongming (ZM), Taixi (TX) and Xiaogang (XG), was evaluated to identify the effect of anthropogenic emissions on O 3 formation. Owing to the high-ambient reactivity of VOCs on the underestimation of sources, photochemical oxidation was assessed to calculate the consumed VOC (VOC cons ) which was followed by the source identification of their initial concentrations. VOC cons was observed to be highest in the summer season (16.7 and 22.7 ppbC) at north (TC and ZM) and in the autumn season (17.8 and 11.4 ppbC) in southward-located stations (TX and XG, respectively). Results showed that VOCs from solvents (25-27%) were the major source at northward stations whereas VOCs-industrial emissions (30%) dominated in south. Furthermore, machine learning (ML): eXtreme Gradient Boost (XGBoost) model based de-weather analysis identified that meteorological factors favor to reduce ambient O 3 levels at TC, ZM and XG stations (-67%, -47% and -21%, respectively) but they have a major role in accumulating the O 3 (+38%) at the TX station which is primarily transported from the upwind region of south-central Taiwan. Crucial insights using ML outputs showed that the finding of the study can be utilized for region-specific data-driven control of emission from VOCs-sources and prioritized to limit the O 3 -pollution at the study location-ns as well as their accumulation in distant regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Ionic liquids (ILs) are a diverse class of solvents which can be selected for task-specific properties, making them attractive alternatives to traditional solvents. To tailor ILs for specific biological applications, it is necessary to understand the structure-property relationships of ILs and their interactions with cells. Here, a selection of carboxylate anion-based ILs were investigated as cryoprotectants, which are compounds added to cells before freezing to mitigate lethal freezing damage. The cytotoxicity, cell permeability, thermal behavior, and cryoprotective efficacy of the ILs were assessed with two model mammalian cell lines. We found that the biophysical interactions, including permeability of the ILs, were influenced by considering the IL pair together, rather than as single species acting independently. All of the ILs tested had high cytotoxicity, but ethylammonium acetate demonstrated good cryoprotective efficacy for both cell types tested. These results demonstrate that despite toxicity, ILs may be suitable for certain biological applications. It also demonstrates that more research is required to understand the contribution of ion pairs to structure-property relationships and that knowing the behavior of a single ionic species will not necessarily predict its behavior as part of an IL.

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM 2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM 2.5 by the LCS network. The calibrated PM 2.5 were shown to agree with reference PM 2.5 measured by the BAM-1020 with R 2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m 3 , which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM 2.5 concentrations in the urban area. Results showed that the highest average PM 2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM 2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM 2.5 . The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM 2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM 2.5 concentrations and formulating regional air pollution control strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Background: In 2022, many universities transitioned from virtual learning back to in-person teaching. Significant changes in working conditions, along with COVID-19-related anxiety during the pandemic, might impact the well-being of lecturers., Objective: This study aims to assess the perceived stress, self-rated physical and occupational health of Vietnamese lecturers in 2022 compared to the pre-pandemic levels, and to identify their associating factors., Method: This is a cross-sectional study conducted in May-June 2022 through an online survey questionnaire. A sample of 425 university lecturers were included. Participants' stress levels were assessed using the Perceived Stress Scale. Participants were asked to compare their current physical and occupational health with before the pandemic. Explanatory variables included (1) COVID-19 exposure; and (2) perceptions of changes in working environment due to the pandemic. Multivariable ordinal least squared regression analyses were used to estimate the coefficient and 95% confidence intervals for the associations between COVID-19 exposure variables, work conditions, and each outcome., Results: Our findings indicated that over 60% of participants experienced moderate stress, while nearly half reported worsened physical and occupational health during the pandemic. Gender, age, perceptions of workload, workplace safety, COVID-19 infection, and incentive programs were significant factors associated with higher stress levels and worse self-rated health., Conclusion: To ensure occupational safety and support lecturers as they return to in-person classes, it is crucial to provide appropriate psychological and health support services.

Trees structure the Earth's most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations 1-6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth's 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories 7 , we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world's most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees., (© 2024. The Author(s).)

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO 2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs 2 AgBiBr 6 perovskites. Machine learning models determined the importance of d 10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs 2 AgBiBr 6 . Conspicuously, d 10 -configured Zn 2+ boosted the photoactivity of Cs 2 AgBiBr 6 . Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d 10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure-property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications., (© 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO 2 capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO 2 adsorption, with an R 2 of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks' partial charges based on the expected CO 2 uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO 2 capture using pseudo-classification predictions., (© 2023. Springer Nature Limited.)

The study aimed to investigate the PM 2.5 variations in different periods of COVID-19 control measures in Northern Taiwan from Quarter 1 (Q1) 2020 to Quarter 2 (Q2) 2021. PM 2.5 sources were classified based on long-range transport (LRT) or local pollution (LP) in three study periods: one China lockdown (P1), and two restrictions in Taiwan (P2 and P3). During P1 the average PM 2.5 concentrations from LRT (LRT-PM 2.5 - P1 ) were higher at Fuguei background station by 27.9% and in the range of 4.9-24.3% at other inland stations compared to before P1. The PM 2.5 from LRT/LP mix or pure LP (Mix/LP-PM 2.5 - P1 ) was also higher by 14.2-39.9%. This increase was due to higher secondary particle formation represented by the increase in secondary ions (SI) and organic matter in PM 2.5-P1 with the largest proportion of 42.17% in PM 2.5 from positive matrix factorization (PMF) analysis. A similar increasing trend of Mix/LP-PM 2.5 was found in P2 when China was still locked down and Taiwan was under an early control period but the rapidly increasing infected cases were confirmed. The shift of transportation patterns from public to private to avoid virus infection explicated the high correlation of the increasing infected cases with the increasing PM 2.5 . In contrast, the decreasing trend of LP-PM 2.5-P3 was observed in P3 with the PM 2.5 biases of ∼45% at all the stations when China was not locked down but Taiwan implemented a semi-lockdown. The contribution of gasoline vehicle sources in PM 2.5 was reduced from 20.3% before P3 to 10% in P3 by chemical signatures and source identification using PMF implying the strong impact of strict control measures on vehicle emissions. In summary, PM 2.5 concentrations in Northern Taiwan were either increased (P1 and P2) or decreased (P3) during the COVID-19 pandemic depending on control measures, source patterns and meteorological conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This study assessed the machine learning based sensitivity analysis coupled with source-apportionment of volatile organic carbons (VOCs) to look into new insights of O 3 pollution in Yunlin County located in central-west region of Taiwan. One-year (Jan 1 to Dec 31, 2021) hourly mass concentrations data of 54 VOCs, NO X , and O 3 from 10 photochemical assessment monitoring stations (PAMs) in and around the Yunlin County were analyzed. The novelty of the study lies in the utilization of artificial neural network (ANN) to evaluate the contribution of VOCs sources in O 3 pollution in the region. Firstly, the station specific source-apportionment of VOCs were carried out using positive matrix factorization (PMF)-resolving six sources viz. AAM: aged air mass, CM: chemical manufacturing, IC: Industrial combustion, PP: petrochemical plants, SU: solvent use and VE: vehicular emissions. AAM, SU, and VE constituted cumulatively more than 65% of the total emission of VOCs across all 10 PAMs. Diurnal and spatial variability of source-segregated VOCs showed large variations across 10 PAMs, suggesting for distinctly different impact of contributing sources, photo-chemical reactivity, and/or dispersion due to land-sea breezes at the monitoring stations. Secondly, to understand the contribution of controllable factors governing the O 3 pollution, the output of VOCs source-contributions from PMF model along with mass concentrations of NO X were standardized and first time used as input variables to ANN, a supervised machine learning algorithm. ANN analysis revealed following order of sensitivity in factors governing the O 3 pollution: VOCs from IC > AAM > VE ≈ CM ≈ SU > PP ≈ NO X . The results indicated that VOCs associated with IC (VOCs-IC) being the most sensitive factor which need to be regulated more efficiently to quickly mitigate the O 3 pollution across the Yunlin County., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Halide perovskites have attracted enormous attention due to their potential applications in optoelectronics and photocatalysis. However, concerns over their instability, toxicity, and unsatisfactory efficiency have necessitated the development of lead-free all-inorganic halide perovskites. A major challenge in designing efficient halide perovskites for practical applications is the lack of effective methods for producing nanocrystals with precise size and shape control. In this work, a layered perovskite, Cs 4 ZnSb 2 Cl 12 (CZS), is found from calculations to exhibit size- and facet-dependent optoelectronic properties in the nanoscale, and thus, a colloidal method is used to synthesize the CZS nanoparticles with size-tunable morphologies: zero- (nanodots), one- (nanowires and nanorods), two- (nanoplates), and three-dimensional (nanopolyhedra). The growth kinetics of the CZS nanostructures, along with the effects of surface ligands, reaction temperature, and time were investigated. The optoelectronic properties of the nanocrystals varied with size due to quantum confinement effects and with shape due to anisotropy within the crystals and the exposure of specific facets. These properties could be modulated to enhance the visible-light photocatalytic performance for toluene oxidation. In particular, the 9.7 nm CZS nanoplates displayed a toluene to benzaldehyde conversion rate of 1893 μmol g -1 h -1 (95% selectivity), 500 times higher than the bulk synthesized CZS, and comparable with the reported photocatalysts. This study demonstrates the integration of theoretical calculations and synthesis, revealing an approach to the design and fabrication of novel, high-performance colloidal perovskite nanocrystals for optoelectronic and photocatalytic applications.

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The southern white-cheeked gibbon Nomascus siki is endemic to Indochina and is classified as critically endangered on the International Union for Conservation of Nature (IUCN) Red List. The most updated information on the status of this species dates back to a decade ago. As hunting has tremendous impacts on wildlife in Southeast Asia, the population of N. siki might have changed a lot in the last decade. Updated information on the status and potential distribution of this species is critically important for conservation and prioritization, especially for N. siki because of its undefined distribution range. The goal of this study was to review the population status of N. siki in Vietnam and Lao People's Democratic Republic (PDR) and to model its potential distribution. In Vietnam, this species has been intensively surveyed in all major areas of occurrence from 2016 to 2021. The total number of N. siki groups recorded and estimated in Vietnam were 324 and 483, respectively. In Lao PDR, the occurrence of N. siki has been confirmed in Nam Kading, Nakai Nam Theun, Hin Nam No, and Phou Hinpoun national protected areas. However, population estimates are generally lacking. The suitable habitat of N. siki was predicted from about 105.00° to 106.80° E longitude and from about 16.60° to 17.90° N latitude located in Quang Binh and Quang Tri provinces (Vietnam), and Khammounan and Savannakhet provinces (Lao PDR). The area of the potential distribution range is about 9894.15 km 2 , both in Vietnam and Lao PDR. Particularly, the high, medium, and low suitable habitats were estimated at around 1229.58 km 2 , 3019.68 km 2 , and 5644.89 km 2 , respectively. The area of suitable habitat of N. siki in Vietnam was predicted to be 4151.25 km 2 , of which only 1257.93 km 2 (30.30%) is in the protected area network. Dong Chau-Khe Nuoc Trong and Bac Huong Hoa Nature Reserves, and Phong Nha-Ke Bang National Park should receive priority for conservation of N. siki in Vietnam. Improving conservation beyond the protected areas' boundaries or transforming the forest enterprises and watershed protection forests into protected areas should also be considered as an alternative for the conservation of N. siki. In Lao PDR, surveys of the species in its entire distribution range should be the first priority., (© 2023. The Author(s), under exclusive licence to Japan Monkey Centre.)

The Mekong River Delta in Vietnam, is concerned by numerous microplastic-related issues such as a lack of wastewater treatment facilities and mismanagement of plastic waste released from agriculture, aquaculture and related activities. This study aimed to examine the presence of microplastics in surface water and sediment by collecting samples from six sites along the Tien River and its distributaries in February 2019. The results showed that the average concentration of microplastics over the entire area was 53.8 ± 140.7 items m -3 in surface water and 6.0 ± 2.0 items g -1 dried weight in sediment, with a predominance of microplastic fibres rather than fragments, respectively 85 % and 98 % in surface water and sediment. In the main flow of surface water, the concentration of microplastics was stable; however, in the sediment, microplastic concentration was affected by the high dynamic flow regime rather than the sources where microplastics are released., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Many natural forests in Southeast Asia are degraded following decades of logging. Restoration of these forests is delayed by ongoing logging and tropical cyclones, but the implications for recovery are largely uncertain. We analysed meteorological, satellite and forest inventory plot data to assess the effect of Typhoon Doksuri, a major tropical cyclone, on the forest landscapes of central Vietnam consisting of natural forests and plantations. We estimated the return period for a cyclone of this intensity to be 40 years. Plantations were almost twice as likely to suffer cyclone damage compared to natural forests. Logged natural forests (9-12 years after cessation of government-licensed logging) were surveyed before and after the storm with 2 years between measurements and remained a small biomass carbon sink (0.1 ± 0.3 Mg C ha -1 yr -1 ) over this period. The cyclone reduced the carbon sink of recovering natural forests by an average of 0.85 Mg C ha -1 yr -1 , less than the carbon loss due to ongoing unlicensed logging. Restoration of forest landscapes in Southeast Asia requires a reduction in unlicensed logging and prevention of further conversion of degraded natural forests to plantations, particularly in landscapes prone to tropical cyclones where natural forests provide a resilient carbon sink. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

Objective: To evaluate a pharmacist-led intervention's effectiveness in reducing drug-related problems (DRPs ( related to prescriptions for pediatric outpatients., Methods: We conducted a randomized controlled trial. We recruited and randomly assigned 31 physicians to control or intervention groups. We collected 775 prescriptions (375 from the control group and 400 from the intervention group) at the start. For 3 weeks, intervention physicians received additional information and meetings with pharmacists in addition to the usual practices of the hospital. We then collected prescriptions at the end of the study. We classified DRPs, based on reliable references (Supplemental Table S1) at baseline and endpoint (a week after the intervention). The primary outcome was the proportion of prescriptions with DRPs, and secondary outcomes were the proportions of prescriptions with specific DRP types., Results: The influence of the intervention on general DRPs and specific DRPs was the study's main finding. The pharmacist-led intervention helped reduce the prescriptions with DRPs proportion in the intervention group to 41.0%, compared with 49.3% in the control group (p < 0.05). The DRPs proportion related to the timing of administration relative to meals, unlike the other DRP types, increased in the control group (from 31.7% to 34.9%) and decreased in the intervention group (from 31.3% to 25.3%), with a significant difference between the 2 groups at endpoint (p < 0.01). Patients aged >2 to ≤6 years (OR, 1.871; 95% CI, 1.340-2.613) and receiving ≥5 drugs (OR, 5.037; 95% CI, 2.472-10.261) were at greater risk of experiencing DRPs related to prescribing., Conclusions: A pharmacist-led intervention improved DRP occurrence related to physicians' prescribing. Pharmacists could be involved in in-depth research with physicians in the prescribing process to provide tailored interventions., Competing Interests: Disclosures. The author declares that there is no relevant or material financial interests that relate to the research described in this paper., (Copyright. Pediatric Pharmacy Association. All rights reserved. For permissions, email: membership@pediatricpharmacy.org 2023.)

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Anthocyanins obtained from jambolan have been used as active agents in different carboxymethyl starch-based tablet formulations and their release profiles evaluated in simulated gastric fluids (SGF) and simulated intestinal (SIF) fluids. Structural analysis highlighted a strong interaction between anthocyanins and carboxymethyl starch, evidenced by scanning electron microscopy and infrared analysis. Tablet dissolution behavior varied according to the pH of the media, being controlled by the swelling and/or erosion of the polymeric matrix. Various formulations for immediate, fast, and sustained release of anthocyanins for 30 min, 2 h and 12 h of dissolution have been developed. It was found that monolithic carboxymethyl starch tablets loaded with powdered jambolan extract efficiently afforded the complete delivery (100% of anthocyanins) to different sites of the simulated gastrointestinal tract and ensured the stability of these pigments, which maintained their antioxidant activity., Competing Interests: The authors declare no conflict of interest.

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020., Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts., Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33-0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07-0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23-2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20-76.70) or unverified (OR = 5.03; 95% CI, 1.66-15.24)., Conclusions: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online "infodemics" to inform public health responses.

Microplastic fibres are the most abundant microplastics in waterways worldwide. The settling of fibres is distinct from other particles because of their aspect ratio and shape. In this paper, we test the hypothesis that length, curliness, and settling orientation control the settling velocity of microplastic fibres in a suite of laboratory experiments. Using a Particle Tracking Velocimetry method, we measured the settling velocity of 683 polyester microplastic fibres of 1 to 4 mm in length. Experimental findings support our hypothesis that for microplastic fibre longer than 1 mm, changing settling orientation from horizontal to vertical can increase 1.7 times the settling velocity. Fibre curliness can significantly reduce the settling velocity, where a curly fibre 1.3 times longer than a straight fibre can settle 1.75 times slower. In contrast, short microplastic fibres (less than 1 mm) mostly settle horizontally, and their settling velocity is unaffected by curliness. The drag force exerting on settling microplastic fibres was analysed, and the sphere-equivalent diameter was found to be a good representation of microplastic fibre size to predict the drag coefficient. Measured settling velocity ranges between 0.1 and 0.55 mm/s and exhibits a slight increase with the increasing length of the fibres. This low-velocity range raises concerns that microplastic fibres can favour biological flocculation, form clustered aggregates with microorganisms, feed aquatic organisms and cause bioaccumulation at higher trophic levels., Competing Interests: Declaration of competing interest The author declares that there are no competing interests., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Tree diversity in Asia's tropical and subtropical forests is central to nature-based solutions. Species vulnerability to multiple threats, which affect provision of ecosystem services, is poorly understood. We conducted a region-wide, spatially explicit assessment of the vulnerability of 63 socioeconomically important tree species to overexploitation, fire, overgrazing, habitat conversion, and climate change. Trees were selected for assessment from national priority lists, and selections were validated by an expert network representing 20 countries. We used Maxent suitability modeling to predict species distribution ranges, freely accessible spatial data sets to map threat exposures, and functional traits to estimate threat sensitivities. Species-specific vulnerability maps were created as the product of exposure maps and sensitivity estimates. Based on vulnerability to current threats and climate change, we identified priority areas for conservation and restoration. Overall, 74% of the most important areas for conservation of these trees fell outside protected areas, and all species were severely threatened across an average of 47% of their native ranges. The most imminent threats were overexploitation and habitat conversion; populations were severely threatened by these factors in an average of 24% and 16% of their ranges, respectively. Our model predicted limited overall climate change impacts, although some study species were likely to lose over 15% of their habitat by 2050 due to climate change. We pinpointed specific natural areas in Borneo rain forests as hotspots for in situ conservation of forest genetic resources, more than 82% of which fell outside designated protected areas. We also identified degraded areas in Western Ghats, Indochina dry forests, and Sumatran rain forests as hotspots for restoration, where planting or assisted natural regeneration will help conserve these species, and croplands in southern India and Thailand as potentially important agroforestry options. Our results highlight the need for regionally coordinated action for effective conservation and restoration., (© 2021 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

Background: Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic., Methods: We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve. We explored the associations between engagement, sentiment polarity, and other characteristics of online information with different outbreak phases using Poisson regression and multinomial logistic regression analysis. We assessed the frequency of keywords over time, and conducted a semantic analysis of keywords using word segmentation., Results: We found a close association between collected online information and the evolution of the COVID-19 situation in Vietnam. Online information generated higher engagements during compared to before the outbreak. There was a close relationship between sentiment polarity and posts' topics: the emotional tendencies about COVID-19 mortality were significantly more negative, and more neutral or positive about COVID-19 incidence. Online newspaper reported significantly more information in negative or positive sentiment than online forums or social media. Most topics of public concern followed closely the progression of the COVID-19 situation during the outbreak: development of the global pandemic and vaccination; the unfolding outbreak in Vietnam; and the subsiding of the outbreak after two months., Conclusion: This study shows how online information can reflect a public health threat in real time, and provides important insights about public awareness and perception during different outbreak phases. Our findings can help public health decision makers in Vietnam and other low and middle income countries with high internet penetration rates to design more effective communication strategies during critical phases of an epidemic., Competing Interests: The authors have declared that no competing interests exist.

Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O 2 and N 2 uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O 2 /N 2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r 2 correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O 2 /N 2 separation based on the adsorption and diffusion selectivity.

A cyclic depsipeptide, nobilamide I ( 1 ), along with the known peptide A-3302-B/TL-119 ( 2 ), was isolated from the saline cultivation of the marine-derived bacterium Saccharomonospora sp., strain CNQ-490. The planar structure of 1 was elucidated by interpretation of 1D and 2D NMR and MS spectroscopic data. The absolute configurations of the amino acids in 1 were assigned by using the C 3 Marfey's analysis and comparing them with those of 2 based on their biosynthetic pathways. Nobilamide I ( 1 ) decreased cell motility by inhibiting epithelial-mesenchymal transition markers in A549 (lung cancer), AGS (gastric cancer), and Caco2 (colorectal cancer) cell lines. In addition, 1 modulated the expression of the matrix metalloproteinase (MMP) family (MMP2 and MMP9) in the three cell lines., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Anthropogenic fibres are an emerging pollutant worldwide. The Can Gio mangrove area is located downstream of the Saigon River, and is characterised by high level of anthropogenic fibres originating from domestic and industrial textile and apparel manufacturing. In this area, biota is thus subjected to a high potential risk of anthropogenic fibre contamination. This study aims to characterise the accumulation of anthropogenic fibres in different tissues, i.e. gills, digestive systems, and remaining tissues, of white clams (Meretrix lyrata) cultivated in the Can Gio beach sand, during a seven-month sampling period. The results showed an average concentration of 3.6 ± 2.1 fibres individual -1 or 2.7 ± 2.4 fibres g -1 ww. Higher fibre accumulation was observed in remaining tissues than in gills and digestive systems, and no temporal variation was observed in all clam tissues. The intake of fibres by humans consuming clams was estimated to be 324 fibres inhabitant -1  yr -1 ., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Analysis of the chemical components from the culture broth of the marine bacterium Saccharomonospora sp. CNQ-490 has yielded three novel compounds: saccharobisindole ( 1 ), neoasterric methyl ester ( 2 ), and 7-chloro-4( 1H )-quinolone ( 3 ), in addition to acremonidine E ( 4 ), pinselin ( 5 ), penicitrinon A ( 6 ), and penicitrinon E ( 7 ). The chemical structures of the three novel compounds were elucidated by the interpretation of 1D, 2D nuclear magnetic resonance (NMR), and high-resolution mass spectrometry (HRMS) data. Compound 2 generated weak inhibition activity against Bacillus subtilis KCTC2441 and Staphylococcus aureus KCTC1927 at concentrations of 32 μg/mL and 64 μg/mL, respectively, whereas compounds 1 and 3 did not have any observable effects. In addition, compound 2 displayed weak anti-quorum sensing (QS) effects against S. aureus KCTC1927 and Micrococcus luteus SCO560.

Short-chain alcohols (i.e., ethanol) can induce membrane interdigitation in saturated-chain phosphatidylcholines (PCs). In this process, alcohol molecules intercalate between phosphate heads, increasing lateral separation and favoring hydrophobic interactions between opposing acyl chains, which interpenetrate forming an interdigitated phase. Unraveling mechanisms underlying the interactions between ethanol and model lipid membranes has implications for cell biology, biochemistry, and for the formulation of lipid-based nanocarriers. However, investigations of ethanol-lipid membrane systems have been carried out in deionized water, which limits their applicability. Here, using a combination of small- and wide-angle X-ray scattering, small-angle neutron scattering, and all-atom molecular dynamics simulations, we analyzed the effect of varying CaCl 2 and NaCl concentrations on ethanol-induced interdigitation. We observed that while ethanol addition leads to the interdigitation of bulk phase 1,2-dipalmitoyl- sn -glycero-3-phosphocholine (DPPC) bilayers in the presence of CaCl 2 and NaCl regardless of the salt concentration, the ethanol-induced interdigitation of vesicular DPPC depends on the choice of cation and its concentration. These findings unravel a key role for cations in the ethanol-induced interdigitation of lipid membranes in either bulk phase or vesicular form.

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H 2 evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H 2 evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

Microplastics in atmospheric fallouts from a developing megacity influenced by a tropical monsoon climate were investigated during a year. Three sites were selected according to the surrounding population density, the land use and occupation. The microplastic deposition fluxes varied between of 71-917 items m -2 d -1 , for an observation size range of 300-5000 μm. Fibers predominated while fragments were observed occasionally. Unexpectedly, contrary to available scientific literature, deposition fluxes did not vary temporally with rainfall and wind intensity or direction, showing no effect of the tropical climate. Variations were observed between sites and were attributed to their environmental characteristics: population density and occupation space. The median length of fibers also differed between sites and could be related to in-situ fragmentation processes due to occupation space (solid waste treatment facility). Those first results from tropical climate region are showing interesting insights and are opening new perspectives on the understanding of microplastics fate from atmospheric fallouts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Genetic screens provide a mechanism to identify genes involved with different cellular and organismal processes. Using a Flp/FRT screen in the Drosophila eye we identified mutations that result in alterations and de-regulation of cell growth and division. From this screen a group of undergraduate researchers part of the Fly-CURE consortium mapped and characterized a new allele of the gene Hippo , Hpo N.1.2 ., (Copyright: © 2021 by the authors.)

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.

Antimalarial agents used as monotherapy are increasingly ineffective due to the emergence of Plasmodium resistant strains. Artemisinin (Arte), extracted from Artemisia annua, presents a good efficiency against the Plasmodium strains and is currently used to treat malaria. To avoid the appearance of new resistant strains to artemisinin, the use of Artemisinin-based Combination Therapy (ACT) with another antimalaria agent was recommended by WHO to provide an effective cure and delayed resistance. Although combined formulations of various drugs with Artemisinin have been developed, their release is immediate, and they require multiple doses with side detrimental effects and effectiveness still desired. To improve its efficiency, controlled release formulations were developed to ensure long-term antiplasmodial activity by associating Artemisinin with a natural antimalarial agent extracted from Peschiera fuchsiaefolia (Pf). The Pf extract (containing mostly low soluble alkaloids) was complexed with carboxymethylcellulose to improve its solubility and stability. Two formulation types are reported. As bilayer tablet dosage form, the kinetic release pattern was an immediate release of Artemisinin, followed by a slow sustained release of Pf for 12 h. As monolithic tablet, the release profile shows a simultaneous sustained release of the two active agents, about of 10 h for Arte and 12 h for Pf., (Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

In aquatic environments, assessment of microplastic concentrations is increasing worldwide but environments from developing countries remain under-evaluated. Due to disparities of facilities, financial resources and human resources between countries, protocols of sampling, analysis and observations used in developed countries cannot be fully adapted in developing ones, and required specific adaptations. In Viet Nam, an adapted methodology was developed and commonly adopted by local researchers to implement a microplastic monitoring in sediments and surface waters of 21 environments (rivers, lakes, bays, beaches) of eight cities or provinces. Microplastic concentrations in surface waters varied from 0.35 to 2522 items m-3, with the lowest concentrations recorded in the bays and the highest in the rivers. Fibers dominated over fragments in most environments (from 47% to 97%). The microplastic concentrations were related to the anthropogenic pressure on the environment, pointing out the necessity in a near future to identify the local sources of microplastics., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: Opioid-induced constipation (OIC) is the most common side effect in patient-prescribed opioids for cancer pain treatment. Current guidelines recommend routine prescription of a laxative for preventing OIC in all patients prescribed an opioid unless a contraindication exists. We determined patterns of prescription of laxative agents in patients with lung cancer initiating opioids., Methods: We performed a retrospective cohort study evaluating the prescription of laxatives for OIC to adult patients with incident lung cancer seen in the Veteran's Affairs (VA) system, between January 1, 2003, and December 31, 2016. Exposure to laxative agents was categorized as follows: none, docusate monotherapy, docusate plus another laxative, and other laxatives only. Prevalence of OIC prophylaxis was analyzed using descriptive statistics. Linear regression was performed to identify time trends in the prescription of OIC prophylaxis., Results: Overall, 130,990 individuals were included in the analysis. Of these, 87% of patients received inadequate prophylaxis (75% no prophylaxis and 12% docusate alone), while 5% received OIC prophylaxis with the unnecessary addition of docusate to another laxative. Through the study period, laxative prescription significantly decreased, while all other categories of OIC prophylaxis were unchanged. We noted an inverse relationship with OIC prophylaxis and likelihood of a diagnosis of constipation at 3 and 6 months., Conclusions: In this study of veterans with lung cancer, almost 90% received inadequate or inappropriate OIC prophylaxis. Efforts to educate physicians and patients to promote appropriate OIC prophylaxis in combination with systems-level changes are warranted.