Epithelial RAC1-dependent cytoskeleton dynamics controls cell mechanics, cell shedding and barrier integrity in intestinal inflammation.

OBJECTIVE: Increased apoptotic shedding has been linked to intestinal barrier dysfunction and development of inflammatory bowel diseases (IBD). In contrast, physiological cell shedding allows the renewal of the epithelial monolayer without compromising the barrier function. Here, we investigated the role of live cell extrusion in epithelial barrier alterations in IBD. DESIGN: Taking advantage of conditional GGTase and RAC1 knockout mice in intestinal epithelial cells (Pggt1b iΔIEC and Rac1 iΔIEC mice), intravital microscopy, immunostaining, mechanobiology, organoid techniques and RNA sequencing, we analysed cell shedding alterations within the intestinal epithelium. Moreover, we examined human gut tissue and intestinal organoids from patients with IBD for cell shedding alterations and RAC1 function. RESULTS: Epithelial Pggt1b deletion led to cytoskeleton rearrangement and tight junction redistribution, causing cell overcrowding due to arresting of cell shedding that finally resulted in epithelial leakage and spontaneous mucosal inflammation in the small and to a lesser extent in the large intestine. Both in vivo and in vitro studies (knockout mice, organoids) identified RAC1 as a GGTase target critically involved in prenylation-dependent cytoskeleton dynamics, cell mechanics and epithelial cell shedding. Moreover, inflamed areas of gut tissue from patients with IBD exhibited funnel-like structures, signs of arrested cell shedding and impaired RAC1 function. RAC1 inhibition in human intestinal organoids caused actin alterations compatible with arresting of cell shedding. CONCLUSION: Impaired epithelial RAC1 function causes cell overcrowding and epithelial leakage thus inducing chronic intestinal inflammation. Epithelial RAC1 emerges as key regulator of cytoskeletal dynamics, cell mechanics and intestinal cell shedding. Modulation of RAC1 might be exploited for restoration of epithelial integrity in the gut of patients with IBD.

Willingness of Healthcare Students in Vietnam to Volunteer During the COVID-19 Pandemic.

Healthcare students play an important role in volunteering activity, often addressing staff shortages. However, during the COVID-19 pandemic, the willingness of students to volunteer in contribution to the pandemic response, especially in Vietnam, has not been thoroughly investigated. This study aimed to determine the prevalence of and factors associated with the willingness of healthcare students to volunteer during the COVID-19 pandemic in Vietnam. For this, an online cross-sectional survey was conducted, between June 7th and July 6th, 2021, among healthcare students from 10 fields of study at the largest public university of medicine and pharmacy in the Mekong Delta, Vietnam. Of 2032 respondents, 1473 (72.5%) reported that they would be willing to volunteer during the COVID-19 pandemic. More than half of the students reported having a desire to volunteer in non-patient contact activities such as data entry (65.9%) and logistics (57.7%). Whereas less than 50% of the participants were willing to volunteer with activities involving patients. Year of education, study field, educational format, living arrangements, health status self-perception, chronic illness possession, COVID-19 fear level, past volunteering experience in non-healthcare sectors, and COVID-19 prevention and control training course attendance were all associated with a willingness to volunteer. The strongest barriers preventing volunteering included fear for the health of their family and lack of training/knowledge. Conclusively, healthcare students reported a high level of willingness, indicating a positive attitude toward responding to the COVID-19 pandemic. Adequate training should be employed to increase the willingness among healthcare students in Vietnam.

Impact of Epithelial Cell Shedding on Intestinal Homeostasis.

The gut barrier acts as a first line of defense in the body, and plays a vital role in nutrition and immunoregulation. A layer of epithelial cells bound together via intercellular junction proteins maintains intestinal barrier integrity. Based on a tight equilibrium between cell extrusion and cell restitution, the renewal of the epithelium (epithelial turnover) permits the preservation of cell numbers. As the last step within the epithelial turnover, cell shedding occurs due to the pressure of cell division and migration from the base of the crypt. During this process, redistribution of tight junction proteins enables the sealing of the epithelial gap left by the extruded cell, and thereby maintains barrier function. Disturbance in cell shedding can create transient gaps (leaky gut) or cell accumulation in the epithelial layer. In fact, numerous studies have described the association between dysregulated cell shedding and infection, inflammation, and cancer; thus epithelial cell extrusion is considered a key defense mechanism. In the gastrointestinal tract, altered cell shedding has been observed in mouse models of intestinal inflammation and appears as a potential cause of barrier loss in human inflammatory bowel disease (IBD). Despite the relevance of this process, there are many unanswered questions regarding cell shedding. The investigation of those mechanisms controlling cell extrusion in the gut will definitely contribute to our understanding of intestinal homeostasis. In this review, we summarized the current knowledge about intestinal cell shedding under both physiological and pathological circumstances.

Anti-PD-1 antibody-mediated activation of type 17 T-cells undermines checkpoint blockade therapy.

Tumors that develop in the genetic LSL-K-rasG12D murine lung cancer model are resistant to anti-PD-1 antibody treatment. Analysis of tumor-bearing lungs from anti-PD-1-treated mice revealed an up to 2.5-fold increase in IL-17-producing T-cells, with minimal change in CD8+ T-cell activity. Neutralization of IL-17 concurrent with anti-PD-1 treatment on the other hand, resulted in robust CD8+ T-cell activation and a threefold reduction in tumor burden. Loss-of-function studies demonstrated that anti-PD-1 driven activation of CD4+ and γδTCR+ T-cells contributed to IL-17-mediated de-sensitization of CD8+ cytotoxic T-cells (CTL) to therapy; and that CTL activation was critical to tumor eradication. Importantly, post-therapy lung Th17 cell prevalence and activity prognosticated treatment efficacy. Consistent with the murine data, analysis of tumor biopsy samples from non-small cell lung cancer (NSCLC) patients revealed that pre-therapy intratumoral CD8+/RORc+ cell ratio correlated with response to immune checkpoint blockade (ICB). These findings provide the initial evidence for a new mechanism of ICB resistance in lung cancer.

Soil Quality and Pomelo Productivity as Affected by Chicken Manure and Cow Dung.

Fruit orchards in the Vietnamese Mekong Delta (VMD) are severely degraded due to many factors, such as low organic matter content, soil acidification, and poor soil management. Organic manures are considered to be a soil conservation measure that decreases soil degradation and acidity. This study aimed to evaluate the impacts of soil organic amendments on the improvement of soil fertility and pomelo productivity. Two soil amendments, namely, chicken manure (CM) and cow dung (CD), were investigated for a period of three years at three pomelo orchards. The soil quality was assessed in two depths (0-20 and 20-50 cm), including the soil pH, electrical conductivity (EC), total nitrogen (Ntot), available phosphorus (Pavail), soil organic matter (SOM), bulk density (BD), and exchangeable cations (Ca, Mg, and K). The results indicated that CD and CM improved soil fertility in topsoil layer (0-20 cm) due to an increase in soil pH, SOM, exchangeable Ca, Ntot, and Pavail. In addition, soil BD significantly reduced after CD and CM were supplied in the three consecutive years of study. The soil quality properties that significantly affected pomelo yield were SOM, Ntot, Pavail, and soil BD. Thus, these soil qualities may be considered as key factors for determining and assessing soil quality in fruit orchards in the VMD. More studies on the influence of organic manures on nutrient uptake and pomelo fruit quality are warranted.

Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation.

Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping.

Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts.

Adherence to anti-estrogen therapy in women with hormone receptor-positive breast cancer utilizing bubble packaging: a pilot study.

PURPOSE: This pilot study evaluated adherence to anti-estrogen therapy in women with hormone receptor-positive breast cancer utilizing bubble packaging. METHODS: This was a single-arm prospective investigational pilot study that enrolled 86 patients between August 2012 and April 2014. Descriptive statistics for patient age, race, insurance, stage, duration of treatment, and comorbidities were computed. All patients received routine prescriptions in a "bubble" pack or daily blister pack dispensed by one pharmacy. Participants were considered adherent if they had taken ≥ 80% of the dispensed drug. Disease-free survival (DFS) and overall survival (OS) data were obtained at 78 months. RESULTS: Fifty patients were included in the analysis. The overall adherence rate was 97%. None of the variables examined (race, age, insurance status, and stage) had an impact on adherence rate. Only duration of endocrine therapy had a marginal effect on adherence (p value = 0.06). The late cohort (duration of therapy 37-60 months) was least likely to be compliant at 89.53%. Our 5-year DFS was 94% and 5-year OS was 96%. There was no statistically significant difference in DFS and OS between patients with adherence rate > 90% and < 90%. CONCLUSION: Adherence rate to bubble packaging was higher than that in historical studies. Although this is a single-arm pilot study, these data suggest bubble packaging of anti-estrogen may be a reasonable option to improve adherence in hormone receptor-positive breast cancer patients.

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data.

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg⁻Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

Molecular Epidemiology of Helicobacter pylori Infection in a Minor Ethnic Group of Vietnam: A Multiethnic, Population-Based Study.

The Helicobacter pylori-induced burden of gastric cancer varies based on geographical regions and ethnic grouping. Vietnam is a multiethnic country with the highest incidence of gastric cancer in Southeast Asia, but previous studies focused only on the Kinh ethnic group. A population-based cross-sectional study was conducted using 494 volunteers (18-78 years old), from 13 ethnic groups in Daklak and Lao Cai provinces, Vietnam. H. pylori status was determined by multiple tests (rapid urease test, culture, histology, and serology). cagA and vacA genotypes were determined by PCR-based sequencing. The overall H. pylori infection rate was 38.1%. Multivariate analysis showed that variations in geographical region, age, and ethnicity were independent factors associated with the risk of H. pylori acquisition. Therefore, multicenter, multiethnic, population based study is essential to assess the H. pylori prevalence and its burden in the general population. Only the E De ethnicity carried strains with Western-type CagA (82%) and exhibited significantly lower gastric mucosal inflammation compared to other ethnic groups. However, the histological scores of Western-type CagA and East-Asian-type CagA within the E De group showed no significant differences. Thus, in addition to bacterial virulence factors, host factors are likely to be important determinants for gastric mucosal inflammation and contribute to the Asian enigma.

Cost-Effectiveness Model for Chemoimmunotherapy Options in Patients with Previously Untreated Chronic Lymphocytic Leukemia Unsuitable for Full-Dose Fludarabine-Based Therapy.

OBJECTIVES: To evaluate the cost-effectiveness of treatment with anti-CD20 monoclonal antibody obinutuzumab plus chlorambucil (GClb) in untreated patients with chronic lymphocytic leukemia unsuitable for full-dose fludarabine-based therapy. METHODS: A Markov model was used to assess the cost-effectiveness of GClb versus other chemoimmunotherapy options. The model comprised three mutually exclusive health states: "progression-free survival (with/without therapy)", "progression (refractory/relapsed lines)", and "death". Each state was assigned a health utility value representing patients' quality of life and a specific cost value. Comparisons between GClb and rituximab plus chlorambucil or only chlorambucil were performed using patient-level clinical trial data; other comparisons were performed via a network meta-analysis using information gathered in a systematic literature review. To support the model, a utility elicitation study was conducted from the perspective of the UK National Health Service. RESULTS: There was good agreement between the model-predicted progression-free and overall survival and that from the CLL11 trial. On incorporating data from the indirect treatment comparisons, it was found that GClb was cost-effective with a range of incremental cost-effectiveness ratios below a threshold of £30,000 per quality-adjusted life-year gained, and remained so during deterministic and probabilistic sensitivity analyses under various scenarios. CONCLUSIONS: GClb was estimated to increase both quality-adjusted life expectancy and treatment costs compared with several commonly used therapies, with incremental cost-effectiveness ratios below commonly referenced UK thresholds. This article offers a real example of how to combine direct and indirect evidence in a cost-effectiveness analysis of oncology drugs.

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial.

BACKGROUND: Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. OBJECTIVE: The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. METHODS: The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. RESULTS: We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence-based CAC innovations to improve coding practice. Expected results to be published summer 2024. CONCLUSIONS: The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. TRIAL REGISTRATION: clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54593.

A multinational study on artificial intelligence adoption: Clinical implementers' perspectives.

BACKGROUND: Despite substantial progress in AI research for healthcare, translating research achievements to AI systems in clinical settings is challenging and, in many cases, unsatisfactory. As a result, many AI investments have stalled at the prototype level, never reaching clinical settings. OBJECTIVE: To improve the chances of future AI implementation projects succeeding, we analyzed the experiences of clinical AI system implementers to better understand the challenges and success factors in their implementations. METHODS: Thirty-seven implementers of clinical AI from European and North and South American countries were interviewed. Semi-structured interviews were transcribed and analyzed qualitatively with the framework method, identifying the success factors and the reasons for challenges as well as documenting proposals from implementers to improve AI adoption in clinical settings. RESULTS: We gathered the implementers' requirements for facilitating AI adoption in the clinical setting. The main findings include 1) the lesser importance of AI explainability in favor of proper clinical validation studies, 2) the need to actively involve clinical practitioners, and not only clinical researchers, in the inception of AI research projects, 3) the need for better information structures and processes to manage data access and the ethical approval of AI projects, 4) the need for better support for regulatory compliance and avoidance of duplications in data management approval bodies, 5) the need to increase both clinicians' and citizens' literacy as respects the benefits and limitations of AI, and 6) the need for better funding schemes to support the implementation, embedding, and validation of AI in the clinical workflow, beyond pilots. CONCLUSION: Participants in the interviews are positive about the future of AI in clinical settings. At the same time, they proposenumerous measures to transfer research advancesinto implementations that will benefit healthcare personnel. Transferring AI research into benefits for healthcare workers and patients requires adjustments in regulations, data access procedures, education, funding schemes, and validation of AI systems.

New insight into thermal hydrolysis of sewage sludge from solubilisation analysis.

Thermal hydrolysis, a sludge pre-treatment process prior to anaerobic digestion, is increasing in popularity in academia and industry due to the potential of biogas production enhancement. However, there is a limited understanding of the solubilisation mechanism, which significantly influences the biogas yield. This study evaluated the influence of flashing, reaction time, and temperature to understand the mechanism. It was found that while hydrolysis is the primary process (responsible for approximately 76-87% of sludge solubilisation), the sudden decompression via flashing at the end of the process, creating shear force to break the cell membrane, contributes a considerable percentage (approximately 24-13% depended on the treatment conditions) to the solubilisation of treated sludge. More importantly, the decompression helps significantly shorten the reaction time from 30 min to 10 min, which in turn reduces the sludge's colour, minimises energy consumption, and eliminates the formation of inhibitory compounds for anaerobic digestion. However, a considerable loss in volatile fatty acids (650 mg L⁻1 of acetic acid at 160 °C) during flash decompression should be considered.

De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.

The lack of relevant annotated datasets represents one key limitation in the application of Natural Language Processing techniques in a broad number of tasks, among them Protected Health Information (PHI) identification in Norwegian clinical text. In this work, the possibility of exploiting resources from Swedish, a very closely related language, to Norwegian is explored. The Swedish dataset is annotated with PHI information. Different processing and text augmentation techniques are evaluated, along with their impact in the final performance of the model. The augmentation techniques, such as injection and generation of both Norwegian and Scandinavian Named Entities into the Swedish training corpus, showed to increase the performance in the de-identification task for both Danish and Norwegian text. This trend was also confirmed by the evaluation of model performance on a sample Norwegian gastro surgical clinical text.

Using a Large Open Clinical Corpus for Improved ICD-10 Diagnosis Coding.

With the recent advances in natural language processing and deep learning, the development of tools that can assist medical coders in ICD-10 diagnosis coding and increase their efficiency in coding discharge summaries is significantly more viable than before. To that end, one important component in the development of these models is the datasets used to train them. In this study, such datasets are presented, and it is shown that one of them can be used to develop a BERT-based language model that can consistently perform well in assigning ICD-10 codes to discharge summaries written in Swedish. Most importantly, it can be used in a coding support setup where a tool can recommend potential codes to the coders. This reduces the range of potential codes to consider and, in turn, reduces the workload of the coder. Moreover, the de-identified and pseudonymised dataset is open to use for academic users.

Influence of dicarboxylic acid polymer in enhancing the growth and productivity of sweet potato (Ipomoea batatas L.) in acidic soil.

The available phosphorus (P) in acid sulfate soils (ASSs) is low because of fixation by aluminum (Al) and iron (Fe), resulting in decreased P use efficiency and crop yield. At present, the use of dicarboxylic acid polymer (DCAP) coated on P fertilizer is expected to improve P use efficiency and plant productivity. However, the influence of DCAP on P solubility and on the yield of sweet potato cultivated in acidic soils has not been elucidated. Thus, the aimed of this study was to evaluate the effect of the use of DCAP-coated P fertilizer on the availability and nutrient uptake of P as well as the yield of sweet potato. Under the greenhouse condition, the use of DCAP significantly improved P availability (~3 mg P kg-1), increasing tuber diameter and length by ~0.5 and ~1.0 cm, respectively. Thus, the productivity of sweet potato in the treatment 40-kg P2O5 and 60-kg P2O5 ha-1 coated with DCAP was higher by about 100 g pot-1 than that in the same rate of P fertilizers (40- and 60-kg P2O5 ha-1) not coated with DCAP. In the field experiment, P accumulation (82.7 kg P2O5 ha-1) and tuber yield (22.0 t ha-1) in the treatment of DCAP-coated with 60-kg P2O5 ha-1 were not significantly different compared with that in the treatment of 80-kg P2O5 ha-1 (82.1 kg P2O5 and 21.7 t ha-1, respectively). Furthermore, the use of DCAP combined with 75% P fertilizer increased the P availability by the same amount as that with the use of 100% P fertilizer. Hence, the use of DCAP reduced about 25% of the chemical P fertilizer applied in soil.

Chemical processes and sustainability of rice-shrimp farming on saline acid sulfate soils in mekong delta.

In the history of rice-shrimp farming (RSF) in the Mekong Delta, in saline acid sulfate soils (ASS), RSF has proved its sustainability as there is no long-term accumulation of acidity and salinity that adversely affect RSF production. However, the soil processes involved in this phenomenon are not well understood. This study aimed to determine the significant changes in soil and water chemical indicators associated with soil processes during different stages of RSF. Sampling was conducted in six stages to determine the pH and electrical conductivity (EC) of canal and field water, and the pHe, ECe, and sodium-soluble saturated extract (Na-sol) of surface soil (surS) and subsurface soil (subS) in the low-salinity area (LSA) and high salinity area (HSA) of Bac Lieu province. Putative exchange (exch.) Na+ hydrolysis occurs at the beginning of the rainy season when excess salts are leached. This drastically increases the pH of the field water by 0.6 pH units, from 8.2 to 8.8 in stages 1 to 2, respectively. Putative sulfurization has been shown to occur in subS in both LSAs and HSAs, with a significant decrease of 0.5 pH units from stages 4 to 5, leading to a decrease in the Na-sol of subS through exch. Na+ transformation. Simultaneously, with active soil preparation and liming for RSF, chemical processes such as exch. Na+ hydrolysis, sulfurization, and Na+ transformation are the main factors promoting exch. Na+ desorption, which help to reduce the long-term build-up of salinity and acidity that adversely affect RSF production. Although gypsum is considered an effective material in improving saline soils, in Vietnam, gypsum is the liming material of CaSO4; however, it is not commonly used in agriculture because of its high cost. By using CaCO3 or dolomite as a liming material in saline ASS, the dissolution of lime by sulfuric acid can provide a good opportunity for soil improvement and rice growth in RSF.

Weather indicators and improving air quality in association with COVID-19 pandemic in India.

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. Supplementary Information: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India.

[This retracts the article DOI: 10.1007/s00500-021-06012-9.].