Dataset on utilizing cropping system-based fertilization techniques to improve soil health and crop output while minimizing tillage.

The dataset provided details on how tillage methods and nutrient management impacted the productivity of the four crops (mustard>mungbean>Transplanting (T.) aus >Transplanting (T.) aman) cropping system and the overall soil health. The specific tillage techniques examined were minimum tillage (MT), conventional tillage (CT), and deep tillage (DT). Regarding nutrient management, NM1 utilized 100 % soil test-based (STB) fertilization following fertilizer gradient generation (FRG); NM2 applied 125 % of STB after FRG-2018; NM3 consisted of 100 % STB (with 80 % from chemical fertilizers and 20 % from cow dung); and NM4 relied on native fertility without any fertilization. Over three consecutive seasonal years (2018-19, 2019-20, and 2020-21), twelve treatments were replicated three times following a factorial totally randomized design. The comparative analysis of crop yield, rice equivalent yield, system productivity and production efficiency indicated superior performance of MT over both CT and DT. Furthermore, in relation to agricultural productivity metrics, the application of the nutrition package NM3 demonstrated performance levels exceeding the average. The adoption of MT and the incorporation of the NM3 nutrition package led to notable advancements in organic matter, field capacity, microbial biomass nitrogen, microbial biomass carbon and soil nutrient levels (N, P, K, S, Zn, and B). Consequently, the synthesis of the NM3 with MT is posited as a strategic approach for soil enhancement and the augmentation of crop productivity.

Genotypic selection and trait variation in sweet orange (Citrus sinensis L. Osbeck) dataset of Bangladesh.

The dataset primarily focused on selecting genotypes of sweet oranges based on their phenotypic performances. The dataset resulted significant variations in the best linear unbiased predictions (BLUPs) of 20 out of 21 traits, including leaves, flowers, fruits, and seeds. A strong positive correlation (r= 0.73 to 0.95) was observed among the majority of morphological traits. The sweet orange genotypes demonstrated considerable genetic variance, surpassing 65% for almost all traits, with a selection accuracy exceeding 92%. Using the multi-trait genotype-ideotype distance index (MGIDI), CS Jain-001 emerged as the top-ranked genotype, followed by BAU Malta-3 and CS Jain-002 in order of desirability. The broad sense heritability of selected traits was above 75.60%, and the selection gain reached a maximum of 12.60. These identified genotypes show promise as potential parent donors in breeding programs, leveraging their strengths and weaknesses to develop promising varieties in Bangladesh.

Prediction and optimization of hardened properties of concrete prepared with granite dust and scrapped copper wire using response surface methodology.

Urban growth in the developing world has prompted researchers to seek alternatives to fine aggregate due to the severe environmental impact of extensive natural sand depletion. On top of that, the accumulation of non-biodegradable dumps, solid trash such as scrapped copper wire (SCW), and industrial remnants like granite dust (GD) have reached alarming levels. Therefore, incorporating these two waste materials in concrete offers a potentially sustainable solution. The study aims at substituting natural fine aggregate with GD as well as incorporating SCW for predicting and optimizing the compressive and splitting tensile strength of concrete using response surface methodology (RSM). Two independent variables, the volumetric percentages of GD (10 %, 20 %, and 30 %) and SCW (0.1 %, 0.3 %, and 0.5 %) in a concrete mix ratio of 1:1.5:3, were utilized to create probabilistic models for compressive and splitting tensile strength at 7 and 28 days. The experimental design employed central composite design (CCD) of RSM and the results of both ANOVA and regression analysis in terms of several statistical functions demonstrated a strong correlation between the predicted values of the responses and the actual experimental results. The developed models were validated by conducting experiments using optimized proportions of GD (23.32 %) and SCW (0.37 %). Finally, the strengths of the optimum content mix yielding 25.12 MPa and 3.266 MPa, respectively for compressive and splitting tensile at 28 days ensure the efficiency of the models due to the substantial similarity between experimental and predicted values. Therefore, integrating GD and SCW for higher-strength concrete in mass production can be a cost-effective alternative, fostering increased recycling of waste and supporting sustainable growth in building construction.

Selection of waterlogging tolerant sesame genotypes (Sesamum indicum L.) from a dataset using the MGIDI index.

The dataset explores the impact of waterlogging stress on sesame plants during the pre-flowering stage, recognizing its global impact on crop yield and the identification of tolerant genotypes using the MGIDI index. Carried out in Bangladesh, the research assesses the survival status, grain yield, and stress tolerance indices of 40 sesame genotypes, revealing that twelve of them demonstrated resilience under 72 h of waterlogging stress at the pre-flowering stage. There were variations in genotypic grain yield, and G15 exhibited the highest yields, recording 5.22 g/plant under normal conditions and 4.10 g/plant under waterlogging stress. The MGIDI index, evaluating waterlogging tolerance, identified G4 as the most favorable genotype, followed by G5 and G12. Factor analysis within the MGIDI index uncovered distinct tolerance and susceptibility indices, highlighting strengths and weaknesses in the selected genotypes. The selection gain percentages of these genotypes ranged from 12.9 to 37.4, indicating high broad-sense heritability (≥0.97). These results underscore the potential of genotype selection based on waterlogging stress indices, providing valuable insights for breeders addressing stress-related crop challenges in the face of changing climatic conditions.

Multi trait stability indexing and trait correlation from a dataset of sweet potato (Ipomoea batatas L.).

A study was conducted in five regions of Bangladesh, specifically Gazipur, Bogura, Jamalpur, Jashore, and Chattogram, each characterized by suitable agro-ecologies for sweet potato cultivation. The purpose of this data article was to demonstrate the correlations between traits and the selection of stable varieties based on the multi-trait stability index (MTSI). The data indicated a direct link between multiple characteristics and both the yield and factors contributing to yield. This implies that enhancing these traits might result in a higher overall production of sweet potato storage roots. Furthermore, the factor analysis for MTSI demonstrated that the desired goal for selection was achieved for all traits, except for mean vine length (VL) and storage root dry weight (DW). The broad sense heritability ranged from 0 to 0.97, and the selection gain percentage ranged from 0 to 42.8. The MTSI analysis identified the sweet potato variety BARI Mistialu-15 as the most stable among the other studied varieties.

Cropping system-based fertilizer strategies for crop productivity and soil health under minimum tillage in grey terrace soil.

A cropping system that is based on three or four crops is currently a widely favored option for augmenting crop productivity to address the escalating global food demand. However, the improper fertilizer management and undue tillage adversely impacts both the productivity of crops and the fertility of the soil. A research investigation was conducted on tillage and nutrient management within the mustard-mungbean-Transplanting aus (T.aus)-Transplanting aman (T.aman) cropping system to examine the impact of fertilizer packages and tillage techniques on the overall productivity of cropping systems, as well as the condition of the soil in grey terrace soil. The research included tillage techniques viz; minimum tillage (MT), conventional tillage (CT) and deep tillage (DT); while nutrient management; NM1: 100 % STB (Soil test based) following FRG (Fertilizer Recommendation Guide-2018), all from chemical fertilizer, NM2: 125 % of STB following FRG- 2018, all from chemical fertilizer, NM3: 100 % STB (80 % from chemical fertilizers and 20 % from cowdung), and NM4: Native fertility (no fertilization). A total of twelve treatments replicated three times following the factorial completely randomized design for three consecutive seasonal years (2018-19, 2019-20, and 2020-21). MT outperformed DT and CT in terms of crop yield, rice equivalent yield (REY), system productivity (SP), and production efficiency (PE). Moreover, NM3 exhibited enhanced performance in terms of agricultural productivity measures. Field capacity (FC), soil organic matter (OM), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and soil nutrients (N, P, K, S, Zn and B) observed an enhancement as a result of the implementation of tillage MT and nutrition package NM3. The investigation indicates that implementing minimum tillage (MT) coupled with an integrated plant nutrition system package (NM3) can assist in the improvement of soil and the enhancement of crop productivity.

Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.

The SARS-CoV-2 3CLpro protein is one of the key therapeutic targets of interest for COVID-19 due to its critical role in viral replication, various high-quality protein crystal structures, and as a basis for computationally screening for compounds with improved inhibitory activity, bioavailability, and ADMETox properties. The ChEMBL and PubChem database contains experimental data from screening small molecules against SARS-CoV-2 3CLpro, which expands the opportunity to learn the pattern and design a computational model that can predict the potency of any drug compound against coronavirus before in-vitro and in-vivo testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem. The F1-score for inactive and active compounds was 93% and 94%, respectively. SHAP (SHapley Additive exPlanations) on XGB classifier to find important fingerprints from the PaDEL descriptors for this task. The results indicated that the PaDEL descriptors were effective in predicting bioactivity, the proposed neural network design was efficient, and the Explanatory factor through SHAP correctly identified the important fingertips. In addition, we validated the effectiveness of our proposed model using a large dataset encompassing over 100,000 molecules. This research employed various molecular descriptors to discover the optimal one for this task. To evaluate the effectiveness of these possible medications against SARS-CoV-2, more in-vitro and in-vivo research is required.

Yield performance and trait correlation of BARI released sweet potato varieties studied under several districts of Bangladesh.

A study was carried out in five sweet potato growing regions of Bangladesh, each characterized by suitable agro-ecologies, in order to demonstrate the most favorable varietal performance and trait correlations. A completely randomized block design with three replications was used to compare the varietal performance of BARI (Bangladesh Agricultural Research Institute) released sweet potato varieties (viz. BARI Mistialu-9, BARI Mistialu-10, BARI Mistialu-12, BARI Mistialu-15 and BARI Mistialu-17). During the 2021-22 cropping season, sweet potato varieties were tested in five districts of Bangladesh, namely Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The findings revealed that the BARI Mistialu-12 variety exhibited remarkable attributes, including a high marketable storage root yield of 39.88 t/ha. Additionally, it demonstrated exceptional performance in various yield components such as vine length, average storage root weight, and dry weight of the root. Furthermore, a positive correlation was observed between several traits and yield, as well as yield-attributing characteristics. This correlation suggests that enhancing these traits could potentially contribute to an overall increase in the storage root yield of sweet potatoes.

Antibiotic Resistance and Plasmid Profiling of Escherichia coli Isolated from Human Sewage Samples.

In developing countries, the occurrence of antibiotic resistance is increasing day by day and antibiotic resistant microorganisms are being found in almost every environmental setting. Plasmids are considered as the main vector in the procurement and propagation of antibiotic resistance in many microorganisms such as Escherichia coli (E. coli). The goal of this study was to examine the antibiotic resistance and screening of plasmid in E. coli strains which were previously identified from human sewage samples. During this study antibiotic susceptibility of E. coli isolates were determined by Kirby-Bauer disk diffusion method against 5 antibiotics (ampicilin, ceftriaxone, amoxicillin, ciprofloxacin, azithromycin). Furthermore, plasmid extraction of each isolate was done according to the protocol of FavorPrepTMPlasmid Mini Kit and plasmid profiling was done by agarose gel electrophoresis. In antibiotic sensitivity test, all E. coli strains showed resistance to ampicilin, amoxicillin, and ceftriaxone. In the plasmid profiling, it was revealed that all the isolates of E. coli harbored plasmids. The plasmid sizes ranged from approximately 1.5 to 15 kb. The findings of this study prove the consequences of antibiotic resistance as well as relationship of plasmid with antibiotic resistance which necessitates proper surveillance on antibiotic usage in the developing countries.

Cognitive impact after short-term exposure to different proton pump inhibitors: assessment using CANTAB software.

INTRODUCTION: Studies have shown that proton pump inhibitors (PPIs) increase the brain burden of amyloid-beta (Aß) and also create vitamin B12 deficiency. However, these two phenomena have deleterious effect on cognition and Alzheimer's disease (AD). Since the use of PPIs has increased tremendously for the last few years, it is of great public health importance to investigate the cognitive impact of PPIs. Hence, the purpose of this study was to investigate the degree of neuropsychological association of each PPI with different cognitive functions. METHODS: Sixty volunteers of either gender were recruited and divided randomly into six groups: five test groups for five classes of PPIs and one control group. All the groups participated in the five computerized neuropsychological tests (nine subtests) of the Cambridge Neuropsychological Test Automated Battery twice: at the beginning of the study and 7 days thereafter. RESULTS: We found statistically and clinically significant impairment in visual memory, attention, executive function, and working and planning function. One-way analysis of variance findings showed that all PPIs had a similar negative impact on cognition. However, paired-samples t tests indicated that omeprazole showed significant (p < 0.05) results in seven subtests; lansoprazole and pantoprazole showed significant results in five subtests; and rabeprazole showed significant results in four subtests. Among five classes of PPIs, esomeprazole showed comparatively less impact on cognitive function with significant results in three subtests. CONCLUSIONS: The present study reveals for the first time that different PPIs have varying degrees of influence on different cognitive domains and have associations with AD. These findings should be considered when balancing the risks and benefits of prescribing these medications. A study done for a longer period of time with a larger sample size might yield better results.

Evaluation of hydrophobic materials as matrices for controlled-release drug delivery.

The present study was undertaken to evaluate the effect of different insoluble and erodable wax-lipid based materials and their content level on the release profile of drug from matrix systems. Matrix tablets of theophylline were prepared using carnauba wax, bees wax, stearic acid, cetyl alcohol, cetostearyl alcohol and glyceryl monostearate as rate-retarding agents by direct compression process. The release of theophylline from these hydrophobic matrices was studied over 8-hours in buffer media of pH 6.8. Statistically significant difference was found among the drug release profile from different matrices. The release kinetics was found to be governed by the type and content of hydrophobic materials in the matrix. At lower level of wax matrices (25%), a potential burst release was observed with all the materials being studied. Bees wax could not exert any sustaining action while an extensive burst release was found with carnauba wax at this hydrophobic load. Increasing the concentration of fat-wax materials significantly decreased the burst effect of drug from the matrix. At higher hydrophobic level (50% of the matrix), the rate and extent of drug release was significantly reduced due to increased tortuosity and reduced porosity of the matrix. Cetostearyl alcohol imparted the strongest retardation of drug release irrespective of fat-wax level. Numerical fits indicate that the Higuchi square root of time model was the most appropriate one for describing the release profile of theophylline from hydrophobic matrices. The release mechanism was also explored and explained with biexponential equation. Application of this model indicates that Fickian or case I kinetics is the predominant mechanism of drug release from these wax-lipid matrices. The mean dissolution time (MDT) was calculated for all the formulations and the highest MDT value was obtained with cetostearyl matrix. The greater sustaining activity of cetostearyl alcohol can be attributed to some level of swelling and erosion within this matrix at lower fat-wax level which is also supported by release exponent values and Fickian fraction release against time profile of this agent. The results generated in this study showed that proper selection of these hydrophobic materials based on their physico-chemical properties is important in designing wax matrix tablets with desired dissolution profile.