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Phalaris minor is the main and troublesome weed of wheat all over the globe. Chemical weed control is a quick and effective method for weed management. However, herbicides are criticized for environmental pollution and the development of resistance in weeds. Therefore, the present study was planned for chemical synthesis, characterization, and dose optimization of chitosan-based nanoparticles of clodinofop propargyl and fenoxaprop-p-ethyl for the management of Phalaris minor grown in wheat. Chitosan-based nanoparticles of clodinofop propargyl and fenoxaprop-p-ethyl were prepared by the ionic gelification technique. The nanoparticles (NPs) of clodinofop propargyl and fenoxaprop-p-ethyl were sprayed at 3–4 leaf stage of the P. minor weed. Seven different doses (D0 = weedy check, D1 = normal herbicide at recommended dose, D2 = nano herbicide at the recommended dose of normal herbicide, D3 = 5-fold lower dose of nano herbicide, D4 = 10-fold lower dose of nano herbicide, D5 = 15-fold lower dose of nano herbicide, and D6 = 20-fold lower dose of nano herbicide) were used. Chitosan-based NPs of herbicides were characterized using UV absorbance, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD) studies. SEM demonstrated particles in the cluster form with porous structure and the average size ranged from 30 to 60 nm. XRD results confirmed the existence of (2θ) peak at 29.79 related to 160 anatase form in the NPs of clodinofop propargyl and 24.65 related to 76 anatase form in the case of fenoxaprop-p-ethyl. The FT-IR analysis of chitosan-based NPs of both the herbicides perfectly matched the standard parameters. UV-visible spectra exhibited absorption peaks at 300 and 330 nm, for the NPs of fenoxaprop-p-ethyl and clodinofop, respectively. The chitosan-based particles of clodinofop propargyl and fenoxaprop-p-ethyl at the recommended dose of normal herbicide caused 100% mortality and visual injury. However, a 5-fold lower dose of chitosan-based NPs of clodinofop propargyl and fenoxaprop-p-ethyl caused the maximum visual injury (94.00%), mortality (93.75%), minimum chlorophyll contents (7.47%), plant height (cm), fresh biomass (0.27 g), and dry biomass (0.08 g) of P. minor. The chitosan-based NPs of clodinofop propargyl and fenoxaprop-p-ethyl at a 10-fold lower dose of normal herbicides and recommended dose produced a similar effect on the previously mentioned traits of P. minor.

Objective: To describe and compare the characteristics of people with SUD and their use of healthcare services in two ways: 1) across varying levels of marginalization and 2) before and during the pandemic. Methods: We conducted a population-based cross-sectional study using administrative data from Ontario, Canada. We included individuals age 16+ with a recorded diagnosis of SUD between June 2018–2019 (pre-pandemic) and June 2021–2022 (during-pandemic). Baseline sociodemographic and clinical characteristics and use of healthcare services were enumerated across the five quintiles of the Ontario Marginalization Index. Results: 259,497 pre-pandemic and 276,459 during-pandemic people with SUD were identified. Over 40% belonged to the two highest marginalization quintiles (Q4/Q5). Most had an outpatient visit with similar percentages across quintiles, however the number of visits increased with increasing marginalization (pre-pandemic: mean 8.5 visits in Q1 vs 13.0 visits in Q5; during-pandemic: mean 9.5 in Q1 vs 13.4 in Q5). There was no consistent pattern in percent of people who sought alcohol-related outpatient care, however more marginalized people sought drug-related outpatient care (pre-pandemic: 19.1% in Q1 vs 31.7% in Q5; during-pandemic: 18.7% in Q1 vs 32.5% in Q5). Almost half of people with SUD had an emergency department (ED) visit, of which more belonged to higher marginalization quintiles (pre-pandemic: 43.5% in Q1 vs 49.8% in Q5; during-pandemic: 41.4% in Q1 vs 49.3% in Q5). Conclusions: SUD prevalence and most health service utilization remained similar from pre- to during-pandemic. Increasing marginalization was associated with increased use of healthcare among people with SUD. Future research should aim to further explore the complex relationship between marginalization and substance use. [ABSTRACT FROM AUTHOR]

Detecting abnormal ECG patterns is a crucial area of study aimed at enhancing diagnostic accuracy and enabling early identification of Chronic Kidney Disease (CKD)-related abnormalities. This study compares a unique strategy for abnormal ECG patterns using the LADTree model to standard machine learning (ML) models. The study design includes data collection from the MIT-BIH Arrhythmia dataset, preprocessing to address missing values, and feature selection using the CfsSubsetEval method using Best First Search, Harmony Search, and Particle Swarm Optimization Search approaches. The performance assessment consists of two scenarios: percentage splitting and K-fold cross-validation, with several evaluation measures such as Kappa statistic (KS), Best First Search, recall, precision-recall curve (PRC) area, receiver operating characteristic (ROC) area, and accuracy. In scenario 1, LADTree outperforms other ML models in terms of mean absolute error (MAE), KS, recall, ROC area, and PRC. Notably, the Naïve Bayes (NB) model has the lowest MAE, but the Support Vector Machine (SVM) performs badly. In scenario 2, NB has the lowest MAE but the highest KS, recall, ROC area, and PRC area, closely followed by LADTree. Overall, the findings indicate that the LADTree model, when optimized for ECG signal data, delivers promising results in detecting abnormal ECG patterns potentially related with CKD. This study advances predictive modeling tools for identifying abnormal ECG patterns, which could enhance early detection and management of CKD, potentially leading to improved patient outcomes and healthcare practices. [ABSTRACT FROM AUTHOR]

Social connections between individuals are often an important source of information for both quantitative and qualitative anthropological research. Here, we seek to understand the relative strengths and weaknesses of sociometric and ethnographic representations of social connections. We do this by comparing network data collected using a sociometric technique (a name generator) with a network drawn by project ethnographers representing their understanding of social structure in the study population. We find many similarities in the two networks, but they offer somewhat different perspectives into the local social structure. Although the ethnographic network is shaped by the ethnographers' deeper knowledge of a subset of network members, individuals with high degree (but not betweenness) in the sociometric network are generally present in the ethnographic network. The ethnographers' interpretation of the factors that lead to high degree centrality in the network is broadly accurate. However, the sociometric network is characterized by a high level of transitivity not seen in the ethnographic network. We consider the importance of the differences we observe for ethnographic practice.

This paper aims at presenting the findings of the study on the position of street vendors in the District Srinagar, Kashmir, in India, including both men and women, during the COVID-19 lockdown. The purpose of the study was to explore various challenges street vendors faced during the COVID-19 lockdown and to highlight the vulnerability of this particular group of informal workers. The data was collected through face-to-face interviews with the use of a questionnaire, on a sample of 150 street vendors from the District Srinagar. In addition, a certain number of in-depth interviews with selected respondents from the sample was done. The study findings show that the majority of the respondents have lost their job during the peak period of COVID-19, i.e. from March to July 2020. The findings have also revealed that the lockdown directly impacted the socio-economic conditions of the workers which made it very difficult for them to survive during the peak of COVID-19. Additionally, workers were struggling very hard in order to fulfill the basic daily needs of their families. Therefore, it is suggested that the government of India should provide financial support to street vendors in order to compensate for the loss caused due to the COVID-19 lockdown.