Amelioration of a saline sodic soil through cultivation of a salt-tolerant grass Leptochloa fusca

Reclamation of saline lands seems difficult for climatic and economic reasons, but cultivation of salt-tolerant plants is an approach to increasing productivity and improvement of salt-affected wastelands. A five-year field study was conducted to evaluate the effects of growing a salt-tolerant species Leptochloa fusca (L.) Kunth (kallar grass) on chemical properties of a saline sodic soil irrigated with poor quality groundwater. Soil salinity, sodicity and pH decreased exponentially by growing kallar grass as a result of leaching of salts from surface (0–20 cm) to lower depths (>100 cm). Concentrations of soluble cations (Na+, K+, Ca2+ and Mg2+) and anions (Cl−, SO42− and HCO3−) were reduced through to greater soil depths. A significant decline in soil pH was attributed to release of CO2 by grass roots and solublization of CaCO3. Both soil salinity and soil pH were significantly correlated with Na+, Ca2+, Mg2+, K+, Cl−, HCO3− and sodium adsorption ratio (SAR). Significant correlations were found between soluble cations (Na+, Ca2+ and K+), soluble anions (Cl−, SO42− and HCO3−) and the SAR. In contrast, there were negative correlations between soil organic matter content and all chemical properties. The ameliorative effects on the soil chemical environment were pronounced after three years of growing kallar grass. Cultivation of kallar grass enhanced leaching and interactions among soil chemical properties and thus restored soil fertility. The soil maintained the improved characteristics with further growth of the grass up to five years suggesting that growing salt-tolerant plants is a sustainable approach to biological amelioration of saline wastelands.J. Akhter, K. Mahmood, K.A. Malik, S. Ahmed and R. Murra

Adherence to Lifestyle Advice and reatments in Pakistani Patients with Type 2 Diabetes Mellitus.

Background: Type 2 diabetes mellitus (T2DM) is a chronic disease that has become a major health care concern, especially in developing countries like Pakistan. Lifestyle modification and appropriate pharmacotherapy are shown to improve blood glucose levels, lipid abnormalities and blood pressure. It is not known how many patients adhere to advice and drugs prescribed. This study aimed to determine adherence to lifestyle and therapeutic advice. Methods: A cross sectional hospital based study was conducted among patients attending the diabetic clinic at the Aga Khan University Hospital, using a structured questionnaire. Adult patients with T2DM and with at least one year duration of diabetes were included in the study. Results: Participants were aged between 32 and 92 years old with a mean age of 55.7 years old (SD ± 10.7). Mean duration of diabetes was 10.7 years old (SD ± 7.7). Majority (94%) of the patients were literate. Around half (47.3%) of the patients have had achieved glycemic target (HbA1c \u3c 7%). Above target glycemic control was more common among patients with ischemic heart disease (68.1%), neuropathy (64.8%) and those on insulin (62.5%). Self-reported non-adherence for blood sugar monitoring (9.5%), physi cal activity (61.7%), tobacco use (43.4%) and foot care (43.9%) were noted. About 25% of the participants were not fully adherent to dietary advice. None of the patients from our study reported non-ad- herence to medications. Good adherence to physical activity was found in males with college degree. The highest percentage of tobacco use (33.3%) was reported among businessmen. Conclusion: We noted low adherence to advice for physical activity, tobacco use and SMBG, but a high adherence to prescribed medications and insulin. This was a selected group visiting a teaching hospital. This will need to be studied further in the community and efforts are required to motivate patients

COVID-19 and liver injury: A systematic review and meta-analysis

Background and Aims: The prevalence and extent of liver damage in coronavirus disease 2019 (COVID-19) patients remain poorly understood, primarily due to small-sized epidemiological studies with varying definitions of “liver injury”. We conducted a meta-analysis to derive generalizable, well-powered estimates of liver injury prevalence in COVID-19 patients. We also aimed to assess whether liver injury prevalence is significantly greater than the baseline prevalence of chronic liver disease (CLD). Our secondary aim was to study whether the degree of liver injury was associated with the severity of COVID-19.Materials and Methods: Electronic databases (PubMed and Scopus) were systematically searched in June 2020 for studies reporting the prevalence of baseline CLD and current liver injury in hospitalized COVID-19 patients. Liver injury was defined as an elevation in transaminases \u3e3 times above the upper limit of normal. For the secondary analysis, all studies reporting mean liver enzyme levels in severe versus non-severe COVID-19 patients were included. A random-effects model was used for meta-analysis. Proportions were subjected to arcsine transformation and pooled to derive pooled proportions and corresponding 95% confidence intervals (CIs). Subgroup differences were tested for using the chi-square test and associated p-value. Means and their standard errors were pooled to derive weighted mean differences (WMDs) and corresponding 95% CIs.Results: Electronic search yielded a total of 521 articles. After removal of duplicates and reviewing the full-texts of potential studies, a total of 27 studies met the inclusion criteria. Among a cohort of 8,817 patients, the prevalence of current liver injury was 15.7% (9.5%-23.0%), and this was significantly higher than the proportion of patients with a history of CLD (4.9% [2.2%-8.6%]; p \u3c 0.001). A total of 2,900 patients in our population had severe COVID-19, and 7,184 patients had non-severe COVID-19. Serum ALT (WMD: 7.19 [4.90, 9.48]; p \u3c 0.001; I2 = 69%), AST (WMD: 9.02 [6.89, 11.15]; p \u3c 0.001; I2 = 73%) and bilirubin levels (WMD: 1.78 [0.86, 2.70]; p \u3c 0.001; I2 = 82%) were significantly higher in patients with severe COVID-19 when compared to patients with non-severe disease. Albumin levels were significantly lower in patients with severe COVID-19 (WMD: -4.16 [-5.97, -2.35]; p \u3c 0.001; I2 = 95%).Conclusions: Patients with COVID-19 have a higher than expected prevalence of liver injury, and the extent of the injury is associated with the severity of the disease. Further studies are required to assess whether hepatic damage is caused by the virus, medications, or both

Early Mental Stress Detection Using Q-Learning Embedded Starling Murmuration Optimiser-Based Deep Learning Model

Stress affects individual of all ages as a regular part of life, but excessive and chronic stress can lead to physical and mental health problems, decreased productivity, and reduced quality of life. By identifying stress at an early stage, individuals can take steps to manage it effectively and improve their overall well-being. Feature selection is a critical aspect of early stress detection because it helps identify the most relevant and informative features that can differentiate between stressed and non-stressed individuals. This paper firstly proposes a variance based feature selection technique that uses q-learning embedded Starling Murmuration Optimiser (QLESMO) to choose relevant features from a publicly available dataset in which stresses experienced by nurses working during the Covid’19 Pandemic is recorded using bio-signals and user surveys. Furthermore, a comparative study with other metaheuristic based feature selection techniques have been demonstrated. Next, to evaluate the efficacy of the proposed algorithm, 10 benchmark test functions have been used. The reduced feature subset is then classified through a 1D convolutional neural network (CNN) model (QLESMO-CNN) and is seen to perform well in terms of the evaluation metrics in comparison to other competitive algorithms. Finally, the proposed technique is compared with the State-of-the-Art methodologies present in literature. The experiments provide a strong basis to determine features that are most relevant for early mental stress classification using a hybrid model combining CNN, Reinforcement Learning and metaheuristic algorithms.publishedVersio

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An investigation of the drivers of social commerce and e-word-of-mouth intentions: Elucidating the role of social commerce in E-business

Building on social commerce (s-commerce) perspectives and the trust transfer theory, this study develops a theoretical model that explains the indirect effects of two types of s-commerce attributes (community and platform) on behavioral outcomes (s-commerce intentions and e-Word-of-Mouth (e-WOM) intentions) through trust in community and platform. We analyze data collected from s-commerce users on travel booking websites using structural equation modeling technique. Results confirm that s-commerce intentions and e-WOM intentions are contingent upon s-commerce community and platform attributes. Moreover, the results provide evidence for the mediating effects of trust in community and platform on the relationship between s-commerce attributes and behavioral outcomes. The study provides further insights about the impact of s-commerce experience on s-commerce intention and e-WOM intention. Moreover, this study contributes to s-commerce research and practice by developing and validating the role of s-commerce community and platform attributes in forming consumers’ s-commerce behavioral outcomes

A preconditioned iterative method for solving systems of nonlinear equations having unknown multiplicity

A modification to an existing iterative method for computing zeros with unknown multiplicities of nonlinear equations or a system of nonlinear equations is presented. We introduce preconditioners to nonlinear equations or a system of nonlinear equations and their corresponding Jacobians. The inclusion of preconditioners provides numerical stability and accuracy. The different selection of preconditioner offers a family of iterative methods. We modified an existing method in a way that we do not alter its inherited quadratic convergence. Numerical simulations confirm the quadratic convergence of the preconditioned iterative method. The influence of preconditioners is clearly reflected in the numerically achieved accuracy of computed solutions.Peer ReviewedPostprint (published version

A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification

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