Poverty, Income Distribution and Social Development in Lahore

In this paper we present a comparative analysis of poverty and income inequality prevalent in the seven towns of Lahore. Further, an analysis of gender inequality and overall social development is presented by considering education, health, and labor market conditions. We have computed the Herfindahl Index, Gini Coefficient, ratio of share of income of the bottom 20% to the top 20% and Sen index in this study. Finally, a composite index of social development is estimated and on the basis of this, index ranking of each town is outlined. Nishter Town is the least socially developed town whereas Aziz Bhatti, Shalimar, and Allama Iqbal towns are less socially developed as compared to Ravi, Cantt and Gunj Buksh towns.Poverty, Income distribution, Gini, Lorenz curve, Gender inequality, Social Development, Lahore, Pakistan

Inter-District Inequalities in Social Service Delivery: A Rationalised Approach towards Funds Disbursement

For a less developed country, Pakistan has experienced a relatively high average per capita growth rate of 2.2 percent, for the period 1950-99 [Easterly (2003)]. Unfortunately, high growth rates have not trickled down sufficiently and the living condition of the general populace leaves a lot to be desired. The UNDP’s Human Development Index (HDI) report released in 2010, ranked Pakistan at 144th on the HDI, out of 178 countries [Wasif (2010)]. The HDI conceptualises poverty to be a multi-dimensional construct and considers adult literacy and life expectancy to be key indicators of the quality of life. Given, that Pakistan has experienced high growth rates but ranks so poorly on the HDI, clearly indicates that despite economic growth, the country faces serious challenges in social service delivery. The coverage of social services is limited and varies across different regions of the country. Easterly (2003) points out that in terms of adult literacy there is a huge variation across provinces and female literacy is only 3 percent in rural Balochistan and Khyber Pakhtunkhwa whereas it is 41 percent in urban Sindh. Zaidi (2005) shows that the situation is not much different in case of health outcomes. The study shows that across the country, nearly half of pregnant women suffer from anaemia and 35 percent of children under age five are malnourished. Moreover, the numbers for infant mortality vary across provinces considerably with urban Punjab having an infant mortality of 70.6 per 1,000 live births compared to the 120.6 of urban Balochistan.

Poverty, Income Distribution and Social Development in Lahore

In this paper we present a comparative analysis of poverty and income inequality prevalent in the seven towns of Lahore. Further, an analysis of gender inequality and overall social development is presented by considering education, health, and labor market conditions. We have computed the Herfindahl Index, Gini Coefficient, ratio of share of income of the bottom 20% to the top 20% and Sen index in this study. Finally, a composite index of social development is estimated and on the basis of this, index ranking of each town is outlined. Nishter Town is the least socially developed town whereas Aziz Bhatti, Shalimar, and Allama Iqbal towns are less socially developed as compared to Ravi, Cantt and Gunj Buksh towns

XGboost-Ampy: Identification of AMPylation Protein Function Prediction Using Machine Learning

A developing post-translational modification known as AMPylation involves the formation of a phosphodiester bond on the hydroxyl group of threonine, serine, or tyrosine. Adenosine monophosphate is covalently attached to the side chain of an amino acid in a peptide during this process, which is catalyzed by AMPylation. We used AMPylation peptide sequence data from bacteria, eukaryotes, and archaea to train the models. Then, we compared the results of several feature extraction methods and their combinations in addition to classification algorithms to obtain more accurate prediction models. To prevent additional loss of sequence information, the PseAAC feature is employed to construct a fixed-size descriptor value in vector space. The basic feature set is received from 2nd features extraction method. All of this was accomplished by deriving the protein characteristics from the evolutionary data and sequence of the BLOUSM62 amino acid residue. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. In this research, we proposed framework for AMPylation identification that makes use of the XGBoost algorithm (AMPylation) and sequence-derived functions. XGBoost -Ampy has an accuracy of 86.7%, a sensitivity of 76.1%, a specificity of 97.5%, and a Matthews’s correlation coefficient (MCC) of 0.753 for predicting AMylation sites. XGBoost -Amp, the first machine learning model developed, has shown promise and may be able to help with this problem

AOPs-XGBoost: Machine learning Model for the prediction of Antioxidant Proteins properties of peptides

Abstract Antioxidant proteins are essential for protecting cells from free radicals. The accurate identification of antioxidant proteins via biological tests is difficult because of the high time and financial investment required. The potential of peptides produced from natural proteins is demonstrated by the fact that they are generally regarded as secure and may have additional advantageous bioactivities. Antioxidative peptides are typically discovered by analyzing numerous peptides created when a variety of proteases hydrolysis proteins. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. We suggested a machine-learning model that we named AOPs-XGBoost, built on sequence features and Extreme Gradient Boosting (XGBoost). We used 10-fold cross-validation testing was performed on a testing dataset using the propose. AOPs-XGBoost classifier, and the results showed a sensitivity of 67.56%, specificity of 93.87%, average accuracy of 80.72%, mean cross-validation (MCC) of 66.29%), and area under the receiver operating characteristic curve (AUC) of 88.01%. The outcomes demonstrated that the XGBoost model outperformed the other models with accuracy of 80.72% and area under the receiver operating characteristic curve of 88.01% which were better than the other models. Experimental results demonstrate that AOPs-XGBoost is a useful classifier that advances the study of antioxidant proteins

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use