Analyzing the factors affecting the use of digital signature system with the technology acceptance model

Abstract. Digital signature is a mechanism that is becoming widespread in the world thanks to its efficiency in electronic environments. The main purpose of the research is to reveal the variables that affect the perception and behaviors of the users using the digital signature system with technology acceptance models to determine the degree of impact of each variable and conceptualize these variables under a structural model. In this regard, 463 questionnaires collected from academic and administrative personnel working at Ataturk and Gümüşhane universities were analyzed. According to analysis results, anxiety has a negative impact on perceived usefulness. It has been obtained that perceived usefulness has a positive effect on attitude, and perceived ease of use does not have a significant effect on attitude. In addition, in the adoption of digital signature technology, it has been seen that personnel actual use is affected by intention to use with a rate of 88.1%.Keywords. Digital signature, Technology acceptance model, Theory of planned behavior, Structural equation model.JEL. C38, M10, M15, M19

Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect

Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII

A novel pathogenic variant in an Iranian Ataxia telangiectasia family revealed by next-generation sequencing followed by in silico analysis

Ataxia telangiectasia (A-T) is a neurodegenerative autosomal recessive disorder with the main characteristics of progressive cerebellar degeneration, sensitivity to ionizing radiation, immunodeficiency, telangiectasia, premature aging, recurrent sinopulmonary infections, and increased risk of malignancy, especially of lymphoid origin. Ataxia Telangiectasia Mutated gene, ATM, as a causative gene for the A-T disorder, encodes the ATM protein, which plays an important role in the activation of cell-cycle checkpoints and initiation of DNA repair in response to DNA damage. Targeted next-generation sequencing (NGS) was performed on an Iranian 5-year-old boy presented with truncal and limb ataxia, telangiectasia of the eye, Hodgkin lymphoma, hyper pigmentation, total alopecia, hepatomegaly, and dysarthria. Sanger sequencing was used to confirm the candidate pathogenic variants. Computational docicing was done using the HEX software to examine how this change affects the interactions of ATM with the upstream and downstream proteins. Three different variants were identified comprising two homozygous SNPs and one novel homozygous frameshift variant (c.80468047delTA, p.Thr2682ThrfsX5), which creates a stop codon in exon 57 leaving the protein truncated at its C-terminal portion. Therefore, the activation and phosphorylation of target proteins are lost. Moreover, the HEX software confirmed that the mutated protein lost its interaction with upstream and downstream proteins. The variant was classified as pathogenic based on the American College of Medical Genetics and Genomics guideline. This study expands the spectrum of ATM pathogenic variants in Iran and demonstrates the utility of targeted NGS in genetic diagnostics. (C) 2017 Published by Elsevier B.V

Eryngium Billardieri Induces Apoptosis via Bax Gene Expression in Pancreatic Cancer Cells

Purpose: Pancreatic adenocarcinoma has a high prevalence all over the world. Most of the therapeutic approaches failed as a result of tumor invasion and rapid metastasis. Several natural plants have been shown to have promising therapeutic effects. Thus, the aim of this study was to investigate the cytotoxic activity of Eryngium billardieri against PANC-1 cancer cell lines. Methods: Dimethylthiazole diphenyltetrazolium bromide assay (MTT assay) and flow cytometry were used to assess the cytotoxicity of E. billardieri extracts against PANC-1 cancer cell lines. Quantitative Polymerase Chain Reaction (qPCR) was conducted to investigate the expression levels of Bcl2- associated X protein (BAX) and cyclin D1. Results: The results of the MTT assay showed that E. billardieri extracts had cytotoxic effects on PANC- 1 cancer cell lines. Moreover, the findings from the gene expression confirmed the over expression of Bax, and under expression of cyclin D1 following treatment with dichloromethane (DCM) and n-hexane (n- hex) extracts in cancer cells (P < 0.05). Interestingly, the flow cytometry results showed that DCM and n- hex extracts of E. billardieri induced apoptosis in PANC- 1 cancer cell lines. Conclusion: The results of this study demonstrated that DCM and n- hex extracts of E. billardieri significantly induce apoptosis by increasing Bax and decreasing cyclin D1 mRNA expression. Therefore, E. billardieri may be regarded as a novel approach for treatment of pancreatic cancer as a result of its promising apoptotic and cytotoxic properties

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM<inf>2·5</inf> air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation: Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Funding: Bill & Melinda Gates Foundation

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : an analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2.5 originating from ambient and household air pollution.Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2.5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure-response curve from the extracted relative risk estimates using the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2.5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2.5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals.Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2.5 exposure, with an estimated 3.78 (95% uncertainty interval 2.68-4.83) deaths per 100 000 population and 167 (117-223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13.4% (9.49-17.5) of deaths and 13.6% (9.73-17.9) of DALYs due to type 2 diabetes were contributed by ambient PM2.5, and 6.50% (4.22-9.53) of deaths and 5.92% (3.81-8.64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2.5.Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2.5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.Peer reviewe

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : An analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes

Mapping disparities in education across low- and middle-income countries

Analyses of the proportions of individuals who have completed key levels of schooling across all low- and middle-income countries from 2000 to 2017 reveal inequalities across countries as well as within populations. Educational attainment is an important social determinant of maternal, newborn, and child health(1-3). As a tool for promoting gender equity, it has gained increasing traction in popular media, international aid strategies, and global agenda-setting(4-6). The global health agenda is increasingly focused on evidence of precision public health, which illustrates the subnational distribution of disease and illness(7,8); however, an agenda focused on future equity must integrate comparable evidence on the distribution of social determinants of health(9-11). Here we expand on the available precision SDG evidence by estimating the subnational distribution of educational attainment, including the proportions of individuals who have completed key levels of schooling, across all low- and middle-income countries from 2000 to 2017. Previous analyses have focused on geographical disparities in average attainment across Africa or for specific countries, but-to our knowledge-no analysis has examined the subnational proportions of individuals who completed specific levels of education across all low- and middle-income countries(12-14). By geolocating subnational data for more than 184 million person-years across 528 data sources, we precisely identify inequalities across geography as well as within populations.Peer reviewe