A Decision Support System for Moving Workloads to Public Clouds

The current economic environment is compellingCxOs to look for better IT resource utilization in order to get morevalue from their IT investments and reuse existing infrastructureto support growing business demands. How to get more from less?How to reuse the resources? How to minimize the Total Cost ofOwnership (TCO) of underlying IT infrastructure and data centeroperation cost? How to improve Return On Investment (ROI) toremain profitable and transform the IT cost center into a profitcenter? All of these questions are now being considered in light ofemerging ‘Public Cloud Computing’ services. Cloud Computingis a model for enabling resource allocation to dynamic businessworkloads in a real time manner from a pool of free resourcesin a cost effective manner. Providing resource on demand atcost effective pricing is not the only criteria when determiningif a business service workload can be moved to a public cloud.So what else must CxOs consider before they migrate to publiccloud environments? There is a need to validate the businessapplications and workloads in terms of technical portability andbusiness requirements/compliance so that they can be deployedinto a public cloud without considerable customization. Thisvalidation is not a simple task.In this paper, we will discuss an approach and the analytictooling which will help CxOs and their teams to automate theprocess of identifying business workloads that should move toa public cloud environment, as well as understanding its costbenefits. Using this approach, an organization can identify themost suitable business service workloads which could be movedto a public cloud environment from a private data center withoutre-architecting the applications or changing their business logic.This approach helps automate the classification and categorizationof workloads into various categories. For example, BusinessCritical (BC) and Non-business Critical (NBC) workloads canbe identified based on the role of business services within theoverall business function. The approach helps in the assessmentof public cloud providers on the basis of features and constraints.This approach provides consideration for industry complianceand the price model for hosting workloads on a pay-per-usebasis. Finally, the inbuilt analytics in the tool find the ‘best-fit’cloud provider for hosting the business service workload. ‘Bestfit’is based on analysis and outcomes of the previously mentionedsteps.Today, the industry follows a manual time consumingprocess for workload identification, workload classification andcloud provider assessment to find the best-fit for business serviceworkload hosting. The suggested automated approach enables anorganization to reduce cost and time when deciding to move toa public cloud environment. The proposed automated approachaccelerates the entire process of leveraging cloud benefits,through an effective, informed, fact-based decision process

ProkSeq for complete analysis of RNA-Seq data from prokaryotes

Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, tools for studying gene expression, determination of differential gene expression, downstream pathway analysis and normalization of data collected in extreme biological conditions is still lacking. Here, we describe ProkSeq, a user-friendly, fully automated RNA-Seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data and visualizing data and results

Facebook use and its predictive factors among students: Evidence from a lower- and middle-income country, Bangladesh

Background:Facebook is a popular social networking site in the modern world. It has an adverse effect such as impairing daily health and psychological health and also interpersonal relationships when the use becomes problematic.AimsTo examine problematic Facebook use (PFU) and its predictors among Bangladeshi students during the COVID-19 pandemic.MethodA cross-sectional online survey was conducted among 601 Bangladeshi students and collected data related to socio-demographic information, behavioral health, internet use behavior, depression, anxiety and problematic Facebook use [assessed using the Bergen Facebook Addiction Scale (BFAS)]. The data were analyzed using descriptive (frequencies and percentages) and inferential statistics (independent sample t-tests, one-way ANOVAs, correlations, and multivariable linear regression).ResultsThe results indicated that 29.1% of participants were problematic Facebook users (using cutoff ≥18 out of 30). Medical college students had higher mean score on PFU than other students (p < 0.001). In addition, the mean score of PFU was significantly higher among the students who were in a relationship (p = 0.001), did not engage in physical activity (p < 0.001), used the internet more than 5 h per day (p < 0.001), used social media (p < 0.001), and had depression or anxiety symptoms (p < 0.001). PFU was significantly associated with depression and anxiety among the whole sample. Predictive factors for PFU included relationship status, daily internet use time, gaming, social media use, depression, and anxiety. The model predicted almost 33.2% variance for PFU.ConclusionsFindings suggest interventions should be implemented for students with a special focus on medical students who had higher score of PFU than other types of students

RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection

Bacterial processes necessary for adaption to stressful host environments are potential targets for new antimicrobials. Here, we report large-scale transcriptomic analyses of 32 human bacterial pathogens grown under 11 stress conditions mimicking human host environments. The potential relevance of the in vitro stress conditions and responses is supported by comparisons with available in vivo transcriptomes of clinically important pathogens. Calculation of a probability score enables comparative cross-microbial analyses of the stress responses, revealing common and unique regulatory responses to different stresses, as well as overlapping processes participating in different stress responses. We identify conserved and species-specific 'universal stress responders', that is, genes showing altered expression in multiple stress conditions. Non-coding RNAs are involved in a substantial proportion of the responses. The data are collected in a freely available, interactive online resource (PATHOgenex). Bacterial stress responses are potential targets for new antimicrobials. Here, Avican et al. present global transcriptomes for 32 bacterial pathogens grown under 11 stress conditions, and identify common and unique regulatory responses, as well as processes participating in different stress responses.Peer reviewe

A population-based nationwide data set concerning the COVID-19 pandemic and serious psychological consequences in Bangladesh

This paper presents the dataset concerning knowledge, pre- ventive behavior, psychological consequences, and suicidal behavior regarding the COVID-19 pandemic in Bangladesh. Data were collected through an online based cross-sectional survey between April 1 and April 10 in 64 districts at the early stage of the COVID-19 pandemic in Bangladesh. A total of 10,067 participants’ data were recruited for analysis. The survey contained items concerning (i) socio-demographic in- formation, (ii) knowledge concerning COVID-19, (iii) behav- ior towards COVID-19, (iv) lockdown and economic issues, (v) assessment of fear of COVID-19, (vi) assessment of in- somnia, (vii) assessment of depression, and (viii) assessment of suicidal ideation. Data were analyzed utilizing SPSS (ver- sion 22) and are represented as frequencies and percentages based on responses to the whole survey. Given that the data were collected across the whole nation, government authori- ties and healthcare policymakers can use the data to develop various models and/or policies regarding preventive strate- gies and help raise awareness through health education to- wards COVID-19

Sleep Problems in Children with Autism Spectrum Disorder in Bangladesh: A Case–Control Study

Background: Sleep problems in children with Autism Spectrum Disorder (ASD) are highly prevalent, but little information is available on this issue in low- to middle-income countries (LMIC) such as Bangladesh. Therefore, the present study investigated the prevalence and socio-demographic determinants of ASD sleep disturbances in a comparison with typically developing children (TDC). Methods: A cross-sectional interview study was carried out within a total of 446 Bangladeshi mothers, whose children’s mean age was 8.1±2.9 years (151 ASD [8.5±2.7 years] and 295 TDC [7.9±2.9 years]); in addition to socio-demographics, the Child Sleep Habit Questionnaire (CSHQ) was used, and a cut-off score of 41 out of 93 points considered as reflecting sleep problems. Results: About 89.7% of the children reported having problems in sleep, with ASD reporting higher frequency vs TDC (94.00% vs 87.50%; χ2=4.678, p=0.031). The overall mean CSHQ score was 48.7±7.6 in total sample, whereas ASD children reported higher scores compared to TDCs (50.9±8.1 vs 47.5±7.0, p<0.001). Similarly, subscales of CSHQ such as sleep duration (4.23±1.56 vs 3.90±1.31, p=0.017), sleep anxiety (7.23±2.05 vs 6.45 ±1.92, p<0.001), night waking (3.82±1.07 vs 3.17±1.89, p<0.001), parasomnias (8.86±2.06 vs 7.85±2.27, p<0.001), and sleep disordered breathing (4.02±2.92 vs 3.43±2.07, p=0.014) were more problematic among ASD compared to TDC. Lastly, 28.5% of ASD reported taking sleep-related medications vs 0.3% for TDC (n=1). Conclusion: Bangladeshi ASD children are highly likely to manifest sleep disturbances, which warrant urgent implementation of parental educational and support programs to mitigate the impact of sleep problems in ASD families

Vitamin D supplementation on prediabetic adults with vitamin D deficiency: a double-blind placebo-controlled randomized clinical trial

Hypovitaminosis D (<20 ng/mL) is thought to increase insulin resistance and meta-inflammation contributing to the pathogenesis of diabetes mellitus (DM). Correcting vitamin D deficiency in people with prediabetes might halt its progression to DM. The aim of this study was to examine the effect of vitamin D supplementation on insulin resistance, glycemic status, and inflammation in prediabetic adults with vitamin D deficiency. This doubleblind randomized placebo-controlled trial was done among 27 newly detected prediabetic adults with hypovitaminosis D randomly assigned to 60,000 IU of vitamin D weekly for eight weeks followed by monthly for the next four months or placebo along with lifestyle modification in both groups [vitamin D (n= 14) vs. Placebo (n=13). They were comparable in terms of sex, age and borlymass index. Glycemic status, fasting plasma glucose (FPG) and Hemoglobin A1C (HbA1C), insulin resistance (homeostasis model assessment of insulin resistance (HOMA-IR) and inflammatory marker high sensitivity C reactive protein (hs-CRP) were measured at baseline and after six months of intervention. Vitamin D levels (ng/mL) increased in both groups from baseline (vitamin D vs. placebo: 12.2±5.9 vs. 3.9±3.5, mean±SD). FPG (mmol/L) significantly decreased in the Vitamin D group (before vs. after: 5.9±0.6 vs. 5.5±0.7, P=0.016, mean±SD), whereas HbA1C (%) and hs- CRP (mg/L) significantly increased in the placebo group (before vs. after- HbA1C: 5.8±0.3 vs. 6.0±0.4, P<0.001; hs-CRP: 5.0±4.4 vs. 5.6±4.9, P=0.039, mean±SD). Percent changes in glycemic status, HOMA-IR, and hs-CRP were statistically similar between the groups. Our study failed to demonstrate the positive effects of vitamin D supplementation on reducing glucose, insulin resistance, or inflammatory marker in prediabetic adult patients with hypovitaminosis D. BSMMU J 2022; 15(3): 167-17

Molecular mechanisms of Yersinia pseudotuberculosis for adaptation and establishment of infection in host tissue

Bacterial pathogens can evade the host’s immune defence to adapt and establish an infection within the host. Some even slip into a quiescent state to establish themselves without acutely harming the host. Phylogenetically unrelated bacteria can share similar strategies for the establishment of infection and for persistence. Our lab previously showed that Yersinia pseudotuberculosis underwent a dramatic reprogramming from a virulent phenotype expressing virulence genes, including T3SS and Yop effectors during early infection, to an adapted phenotype capable of persisting in tissue. The overall aim of my PhD study was to dissect the mechanisms behind bacterial adaptation and maintenance of infection within host tissue using Y. pseudotuberculosis as a model pathogen. The ultimate goal is to identify key players of critical importance for the ability of the bacterium to maintain and establish infection in host tissue. In my studies, I mainly focused on bacterial biofilm and the role of the alternative sigma factor RpoN. Much of my studies involve RNA-Seq analyses, encouraging me to develop a convenient, time-efficient, and all-purpose RNA-Seq data analysis package especially designed for prokaryotic organisms. The package is available online as a free tool and can be used by any biologist with minimal computational knowledge. We systematically examined biofilm formation of Y. pseudotuberculosis under different stress conditions and found that biofilm development involved a series of adaptive responses against various stressors, including bile, pH, amino acid deprivation, and temperature and oxygen-level changes. Analyses of transcription profiles of bacteria forming biofilm in different conditions revealed a set of core genes that were similarly regulated in biofilm bacteria independently of induced environment. The transcriptional regulator RpoN, commonly known as sigma 54, was found to be important for biofilm formation, and a ∆rpoN mutant strain was severely attenuated in virulence. To understand the regulatory mechanisms involved, we investigated gene expressions in wild-type (WT) and the isogenic ∆rpoN mutant strain and also chromatin immunoprecipitation followed by sequencing. We have identified RpoN binding sites in the Y. pseudotuberculosis genome and revealed a complex regulation by RpoN involving both activation and repression effects. We also investigated the role of RpoN in regulation of the Type III secretion system (T3SS) and found that RpoN was required for a functional T3SS, which is essential for bacterial virulence properties in host tissue. Our work indicates that Yersinia modulates itself in multiple ways to create niches favourable to growth and survival in the host environment. We have identified some key regulators and genes that will be explored further for their potential as novel targets for the development of new antibiotics.Uppgift om ISBN för tryckt format saknas i publikationenUppgift om serienummer saknas i publikationen </p

Molecular mechanisms of Yersinia pseudotuberculosis for adaptation and establishment of infection in host tissue

Bacterial pathogens can evade the host’s immune defence to adapt and establish an infection within the host. Some even slip into a quiescent state to establish themselves without acutely harming the host. Phylogenetically unrelated bacteria can share similar strategies for the establishment of infection and for persistence. Our lab previously showed that Yersinia pseudotuberculosis underwent a dramatic reprogramming from a virulent phenotype expressing virulence genes, including T3SS and Yop effectors during early infection, to an adapted phenotype capable of persisting in tissue. The overall aim of my PhD study was to dissect the mechanisms behind bacterial adaptation and maintenance of infection within host tissue using Y. pseudotuberculosis as a model pathogen. The ultimate goal is to identify key players of critical importance for the ability of the bacterium to maintain and establish infection in host tissue. In my studies, I mainly focused on bacterial biofilm and the role of the alternative sigma factor RpoN. Much of my studies involve RNA-Seq analyses, encouraging me to develop a convenient, time-efficient, and all-purpose RNA-Seq data analysis package especially designed for prokaryotic organisms. The package is available online as a free tool and can be used by any biologist with minimal computational knowledge. We systematically examined biofilm formation of Y. pseudotuberculosis under different stress conditions and found that biofilm development involved a series of adaptive responses against various stressors, including bile, pH, amino acid deprivation, and temperature and oxygen-level changes. Analyses of transcription profiles of bacteria forming biofilm in different conditions revealed a set of core genes that were similarly regulated in biofilm bacteria independently of induced environment. The transcriptional regulator RpoN, commonly known as sigma 54, was found to be important for biofilm formation, and a ∆rpoN mutant strain was severely attenuated in virulence. To understand the regulatory mechanisms involved, we investigated gene expressions in wild-type (WT) and the isogenic ∆rpoN mutant strain and also chromatin immunoprecipitation followed by sequencing. We have identified RpoN binding sites in the Y. pseudotuberculosis genome and revealed a complex regulation by RpoN involving both activation and repression effects. We also investigated the role of RpoN in regulation of the Type III secretion system (T3SS) and found that RpoN was required for a functional T3SS, which is essential for bacterial virulence properties in host tissue. Our work indicates that Yersinia modulates itself in multiple ways to create niches favourable to growth and survival in the host environment. We have identified some key regulators and genes that will be explored further for their potential as novel targets for the development of new antibiotics.Uppgift om ISBN för tryckt format saknas i publikationenUppgift om serienummer saknas i publikationen </p

Personality Traits Recognition on Social Network - Facebook

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data