A molecular phylogeny of selected species of Genus Prunus L. (Rosaceae) from Pakistan using the TRN-L & TRN-F spacer DNA

The genus Prunus L. is an important plant for fruit production and it includes plums, apricots, cherries, almonds and peaches that are sources of food for the local people. The family Rosaceae is not yet published in Flora of Pakistan and there is a lot of taxonomic work that is yet to be done for the proper classification and placement of different genera under different sub-families. Prunus is found in almost all the four provinces of Pakistan including Punjab, Khyber Pakhtunkhwa (former NWFP), Sindh and Baluchistan which includes Azad Kashmir region. In the present study, the genus Prunus was studied in detail to find out the phylogenetic relationship among the 12 species of Prunus selected from different regions of Pakistan and GenBank using the maximum parsimony analysis of sequence polymorphism in chloroplast TRN-L and TRN-F spacer DNA. The results for the TRN-L and TRN-F primers confirm the work done by early phylogenetists including Potter and Bortiri with additions to new species from Pakistan including Prunus dulcis (Mill.) D.A. Webb. (Syn. Prunus amygdalus) and Prunus cornuta (Wall. ex. Royle) Steudel. which are indigenous to Pakistan.Key Words: Prunus, chloroplast, TRN-L, TRN-F, Pakistan

Development and properties of polymeric nanocomposite coatings

Polymeric-based nanocomposite coatings were synthesized by reinforcing epoxy matrix with titanium nanotubes (TNTs) loaded with dodecylamine (DOC). The performance of the developed nanocomposite coatings was investigated in corrosive environments to evaluate their anti-corrosion properties. The SEM/TEM, TGA, and FTIR analysis confirm the loading of the DOC into the TNTs. The UV-Vis spectroscopic analysis confirms the self-release of the inhibitor (DOC) in response to the pH change. The electrochemical impedance spectroscopic (EIS) analysis indicates that the synthesized nanocomposite coatings demonstrate superior anticorrosion properties at pH 2 as compared to pH 5. The improved anticorrosion properties of nanocomposite coatings at pH 2 can be attributed to the more effective release of the DOC from the nanocontainers. The superior performance makes polymeric nanocomposite coatings suitable for many industrial applications.Qatar University, University of Auckland, Qatar FoundationScopu

Genome-Wide Detection of Small Regulatory RNAs in Deep-Sea Bacterium Shewanella piezotolerans WP3

Shewanella are one of the most abundant Proteobacteria in the deep-sea and are renowned for their versatile electron accepting capacities. The molecular mechanisms involved in their adaptation to diverse and extreme environments are not well understood. Small non-coding RNAs (sRNAs) are known for modulating the gene expression at transcriptional and posttranscriptional levels, subsequently playing a key role in microbial adaptation. To understand the potential roles of sRNAs in the adaptation of Shewanella toward deep-sea environments, here an in silico approach was utilized to detect the sRNAs in the genome of Shewanella piezotolerans WP3, a piezotolerant and psychrotolerant deep-sea iron reducing bacterium. After scanning 3673 sets of 5′ and 3′ UTRs of orthologous genes, 209 sRNA candidates were identified with high confidence in S. piezotolerans WP3. About 92% (193 out of 209) of these putative sRNAs belong to the class trans-encoded RNAs, suggesting that trans-regulatory RNAs are the dominant class of sRNAs in S. piezotolerans WP3. The remaining 16 cis-regulatory RNAs were validated through quantitative polymerase chain reaction. Five cis-sRNAs were further shown to act as cold regulated sRNAs. Our study provided additional evidence at the transcriptional level to decipher the microbial adaptation mechanisms to extreme environmental conditions

Oral health behind the bars: oral health seeking behavior among jail prisoners at central jail of Peshawar, Pakistan: a cross-sectional study

Background: The oral health care-seeking behavior among prison inmates is an overlooked area, often leading to deteriorating general health due to the prisoners’ limited awareness of oral hygiene practices. It is crucial to address this issue and understand the factors associated with oral healthcare-seeking behavior in prisons. Objective: To assess the oral healthcare-seeking behavior of prison inmates at Central Prisoner Jail, Peshawar Pakistan and to look the factors associated with their dental care utilization. Material and Methods: This cross-sectional study was conducted at Central Prisoner Jail, Peshawar Khyber Pakhtunkhwa, Pakistan from November 2021 to April 2022. A consecutive sampling technique was used to collect data from both convicted and under-trial prisoners by using a pre-tested WHO Basic Oral Health Survey 2013 tool. Our outcome variable was “Visit to a dentist in the last 12 months (Never/Once or more than one visit). Chi-square test was used to determine univariate association with other explanatory variables while multivariable logistic regression was performed to adjust for potential confounders. Result: A total of 225 participants were recruited to the study with a mean (SD) age of 32.9(11.4). More than two-thirds of 200(88.9%) of the participants were males. One-third of the sample never visited the dentist75(33.3). Participants who completed college/university education and never visited the dentist in the last 12 months constituted a smaller proportion (17.6%) compared to those who visited the dentist once or more than once n = 28(82.4%, p-value = 0.003). Individuals who were using toothbrushes were most frequently visiting the dentist n = 130(72.6%=p value = 0.001) as compared to never visitors. Multivariate logistic regression analysis revealed that Participants who experienced teeth pain or discomfort had 0.42 times lower odds of visiting the dentist compared to those who did not experience any pain or discomfort [AOR 0.42 (95% CI 0.17–0.80), p = 0.04]. Similarly, Participants who do not use any denture have 4.06 times higher odds[AOR 4.06(95% CI 1.76–9.36), p = 0.001] of visiting the dentist compared to those who use a denture. Conclusion: Our result demonstrates that those prisoners who were experiencing tooth pain or discomfort and not using dentures were the strong predictors with lower dental visit frequency to seek oral health care

"Systematic Analysis of the Factors That Impact upon the Mindset of Knowledge Sharing Behaviour (KSB) for Individuals within Academia"

The aim of this study is to provide an examination of the factors that have a bearing on KSB, based upon attitudes amongst academics in developing countries, using a particular focus upon academics within the University of Baghdad. With the research study, structural equation modelling was undertaken by using a questionnaire survey for examination of attitudes to microfoundations with regard to KSB amongst a total of 326 academics based at the University of Baghdad. With regard to KSB, it was found that three of the hypothesised factors (anticipation of extrinsic rewards, anticipation of reciprocal relationships and perception of reciprocal benefits) were significantly and positively related. No significant relationship, however, was found to exist between KSB and interpersonal interactions. Based upon the results, a refined, valid model succeeds in exhibiting good explanatory power for the prediction of the intentions for the KSB of academics. Furthermore, it was suggested by the results that academics who were less educated had a greater willingness for knowledge sharing than those who were more highly educated. Based upon the unprecedented data, the paper makes a contribution to growing KSB-theory-related research, particularly with respect to the planned model of behaviour, and puts forward empirical evidence in support of the relationship between attitude and the KSB of academics

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model

A three-level ransomware detection and prevention mechanism

Ransomware encrypts victim's files or locks users out of the system. Victims will have to pay the attacker a ransom to decrypt and regain access to the user files. Petya targets individuals and companies through email attachments and download links. NotPetya has worm-like capabilities and exploits EternalBlue and EternalRomance vulnerabilities. Protection methods include vaccination, applying patches, et cetera. Challenges faced to combat ransomware include social engineering, outdated infrastructures, technological advancements, backup issues, and conflicts of standards. Three- Level Security (3LS) is a solution to ransomware that utilizes virtual machines along with browser extensions to perform a scan, on any files that the user wishes to download from the Internet. The downloaded files would be sent over a cloud server relay to a virtual machine by a browser extension. Any changes to the virtual machine after downloading the file would be observed, and if there were a malfunction in the virtual machine, the file would not be retrieved to the user's system