Empirical Evidence on Enhanced Mutation Rates of 19 RM-YSTRs for Differentiating Paternal Lineages.

Rapidly mutating Y-chromosomal short tandem repeats (RM Y STRs) with mutation rates ≥ 10-2 per locus per generation are valuable for differentiating amongst male paternal relatives where standard Y STRs with mutation rates of ≤10-3 per locus per generation may not. Although the 13 RM Y STRs commonly found in commercial assays provide higher levels of paternal lineage differentiation than conventional Y STRs, there are many male paternal relatives that still cannot be differentiated. This can be improved by increasing the number of Y STRs or choosing those with high mutation rates. We present a RM Y STR multiplex comprising 19 loci with high mutation rates and its developmental validation (repeatability, sensitivity and male specificity). The multiplex was found to be robust, reproducible, specific and sensitive enough to generate DNA profiles from samples with inhibitors. It was also able to detect all contributor alleles of mixtures in ratios up to 9:1. We provide preliminary evidence for the ability of the multiplex to discriminate between male paternal relatives by analyzing large numbers of male relative pairs (536) separated by one to seven meioses. A total of 96 mutations were observed in 162 meioses of father-son pairs, and other closely related male pairs were able to be differentiated after 1, 2, 3, 4, 5, 6 and 7 meiosis in 44%, 69%, 68%, 85%, 0%, 100% and 100% of cases, respectively. The multiplex offers a noticeable enhancement in the ability to differentiate paternally related males compared with the 13 RM Y STR set. We envision the future application of our 19 RM Yplex in criminal cases for the exclusion of male relatives possessing matching standard Y STR profiles and in familial searching with unknown suspects. It represents a step towards the complete individualization of closely related males

SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks

Capsule Networks (CapsNets) preserve the hierarchical spatial relationships between objects, and thereby bear the potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. This makes CapsNets suitable for the smart cyber-physical systems (CPS), where a large amount of training data may not be available. A large body of work has explored adversarial examples for CNNs, but their effectiveness on CapsNets has not yet been studied systematically. In our work, we perform an analysis to study the vulnerabilities in CapsNets to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark and CIFAR10 datasets, mislead CapsNets in making a correct classification, which can be catastrophic for smart CPS, like autonomous vehicles. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN (LeNet), to a 9-layer CNN (VGGNet), and to a 20-layer CNN (ResNet), and analyze the outcome, compared to the CapsNets, to study their different behaviors under the adversarial attacks

Characterisation of bacteriocins produced by Lactobacillus spp. isolated from the traditional Pakistani yoghurt and their antimicrobial activity against common foodborne pathogens

Lactic acid bacteria (LAB) are widely known for their probiotic activities for centuries. These bacteria synthesise some secretory proteinaceous toxins, bacteriocins, which help destroy similar or interrelated bacterial strains. This study was aimed at characterising bacteriocins extracted from Lactobacillus spp. found in yoghurt and assessing their bactericidal effect on foodborne bacteria. Twelve isolated Lactobacillus spp. were examined to produce bacteriocins by the organic solvent extraction method. Bacteriocins produced by two of these strains, Lactobacillus helveticus (BLh) and Lactobacillus plantarum (BLp), showed the most significant antimicrobial activity, especially against Staphylococcus aureus and Acinetobacter baumannii. Analysis of SDS-PAGE showed that L. plantarum and L. helveticus bacteriocins have a molecular weight of ~10 kDa and ~15 kDa, respectively. L. plantarum (BLp) bacteriocin was heat stable while L. helveticus (BLh) bacteriocin was heat labile. Both bacteriocins have shown activity at acidic pH. Exposure to a UV light enhances the activity of the BLh; however, it had negligible effects on the BLp. Different proteolytic enzymes confirmed the proteinaceous nature of both the bacteriocins. From this study, it was concluded that bacteriocin extracts from L. helveticus (BLh) can be considered a preferable candidate against foodborne pathogens as compared to L. plantarum (BLp). These partially purified bacteriocins should be further processed to attain purified product that could be useful for food spoilage and preservation purposes

Population genetic portrait of Pakistani Lahore-Christians based on 32 STR loci.

Phylogenetic relationship and the population structure of 500 individuals from the Christian community of Lahore, Pakistan, were examined based on 15 autosomal short tandem repeats (STRs) using the AmpFℓSTR Identifiler Plus PCR Amplification Kit and our previously published Y-filer kit data (17 Y-STRs) of same samples. A total of 147 alleles were observed in 15 loci and allele 11 at the TPOX locus was the most frequent with frequency value (0.464). The data revealed that the Christian population has unique genetic characteristics with respect to a few unusual alleles and their frequencies relative to the other Pakistani population. Significant deviations from Hardy-Weinberg equilibrium were found at two loci (D13S317, D18S51) after Boneferroni's correction (p ≤ 0.003). The combined power of discrimination, combined power of exclusion and cumulative probability of matching were 0.999999999999999978430815060354, 0.999995039393942 and 2.15692 × 10-17, respectively. On the bases of genetic distances, PCA, phylogenetic and structure analysis Lahore-Christians appeared genetically more associated to south Asian particularly Indian populations like Tamil, Karnataka, Kerala and Andhra Pradesh than rest of global populations

Literature review on life cycle assessment of transportation alternative fuels

Data availability: No data was used for the research described in the article.Copyright © 2023 The Authors. Environmental concerns, such as global warming and human health damage, are intensifying, and the transportation sector significantly contributes to carbon and harmful emissions. This review examines the life cycle assessment (LCA) of alternative fuels (AF), evaluating current research on fuel types, LCA framework development, life cycle inventory (LCI), and impact selection. The objectives of this paper are: (1) to compare various AF LCA frameworks and develop a comprehensive framework for the transportation sector; (2) to identify emission hotspots of different AFs through simulations and real-world cases; (3) to review AF LCA research; (4) to extract valuable information for potential future research directions. The analysis reveals that all stages, except for hydrogen use, have an environmental impact. LCA boundaries and LCIs vary considerably depending on the raw materials, production processes, and products involved, leading to different emission hotspots. Due to knowledge or data limitations, some stages remain uncalculated in the current study, emphasizing the need for further refinement of the AF LCI. Future research should also explore the various impacts of widespread adoption of alternative fuels in transportation, encompassing social, economic, and environmental aspects. Lastly, the review provides structured recommendations for future research directions

Safety, tolerability, and nocebo phenomena during transcranial magnetic stimulation: a systematic review and meta‐analysis of placebo‐controlled clinical trials

Background The methodology used for the application of repetitive transcranial magnetic stimulation (TMS) is such that it may induce a placebo effect. Respectively, adverse events (AEs) can occur when using a placebo, a phenomenon called nocebo. The primary aim of our meta‐analysis is to establish the nocebo phenomena during TMS. Safety and tolerability of TMS were also studied. Methods After a systematic Medline search for TMS randomized controlled trials (RCTs), we assessed the number of patients reporting at least one AE and the number of discontinuations because of AE in active and sham TMS groups. Results Data were extracted from 93 RCTs. The overall pooled estimate of active TMS and placebo treated patients who discontinued treatment because of AEs was 2.5% (95% CI 1.9%‐3.2%) and 2.7% (95% CI 2.0%‐3.5%), respectively. The pooled estimate of active TMS and placebo treated patients experiencing at least one AE was 29.3% (95% CI 19.0%‐22.6%) and 13.6% (95% CI 11.6%‐15.8%), respectively, suggesting that the odds of experiencing an AE is 2.60 times higher (95% CI 1.75‐3.86) in the active treatment group compared to placebo (p < 0.00001). The most common AE was headache, followed by dizziness. Secondary meta‐analyses in depression and psychotic disorders showed that the odds of experiencing an AE is 3.98 times higher (95% CI 2.14‐7.40) and 2.93 times higher (95% CI 1.41‐6.07), respectively, in the active treatment groups compared to placebo. Conclusions TMS is a safe and well‐tolerated intervention. Nocebo phenomena do occur during TMS treatment and should be acknowledged during clinical trial design and daily clinical practice

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%