International Journal of Bioinformatics and Intelligent Computing

A Comparative Genomic Analysis of Georgenia sps. for Mining of LysR Transcriptional Regulator Sequences

Tejas Oza, Pooja Patel, Vrinda S. Thaker — Mon, 08 Jan 2024 22:12:01 -0600

Georgenia is a genus belonging to the actinomycetes group. The genera comprise only 33 poorly characterized species with reference genomes of 10 distinct species. However, none of the species is well characterized for their genome characteristics. Our laboratory isolate from tomato plant leaf was identified and sequenced for identification and found to be Georgenia sp. Later genomic analysis revealed many functional genes having characteristic functions to be analysed. The source of isolation raised the possibility of having functional genes to enhance senescence or having plant pathogenic activity by Georgenia sp. SUBG003. To explore in silico presence of these genes or gene pools genomic islands were identified and analysed for our isolate and other 10 reference genomes of the Georgenia genus. Genomic islands were further explored for transcription regulators and finally, LysR transcriptional regulator sequences were extracted and a phylogeny among sequences was built from multiple sequence alignment.

Msaken, Tunisia: A Common Paternal Ancestor Confirmed by Y Chromosome DNA Analysis

Kamel AL-Gazzah — Thu, 18 Jan 2024 03:55:32 -0600

Msaken City (Tunisia) is believed to have been founded around 1360 AD by five related men who migrated from West Asia. The population would have grown with the descendants of these founders and with the arrival of other populations from different regions of Tunisia. In order to elucidate the TMRCA of the founder population and to reveal their geographic origin, 23 males from different families of Msaken were examined, using the services of commercial companies, for 12 to 440 Y chromosome Short Tandem Repeats (STR) and Single Nucleotide Polymorphisms (SNP) markers using NG testing technology. Eight samples were genotyped for SNPs to determine their Haplogroups. In order to refine the phylogeny, traditional Sanger testing was performed on one sample for 300,000 bp in Y Chr (in Walk Through the Y chromosome project). Seven samples were also tested using Next Generation Testing (BigY) covering 20 million bp of Y chromosome overlapping 85% of Gold standard region (chromosome Y positions placed on the phylogenetic tree by the YCC) using NGS instruments, HiSeq 2000 and 2500. A comparison of STR results with data from different sources and databases was made, using SQL scripts and data mining tools, to find matching haplotypes. All the STR results were found to have no more than three mismatches per 12 markers and not more than six mismatches per 67 markers and the SNP results showed that all tested samples belonged to Haplogroup J-M172 inside its subgroup J-L24. Relying on the common STR marker values, we define a Msaken-Haplotype. NG tests for our samples as well as those added to the yfull.com tree allowed us to refine the phylogeny of J-L24 and the samples were all found to belong to J-L271 Haplogroup and share 54 exclusive SNPs. The calculated Time to Most Recent Common Ancestor (TMRCA) based on NG testing, ranges between1500 and 6200 YBP showing a strong bottleneck around 5400 YBP. The variation of the collected results shows a geographic root of J-L192 in East Anatolia, present day Armenia, Azerbaijan, and West Iran. 20 to 30% of random Tunisian STR haplotypes belonging to J-M172 (J2) Haplogroup exhibit the Msaken-Haplotype.

Elucidating the Association of Key Socio-demographic Factors Underlying Happiness and Well-being in the Eastern Indian Bengali Population

Wed, 31 Jan 2024 00:00:00 -0600

Background: Subjective happiness or well-being is an important aspect of positive psychology and is determined by several external factors, based on several of which, the World Happiness Report ranks the countries annually. India’s happiness ranking has consistently declined over the years compared to the neighboring countries.

Methods: The present study considered relevant socio-demographic factors and assessed their association, if any, in subjective happiness using multiple linear regression analysis among the eastern Indian Bengali population. A total of 191 participants were recruited for the study and their subjective happiness scores were measured using a well validated Subjective Happiness Scale.

Results: The result showed a significant association of 3 factors viz. individual’s ‘choice to stay or work in a group or alone’ (p ≤ 0.0001); ‘frequency of feeling sad in daily life (p ≤ 0.0001); and ‘personal relationship satisfaction’ (p ≤ 0.0001) with subjective happiness scores in both males and females. Eight other variables showed a gender-specific association with happiness (p ≤ 0.05).

Conclusion: These three socio-demographic factors might thus be the key determinants in regulating subjective well-being in this section of the world population. This information might thus be helpful in future counselling of individuals suffering from distress or severe depression and keep their better mental health.

HUBO & QUBO and Prime Factorization

Samer Rahmeh, Adam Neumann — Thu, 08 Feb 2024 00:32:03 -0600

This document details the methodology and steps taken to convert Higher Order Unconstrained Binary Optimization (HUBO) models into Quadratic Unconstrained Binary Optimization (QUBO) models. The focus is primarily on prime factorization problems; a critical and computationally intensive task relevant in various domains including cryptography, optimization, and number theory.

The conversion from Higher-Order Binary Optimization (HUBO) to Quadratic Unconstrained Binary Optimization (QUBO) models is crucial for harnessing the capabilities of advanced computing methodologies, particularly quantum computing and DYNEX neuromorphic computing. Quantum computing offers potential exponential speedups for specific problems through its intrinsic parallelism capabilities. Conversely, DYNEX neuromorphic computing enhances efficiency and accelerates the resolution of intricate, pattern-oriented tasks by simulating memristors in GPUs, employing a highly decentralized approach, via Blockchain technology. This transformation enables the exploitation of these cutting-edge computing paradigms to address complex optimization challenges effectively.

Through detailed explanations, mathematical formulations, and algorithmic strategies, this document aims to provide a comprehensive guide to understanding and implementing the conversion process from HUBO to QUBO. It underscores the importance of such transformations in making prime factorization computationally feasible on both existing classical computers and emerging computing technologies.

Identification of Antibiotic Drugs against SARS-CoV2 Mpro: A Computational Approach for Drug Repurposing

Hridoy Ranjan Bairagya, Sweety Gupta, Sayanti Pal — Thu, 08 Feb 2024 02:06:14 -0600

The coronavirus pandemic has posed a significant challenge for researchers seeking to develop new compounds and repurpose existing drugs to manage this disease. It has been found that the Main protease enzyme (Mpro) is critical to the replication of the virus, making it an attractive target for drug development. Different antibiotics have been proven effective against different viruses, leading to their recommendation for COVID-19.

In this study, virtual screening, pharmacokinetics, QSAR, and molecular docking techniques were used to investigate the best antibiotic drugs for COVID-19 by targeting the active and inactive conformations of the Mpro enzyme. The results of the study demonstrate that Praziquantel is a promising candidate for COVID-19 treatment. This is due to several reasons:

First, Praziquantel exhibits better binding energy in both the conformations of Mpro. Second, it binds in S-3A site in native conformation and S-1B in active state. Third, Praziquantel has excellent absorption properties, strong blood-brain barrier penetration power, and reasonably good solubility.

Therefore, the study nominates Praziquantel as the best option for future experimental and pre-clinical investigations for COVID-19.

Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform

Adam Neumann — Mon, 12 Feb 2024 01:06:42 -0600

The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing - a technique divergent from conventional computing methods - to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models traditionally challenged by the intricacy of computing their gradient. Our proposed methodology, termed “quantum mode training”, blends standard gradient updates with an off-gradient direction derived from RBM ground state samples. This approach significantly improves the training efficacy of RBMs, outperforming traditional gradient methods in terms of speed, stability, and minimized converged relative entropy (KL divergence). This study not only highlights the capabilities of the Dynex platform in progressing unsupervised learning techniques but also contributes substantially to the broader comprehension and utilization of neuromorphic computing in complex computational tasks.

The The Frigging Kinship Between Prostate Carcinogenesis and the Genomic Landscape of Indian Males

Souradeep Banerjee, Mainak Sengupta, Bratati Dutta, Soumili Biswas — Thu, 15 Feb 2024 21:14:49 -0600

Prostate Cancer (PCa) is a major global health issue in men over 50 years of age, arising due to complex and often population-specific interactions between genes, environment, and lifestyle. According to GLOBOCAN, PCa incidence in India is on the rise, albeit at a slower rate than that of the global average. This review delves deep into the nuanced relationship between PCa and the genomic landscape of Indian males. The study thoroughly reviews the epidemiology of PCa and examines the contribution of the genetic determinants like polymorphisms, mutations, and methylation patterns; microbial infections as well as the influence of lifestyle habits like diet, smoking, and drinking on the progress and manifestation of cancer among Indian males, thus intending to provide a holistic understanding of the complex interplay driving the malady. This knowledge would not only help us understand the disease mechanisms but also provide a basis for personalized diagnostic, therapeutic, and preventive strategies tailored to India's unique genetic and environmental landscape.