Biology subjects

Hussain, M. A.

Publications and source records attributed to Hussain, M. A..

8 recordsLinked to original sources

bioRxiv2026

Dissecting the genetic basis of DUS characters in Pakistani wheat varieties

Distinctness, Uniformity and Stability (DUS) testing is fundamental to varietal characterization and the protection of Plant Breeders Rights (PBRs), but conventional DUS assessment relies predominantly on morphological characteristics whose expression may be influenced by environmental conditions, developmental stage and observer-related variation. Integrating character-specific molecular markers with conventional DUS assessment may therefore improve the reproducibility and genetic resolution of varietal identification. This study investigated the genetic architecture of DUS characteristics in a panel of 102 historical and modern Pakistani wheat varieties using genome-wide association studies (GWAS). Phenotypic variation was evaluated using distribution, correlation and hierarchical clustering analyses, while the germplasm was genotyped using a wheat genotyping-by-targeted sequencing platform. After quality filtering, 21,670 high-quality single-nucleotide polymorphisms (SNPs) were retained for analyses of genetic diversity, population structure, linkage disequilibrium (LD) and marker-trait associations. Population structure analysis indicated extensive admixture, with cross-entropy supporting K = 9 ancestral components, whereas hierarchical genetic clustering separated the germplasm into five broad groups. The first three principal components explained 8.09%, 7.20% and 5.39% of the total genetic variation, respectively. GWAS using BLINK and FarmCPU identified significant associations for several diagnostically important DUS characteristics. Growth habit was strongly associated with SNP 1B_129176704, with an additional signal on chromosome 5B. Shattering showed its strongest association at 1D_445256324, accompanied by several closely linked SNPs in the same region and a secondary significant locus at 7B_703502819. Flag leaf length was associated with a cluster of SNPs on chromosome 6A, while tiller density showed a major association with 5D_205488820. These findings demonstrate substantial phenotypic and genetic diversity within Pakistani wheat germplasm and identify genomic regions that merit validation as character-specific molecular tools to complement conventional DUS testing and PBR implementation.

plant biology↗

bioRxiv2025

Single pointwise samples of electric field on a neuron model cannot predict activation threshold by brain stimulation

BackgroundSome computational models of neural activation by transcranial magnetic stimulation overestimate the electric field (E-field) threshold compared to in vivo measurements. A recent study proposed a statistical method to account for the influence of microscopic perturbations to the E-field. The method, however, relies on the unsubstantiated assumption that thresholds can be predicted by single pointwise samples of the E-field strength along neural cables. ObjectiveWe analyzed neural responses to E-field with microscopic perturbations and demonstrate via theoretical derivation and simulations that neural activation is not determined by pointwise E-field amplitude but rather by spatial integration of the E-field along the neural cable. Therefore, the influence of microscopic E-field perturbations is negligible due to the spatiotemporal filtering by the neural membrane and axoplasm. MethodsWe derive the axial and transmembrane currents in a neural cable for an imposed E-field with microscopic perturbations. We simulate neural activation thresholds of unmyelinated and myelinated axons in two stimulation scenarios and compare thresholds for E-field activation with and without perturbations. ResultsIn the theoretical derivation, the perturbation terms average out to zero on larger spatial scales indicating that they do not influence neural activation thresholds. Simulated thresholds with the E-field spatial perturbations present had negligible differences (< 3.4%) compared to those without. ConclusionSingle point samples of the microscopic E-field on a neural cable cannot predict neural activation thresholds. Neural simulations should be used to determine any influence of the E-field spatial perturbations. The latter, however, are unlikely to account for the difference between experimental and simulated E-field thresholds.

bioengineering↗

bioRxiv2023

Enhancing Neurocognitive Outcome Prediction in Congenital Heart Disease Patients: The Role of Brain Age Biomarkers and Beyond

This paper aimed to investigate the predictive power of combining demographic, socioeconomic, and genetic factors with a brain MRI-based quantified measure of accelerated brain aging (referred to as deltaAGE) for neurocognitive outcomes in adolescents and young adults with Congenital Heart Disease (CHD). Our hypothesis posited that including the brain age biomarker (deltaAGE) would enhance neurocognitive outcome predictions compared to models excluding it. We conducted comprehensive analyses, including leave-one-subject-out and leave-one-group-out cross-validation techniques. Our results demonstrated that the inclusion of deltaAGE consistently improved prediction performance when considering the Pearson correlation coefficient, a preferable metric for this study. Notably, the deltaAGE -augmented models consistently outperformed those without deltaAGE across all cross-validation setups, and these correlations were statistically significant (p-value < 0.05). Therefore, our hypothesis that incorporating the brain-age biomarker alongside demographic, socioeconomic, and genetic factors enhances neurocognitive outcome predictions in adolescents and young adults with CHD is supported by the findings.

bioinformatics↗

bioRxiv2023

Can deep learning predict human intelligence from structural brain MRI?

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. Nevertheless, population-level association does not fully account for individual variability in intelligence. To address this, individual prediction studies emerge recently. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subjects verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, FSIQ), which reflect both fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input images, six deep learning models, and two outcome settings, on 850 autistic and healthy subjects 6-64 years of age. Results show promise with statistical significance, and also open up questions inviting further future studies.

bioengineering↗

bioRxiv2023

Influence of Demographic, Socio-economic, and Brain Structural Factors on Adolescent Neurocognition: A Correlation Analysis in the ABCD Initiative

The Adolescent Brain Cognitive Development (ABCD) initiative is a longitudinal study aimed at characterizing brain development from childhood through adolescence and identifying key biological and environmental factors that influence this development. The study measures neurocognitive abilities across a multidimensional array of functions, with a focus on the critical period of adolescence during which physical and socio-emotional changes occur and the structure of the cortical and white matter changes. In this study, we perform a correlation analysis to examine the linear relation of adolescent neurocognition functions with the demographic, socio-economic, and magnetic resonance imaging-based brain structural factors. The overall goal is to obtain a comprehensive understanding of how natural and nurtural factors influence adolescent neurocognition. Our results on > 10,000 adolescents show many positive and negative statistical significance interrelations of different neurocognitive functions with the demographic, socioeconomic, and brain structural factors, and also open up questions inviting further future studies.

bioinformatics↗

bioRxiv2022

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

An outbreak of Monkeypox has been reported in 75 countries so far, and it is spreading at a fast pace around the world. The clinical attributes of Monkeypox resemble those of Smallpox, while skin lesions and rashes of Monkeypox often resemble those of other poxes, for example, Chickenpox and Cowpox. These similarities make Monkeypox detection challenging for healthcare professionals by examining the visual appearance of lesions and rashes. Additionally, there is a knowledge gap among healthcare professionals due to the rarity of Monkeypox before the current outbreak. Motivated by the success of artificial intelligence (AI) in COVID-19 detection, the scientific community has shown an increasing interest in using AI in Monkeypox detection from digital skin images. However, the lack of Monkeypox skin image data has been the bottleneck of using AI in Monkeypox detection. Therefore, in this paper, we used a web-scrapping-based Monkeypox, Chickenpox, Smallpox, Cowpox, Measles, and healthy skin image dataset to study the feasibility of using state-of-the-art AI deep models on skin images for Monkeypox detection. Our study found that deep AI models have great potential in the detection of Monkeypox from digital skin images (precision of 85%). However, achieving a more robust detection power requires larger training samples to train those deep models.

bioengineering↗

bioRxiv2022

A Web-scrapped Skin Image Database of Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles

Monkeypox has emerged as a fast-spreading disease around the world and an outbreak has been reported in 75 countries so far. Although the clinical attributes of Monkeypox are similar to those of Smallpox, skin lesions and rashes caused by Monkeypox often resemble those of other types of pox, for example, chickenpox and cowpox. This scenario makes an early diagnosis of Monkeypox challenging for the healthcare professional just by observing the visual appearance of lesions and rashes. The rarity of Monkeypox before the current outbreak further created a knowledge gap among healthcare professionals around the world. To tackle this challenging situation, scientists are taking motivation from the success of supervised machine learning in COVID-19 detection. However, the lack of Monkeypox skin image data is making the bottleneck of using machine learning in Monkeypox detection from patient skin images. Therefore, in this project, we introduce the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. We used web-scraping to collect Monkeypox, Chickenpox, Smallpox, Cowpox, and Measles infected skin as well as healthy skin images to build a comprehensive image database and make it publicly available. We believe that our database will facilitate the development of baseline machine learning algorithms for early detection of Monkeypox in clinical settings. Our dataset is available at the following Kaggle link: https://www.kaggle.com/datasets/arafathussain/monkeypox-skin-image-dataset-2022.

bioinformatics↗

bioRxiv2018

Novel Method for Viability Assessment of Bifidobacterium longum ATCC 15707 on Non-dairy Foods during drying

This study demonstrates a new technique for separating and purifying viable microbes from samples that interfere with viability staining. The viability of Bifidobacterium longum ATCC 15707 was assessed using PBDC to separate bacteria from complex non-dairy food matrices and Quantitative Fluorescence Microscopy (QFM) to determine individual cells using LIVE/DEAD BacLight bacterial viability staining. Water agar (3%) was used to retain cells of B. longum and offered a lower fluorescence background with BacLight viability staining, compared with fixation on polycarbonate (PC) black membrane. The effect of drying temperatures and non-dairy foods on viability of B. longum was assessed. B. longum coated on oat, peanut or raisin was separated by filtration, low- and high-speed centrifugation, flotation and sedimentation buoyant density centrifugation. Purified cells were subsequently deposited on water agar for rehydration followed by LIVE/DEAD BacLight viability staining and enumeration. Conventional plate counting was also conducted to compare viability results. Finally, the applicability of this novel method for viability assessment was demonstrated and informative information of cell membrane damages of B. longum incorporated onto non-dairy foods during 24 h drying was observed. Viability assessment of B. longum coated onto oat, peanut, or raisin was much lower by plate counting compared to viability staining. Drying appeared to have a greater impact when viability was assessed by plate counting compared to viability staining.\n\nIMPORTANCEEnumeration of viable beneficial bacteria from function foods presents a significant bottle neck for product development and quality control. Interference with microscopic and/or fluorescent techniques by ingredients, time required to incubate plated microbes, and the transient nature of the colony forming unit make rapid assessment of viable bacteria difficult. Viability assessment of Bifidobacterium longum ATCC 15707 by Percoll Buoyant Density Gradient Centrifugation with LIVE/DEAD BacLight viability staining on water agar (3%) was in agreement with serial dilution enumeration. Without the need for incubation viability assessment by staining provided a more rapid means to assess the impact of drying on the viability of B. longum coated onto oat, peanut or raisin.

microbiology↗