Biology subjects

Kabir, M. F.

Publications and source records attributed to Kabir, M. F..

5 recordsLinked to original sources

bioRxiv2026

Dissecting Genetic Variability and Multi-Trait Performance in Cucumber

Cucumber improvement requires the simultaneous enhancement of yield, earliness, fruit quality, and postharvest performance, yet many breeding studies rely on single-season or single-trait evaluations. This study therefore assessed 18 diverse cucumber genotypes across two consecutive Rabi seasons (2025 and 2026) using 23 agronomic, phenological, yield, fruit-quality, and postharvest traits, of which 12 non-redundant core traits were retained for the principal genetic and multivariate analyses. Genotypic variability, GCV, PCV, broad-sense heritability, genetic advance, interannual correlation, principal component analysis, hierarchical clustering, and multi-trait selection using MGIDI and MTSI were evaluated. Genotype effects were highly significant for nearly all estimable traits in both years (P<0.001). CU-10 produced the highest yield in both years (6.90 and 7.69 t ha-1), followed by CU-11 (4.38 and 4.89 t ha-1) and CU-09 (4.12 and 4.59 t ha-1). Yield showed high GCV (51.80%), PCV (52.21%), broad-sense heritability (98.46%), and GAM (106.04%). Fruit number plant-1 and fruit weight also exhibited high heritability (97.57% and 95.91%) and GAM (80.75% and 59.46%). Interannual repeatability was strongest for fruit number plant-1 (r=0.987), yield (r=0.986), and plant mortality (r=0.981), whereas shelf life was less stable (r=0.426, P=0.078). The first two principal components explained 57.29% of total variation, increasing to 70.92% with PC3. MGIDI selected CU-09, CU-11, and check variety Anuska, whereas MTSI selected CU-11, CU-01, and CU-06. Overall, CU-11 was the only genotype selected by both MGIDI and MTSI, indicating a favourable balance of multi-trait performance and relative interannual stability, while the other selected genotypes provided complementary breeding value for hybridization and further validation. These findings provide an integrated framework for improving productivity, stability, and postharvest performance in cucumber breeding programs.

genetics↗

bioRxiv2023

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students concentration via hyper-parameter tuning

Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students cognitive states. Real-time classification of students confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since real-time EEG data is dynamic and highly dimensional, current approaches have some limitations for predicting mental states based on this data. This paper introduces an optimal deep learning (DL) model for the BCI, ODL-BCI, optimized through hyperparameter tuning techniques to address the limitations of classifying students confusion in real time. Leveraging the "confused student EEG brainwave" dataset, we employ Bayesian optimization to fine-tune hyperparameters of the proposed DL model. The model architecture comprises input and output layers, with several hidden layers whose nodes, activation functions, and learning rates are determined utilizing selected hyperparameters. We evaluate and compare the proposed model with some state-of-the-art methods and standard machine learning (ML) classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Nave Bayes, Random Forest, SVM, and XG Boost, on the EEG confusion dataset. Our experimental results demonstrate the superiority of the optimized DL model, ODL-BCI. It boosts the accuracy between 4% and 9% over the current approaches, outperforming all other classifiers in the process. The ODL-BCI implementation source codes can be accessed by anyone at https://github.com/MdOchiuddinMiah/ODL-BCI.

neuroscience↗

bioRxiv2022

Eosinophilic esophagitis-associated epithelial remodeling may limit esophageal carcinogenesis

Under homeostatic conditions, esophageal epithelium displays a proliferation/differentiation gradient that is generated as proliferative basal cells give rise to suprabasal cells then terminally differentiated superficial cells. This proliferation/differentiation gradient is perturbed in esophageal pathologies both benign and malignant. Esophageal cancer is among the deadliest forms of human malignancy with 5-year survival rates of <20%. Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are the two most common subtypes of esophageal cancer. Gastroesophageal reflux disease (GERD) is a primary risk factor for EAC. Although GERD and the food allergy-mediated condition eosinophilic esophagitis (EoE) are both associated with chronic esophageal inflammation and epithelial remodeling, including basal cell hyperplasia, epidemiological evidence suggests that EoE patients do not develop esophageal malignancy. Here, we perform single cell RNA-sequencing in murine models of EoE and ESCC to delineate the effects that these two conditions have specifically upon the cellular landscape of esophageal epithelium. In mice with EoE or ESCC, we find expansion of cell populations as compared to normal esophageal epithelium. In mice with EoE, we detect expansion of 4 suprabasal populations coupled with depletion of 4 basal cell populations. By contrast, mice with ESCC display expansion of 4 basal populations as well as depletion of 3 superficial populations. We further evaluated modules of co-expressed genes in EoE- and ESCC-enriched epithelial cell clusters. Senescence, glucocorticoid receptor signaling, and granulocyte-macrophage colony-stimulating factor pathways were associated with EoE-enriched clusters while pathways associated with cell proliferation and metabolism were identified in ESCC-enriched clusters. Finally, by pairing murine models of EoE and ESCC, we demonstrate that exposure to EoE inflammation limits esophageal carcinogenesis. Our findings provide the first functional investigation of the relationship between EoE and esophageal cancer and suggest that esophageal epithelial remodeling events occurring in response to EoE inflammation may limit act to esophageal carcinogenesis which may have future implications for leveraging allergic inflammation-associated alterations in epithelial biology to prevent and/or treat esophageal cancer.

cell biology↗

bioRxiv2021

Single cell transcriptomic analysis reveals cellular diversity of murine esophageal epithelium and age-associated mitochondrial dysfunction

Stratified squamous epithelium of the esophagus is comprised of basal keratinocytes that execute a terminal differentiation program in overlying suprabasal and superficial cell layers. Although morphologic progression coupled with expression of specific molecular markers has been characterized along the esophageal epithelial differentiation gradient, the molecular heterogeneity within the cell types along this trajectory has yet to be classified at the level of single cell resolution. To explore the molecular characteristics of esophageal keratinocytes along the squamous differentiation continuum, we performed single cell RNA-Sequencing transcriptomic profiling of 7,972 cells from murine esophageal epithelial sheets. We identified 8 distinct cell clusters in esophageal epithelium, unveiling an unexpected level of diversity, particularly among basal cells. We further mapped the cellular pathways and lineage trajectories within basal, suprabasal, and superficial clusters as well as within the heterogeneous basal cell populations, providing a comprehensive molecular view of esophageal epithelial cells in the context of squamous differentiation. Finally, we explored the impact of tissue aging upon esophageal epithelial cell clusters and demonstrated that mitochondrial dysfunction is a feature of aging in normal esophageal epithelium. These studies provide an unparalleled molecular perspective on murine esophageal keratinocytes that will serve as a valuable resource for dissecting cell type-specific roles in esophageal biology under conditions of homeostasis, aging, and tissue pathology.

molecular biology↗

bioRxiv2019

Studies on Biochemical Changes and Prolactin Level Evaluation in Patients with Chronic Kidney Disease

BackgroundChronic kidney disease (CKD) is progressive loss in kidney function over a period of months or years. CKD is an internationally recognized public health problem affecting 5-10% of the world population and day-by-day the number of cases are increasing at an alarming rate. In CKD, raised levels of prolactin in blood may cause vascular derangements which might lead to worse cardiovascular consequences in CKD patients.\n\nObjectivesTo assess serum creatinine, hemoglobin (Hb), urea, red blood cell (RBC), protein creatinine ratio (PCR) and prolactin in CKD patients.\n\nMaterial and MethodsThis study included 110 patients, 61 were males and 49 were females and their age range 1 to more than 60 years. The control group also consisted of same number of participants as the patients; who were free from signs and symptoms of kidney disease and prolactin hormone disorders.\n\nResultsThe study shows that all the biochemical parameters in CKD patients were found to be significantly high compared with control group (P<0.001). Serum prolactin concentrations in CKD patients were also increased significantly compared with control group (P[≤] 0.05). It was found that level of prolactin hormone secretion was higher in male CKD patients than male control.\n\nConclusionAlthough males are more prone to chronic kidney disease, but the percentage of females is not negligible. All the biochemical parameters and prolactin level changed significantly in the CKD patients. It is interesting that in case of CKD, males prolactin secretion becomes higher.

biochemistry↗