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"Ghafoor, Abdul"
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Estimating genetic variability among diverse lentil collections through novel multivariate techniques
Lentil is an important food legume throughout the world and Pakistan stands at 18 th position with 8,610 tons production from 17,457 hectares. It is rich in protein, carbohydrates, fat, fiber, and minerals that can potentially meet food security and malnutrition issues, particularly in South Asia. Two hundred and twenty lentil genotypes representing Pakistan (178), Syria (14), and the USA (22) including 6 from unknown origins were studied for yield, yield contributing traits, and cooking time (CT). Genotype 6122 (Pakistan) performed the best during both years with seed yield per plant (SY) 68±1.7 g, biological yield per plant (BY) 264±2.8 g, pod size (PS) 0.61±0.01 cm, number of seeds per pod (NSP) 2, cooking time (CT) 11 minutes, with no hard seed (HS). The genotypes 6122 (Pakistan) and 6042 (Syria) produced the highest BY, hence these have the potential to be an efficient source of fodder, particularly during extreme winter months. The genotypes 5698 (Pakistan) and 6015 (USA) were late in maturity during 2018–19 while 24783 and 5561 matured early in 2019. A minimum CT of 10 minutes was taken by the genotypes 6074 and 5745 of Pakistani origin. The lowest CT saves energy, time, and resources, keeps flavor, texture, and improves protein digestibility, hence the genotypes with minimum CT are recommended for developing better lentil cultivars. Pearson correlation matrix revealed significant association among several traits, especially SY with BY, PS, and NSP which suggests their use for the future crop improvement program. The PCA revealed a considerable reduction in components for the selection of suitable genotypes with desired traits that could be utilized for future lentil breeding. Structural Equational Model (SEM) for SY based on covariance studies indicated the perfect relationship among variables. Further, hierarchical cluster analysis establishes four clusters for 2017–18, whereas seven clusters for 2018–19. Cluster 4 of 2017–18 and cluster 5 of 2018–19 exhibited the genotypes with the best performance for most of the traits (SY, BY, PS, NSP, CT, and HS). Based on heritability; HSW, SY, BY, NSP were highly heritable, hence these traits are expected for selecting genotypes with genes of interest and for future lentil cultivars. In conclusion, 10 genotypes (5664, 5687, 6084, 6062, 6122, 6058, 6087, 5689, 6042 and 6074) have been suggested to evaluate under multi-location environments for selection of the best one/s or could be utilized in hybridization in future lentil breeding programs.
Journal Article
Climate as the primary moderator: towards context-driven design and implementation of vertical greenery systems for stormwater management
Urbanisation intensifies stormwater management challenges by expanding impervious surfaces, increasing flood risk and degrading water quality. Vertical greenery systems (VGS) are increasingly promoted as nature-based solutions for space-constrained cities, yet their performance remains highly variable and context-dependent. This systematic review, conducted in accordance with preferred reporting items for systematic reviews and meta-analyses guidelines and synthesising evidence from 34 peer-reviewed studies, demonstrates that VGS hydrological efficacy is not a universal constant but is fundamentally moderated by local climatic conditions. Climate acts as the primary filter, shaping system function across tropical, temperate, Mediterranean, arid, and cold zones, each presenting distinct opportunities and constraints. Beyond climate, performance is refined by system typology (green façades versus living walls), plant functional traits (notably root architecture), and substrate composition, which together determine trade-offs between runoff delay, retention, and water quality improvement. The review further reveals a stark divergence in adoption barriers: in the Global North, high costs, regulatory complexity, and data gaps create institutional inertia, whereas in the Global South, limited awareness, misaligned priorities, and weak policy frameworks relegate stormwater management to a low priority. To address these challenges, we propose a five-pillar implementation framework centred on climate-responsive design protocols, advanced economic valuation of co-benefits, adaptive governance, long-term monitoring, and context-sensitive community engagement. This review provides actionable, evidence-based guidance for integrating VGS as resilient, equitable, and multifunctional components of sustainable urban water management, tailored not to global templates, but to local environmental and socio-institutional realities.
Journal Article
Stability, Intracellular Delivery, and Release of siRNA from Chitosan Nanoparticles Using Different Cross-Linkers
Chitosan (CS) nanoparticles have been extensively studied for siRNA delivery; however, their stability and efficacy are highly dependent on the types of cross-linker used. To address this issue, three common cross-linkers; tripolyphosphate (TPP), dextran sulphate (DS) and poly-D-glutamic acid (PGA) were used to prepare siRNA loaded CS-TPP/DS/PGA nanoparticles by ionic gelation method. The resulting nanoparticles were compared with regard to their physicochemical properties including particle size, zeta potential, morphology, binding and encapsulation efficiencies. Among all the formulations prepared with different cross linkers, CS-TPP-siRNA had the smallest particle size (ranged from 127 ± 9.7 to 455 ± 12.9 nm) with zeta potential ranged from +25.1 ± 1.5 to +39.4 ± 0.5 mV, and high entrapment (>95%) and binding efficiencies. Similarly, CS-TPP nanoparticles showed better siRNA protection during storage at 4˚C and as determined by serum protection assay. TEM micrographs revealed the assorted morphology of CS-TPP-siRNA nanoparticles in contrast to irregular morphology displayed by CS-DS-siRNA and CS-PGA-siRNA nanoparticles. All siRNA loaded CS-TPP/DS/PGA nanoparticles showed initial burst release followed by sustained release of siRNA. Moreover, all the formulations showed low and concentration-dependent cytotoxicity with human colorectal cancer cells (DLD-1), in vitro. The cellular uptake studies with CS-TPP-siRNA nanoparticles showed successful delivery of siRNA within cytoplasm of DLD-1 cells. The results demonstrate that ionically cross-linked CS-TPP nanoparticles are biocompatible non-viral gene delivery system and generate a solid ground for further optimization studies, for example with regard to steric stabilization and targeting.
Journal Article
Parents’ financial socialization or socioeconomic characteristics: which has more influence on Gen-Z’s financial wellbeing?
This research aims to explore the effectiveness of various approaches for promoting financial wellbeing among Gen-Z, with a particular focus on the parental role in shaping financial attitudes and behavior, using the theoretical lens of family financial socialization theory. The study utilized a mixed-methods approach to obtain in-depth findings on parental financial socialization and parental socioeconomic characteristics for Gen-Zs’ financial wellbeing. The qualitative findings revealed that parents use different strategies to financially socialize children by involving them in savings, financial decisions, and household and personal finances. Furthermore, it is found that parents are more inclined to teach daughters than sons about finances because of the expectations that females handle the household finances. Likewise, quantitative findings revealed that the father’s education level negatively influences Gen-Z’s financial attitude. This study strengthens the concept of family socialization and establishes a ground to explore potential mechanisms of action and implications for future research and practice.
Journal Article
Transformation of 2D Roesser into Causal Recursive Separable Denominator Model and Decomposition into 1D Systems
A transformation for the 2D system is proposed to transform the original 2D systems into causal recursive separable denominator systems. 2D causal recursive separable denominator systems with minimal rank-decomposition can be written into two 1D systems for ease in the analysis and model order reduction.
Journal Article
Insights into Drought Stress Signaling in Plants and the Molecular Genetic Basis of Cotton Drought Tolerance
Drought stress restricts plant growth and development by altering metabolic activity and biological functions. However, plants have evolved several cellular and molecular mechanisms to overcome drought stress. Drought tolerance is a multiplex trait involving the activation of signaling mechanisms and differentially expressed molecular responses. Broadly, drought tolerance comprises two steps: stress sensing/signaling and activation of various parallel stress responses (including physiological, molecular, and biochemical mechanisms) in plants. At the cellular level, drought induces oxidative stress by overproduction of reactive oxygen species (ROS), ultimately causing the cell membrane to rupture and stimulating various stress signaling pathways (ROS, mitogen-activated-protein-kinase, Ca2+, and hormone-mediated signaling). Drought-induced transcription factors activation and abscisic acid concentration co-ordinate the stress signaling and responses in cotton. The key responses against drought stress, are root development, stomatal closure, photosynthesis, hormone production, and ROS scavenging. The genetic basis, quantitative trait loci and genes of cotton drought tolerance are presented as examples of genetic resources in plants. Sustainable genetic improvements could be achieved through functional genomic approaches and genome modification techniques such as the CRISPR/Cas9 system aid the characterization of genes, sorted out from stress-related candidate single nucleotide polymorphisms, quantitative trait loci, and genes. Exploration of the genetic basis for superior candidate genes linked to stress physiology can be facilitated by integrated functional genomic approaches. We propose a third-generation sequencing approach coupled with genome-wide studies and functional genomic tools, including a comparative sequenced data (transcriptomics, proteomics, and epigenomic) analysis, which offer a platform to identify and characterize novel genes. This will provide information for better understanding the complex stress cellular biology of plants.
Journal Article
Role of precision medicine in obstructive sleep apnoea
Obstructive sleep apnoea is a substantial clinical and public health problem because it contributes to harmful effects on quality of life, daytime symptoms, road traffic incidents, and cardiometabolic disease. Increasingly, obstructive sleep apnoea is recognised as a heterogeneous disease, and patients have varied susceptibility to long term complications and different responses to treatment. This narrative review summarises the current knowledge of precision medicine in obstructive sleep apnoea, particularly the role of symptom clusters, polysomnogram phenotypes, physiological endotypes, and circulating biomarkers in defining subtypes. In the near future, the prognostic accuracy of these measures in predicting long term complications in obstructive sleep apnoea will likely be improved, together with better matching of treatments to disease subtypes.
Journal Article
MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data
Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time.
Journal Article