Search Results (51)

Brassinosteroids (BRs) play an essential role in regulating plant metabolic pathways that influence growth, development, and stress responses. However, their role in alleviating cobalt (Co) stress has not been extensively studied. This research aimed to assess the impact of exogenous BRs (0.1 µM) on maize subjected to Co stress (300 µM) in a hydroponic experiment. The results indicated that BR supplementation significantly decreased the accumulation of H₂O₂ by 17.79 and 16.66%, O₂^•− by 28.5 and 21.48%, and MDA by 37.5 and 37.9% in shoot and root, respectively, as compared to Co stress alone. Additionally, BRs enhanced endogenous levels of BRs (31.16%) and growth hormones (IAA 50.8%, JA 57.8%, GA 52.5%), and reduced Co contents by 26.3% in roots and 36.1% in shoots. BRs enhanced antioxidant enzyme activity both in the shoot and root, leading to reduced ROS levels as confirmed by laser scanning confocal microscopy. Furthermore, BRs increased phenols, flavonoids, and soluble sugars, and elevated total protein content. Observations from transmission electron microscopy indicated reduced ultrastructural damage in plants treated with BRs under Co stress. Taken together, this study highlights the role of BRs in alleviating Co stress in maize, demonstrating their efficiency in enhancing stress tolerance by modulating hormone levels and key metabolic processes. Full article

Background and Objectives: Growing evidence suggests a strong relationship between hypertension and cancer, which can increase the risk of poor prognosis. However, data regarding mortality related to cancer and hypertension are limited. Our study aims to analyze the mortality trends related to cancer and hypertension in the United States from 1999 to 2023. Materials and Methods: A retrospective observational analysis was conducted using mortality data for the adult U.S. population from 1999 to 2023, retrieved from the CDC WONDER database using death certificates. Age-adjusted mortality rates (AAMRs) were calculated, and annual percentage changes (APCs) were analyzed using JoinPoint Regression. Results: From 1999 to 2023, a total of 1,406,107 deaths related to cancer and hypertension were recorded in the United States. The AAMR increased from 12.59 in 1999 to 35.49 in 2023. Males had a higher mortality rate compared to women throughout the study period (AAMR; 30.3 vs. 20.4). Non-Hispanic (NH) Black Americans, or African Americans had the highest mortality rates, followed by NH white, Hispanic or Latino groups, and other NH groups. The highest AAMR was observed in the South, followed by the Midwest, the Northeast, and the West. Rural areas had higher mortality rates compared to urban areas. Conclusions: Cancer- and hypertension-related mortality rates have consistently increased in the United States from 1999 to 2023, particularly affecting males, NH Black Americans, the southern region, and rural areas. The trends highlight the need for targeted prevention, including early screening, lifestyle changes, and treatment adherence. Full article

The study aimed to examine the effects of dietary supplementation with microencapsulated butyric acid (EBA) and yeast culture (YC) in broiler diets. A total of 450 Ross-308 broiler chicks were selected and randomly allocated to five dietary treatments with six replicates (15 birds per replicate) in a complete block design. The experimental diets included the following treatments: (1) Negative control (NC) with basal diet without any additives. (2) Positive control (PC) with basal diet + 0.2 g/kg enramycin. (3) EBA, basal diet + 0.3 g/kg EBA. (4) YC, basal diet + 1 g/kg YC. (5) EBA+YC, basal diet + 0.3 g/kg EBA and 1 g/kg YC. The results indicated a non-significant effect on feed intake (FI) during the experiment periods. However, the EBA+YC treatment exhibited significantly increased body weight gain (BWG), better feed conversion ratio (FCR), and enhanced carcass traits (p < 0.05) compared to other treatments. A significant effect was observed for the immune organ weights and ND titters. Villus height (VH) and the ratio of villus height-to-crypt depth (VH: CD) were noted for EBA+YC across all other treatments. Ileal microbial analysis revealed a significantly lower count of E. coli and Salmonella in the ileal digesta of broiler chickens in the EBA+YC treatment compared to the NC group (p < 0.05). In conclusion, dietary supplementation with any supplement positively influences the broiler’s performance, carcass characteristics, gut health, and immune status over the NC group. More pronounced improvements were obtained from the EBA+YC group, indicating that EBA and YC had a synergistic effect on broilers. Full article

Mammalian fertilization is a complex and highly regulated process that has garnered significant attention, particularly with advancements in assisted reproductive technologies such as in vitro fertilization (IVF). The fusion of egg and sperm involves a sequence of molecular and cellular events, including capacitation, the acrosome reaction, adhesion, and membrane fusion. Critical genetic factors, such as IZUMO1, JUNO (also known as FOLR4), CD9, and several others, have been identified as essential mediators in sperm–egg recognition and membrane fusion. Additionally, glycoproteins such as ZP3 within the zona pellucida are crucial for sperm binding and triggering the acrosome reaction. Recent gene-editing technologies, such as CRISPR/Cas9 and conditional knockout models, have facilitated the functional annotation of genes such as SPAM1 and ADAM family members, further elucidating their roles in capacitation and adhesion. Furthermore, the integration of CRISPR-Cas9 with omics technologies, including transcriptomics, proteomics, and lipidomics, has unlocked new avenues for identifying previously unknown genetic players and pathways involved in fertilization. For instance, transcriptomics can uncover gene expression profiles during gamete maturation, while proteomics identifies key protein interactions critical for processes such as capacitation and the acrosome reaction. Lipidomics adds another dimension by revealing how membrane composition influences gamete fusion. Together, these tools enable the discovery of novel genes, pathways, and molecular mechanisms involved in fertility, providing insights that were previously unattainable. These approaches not only deepen our molecular understanding of fertility mechanisms but also hold promise for refining diagnostic tools and therapeutic interventions for infertility. This review summarizes the current molecular insights into genes orchestrating fertilization and highlights cutting-edge methodologies that propel the field toward novel discoveries. By integrating these findings, this review aims to provide valuable knowledge for clinicians, researchers, and technologists in the field of reproductive biology and assisted reproductive technologies. Full article

In this research, we propose an energy-management scheme for domestic users, which uses the load-shifting strategy of demand-side management (DSM). The research demonstrates that the energy sector’s circular debt problem from the viewpoint of a developing country can be solved by incorporating DSM. Circular debt is a chain reaction that arises when the balance between cost and energy supply collapses. Circular debt is an ongoing problem in Pakistan, where economic crises are continuously posing a threat to the energy sector. DSM is envisioned to address these concerns in a dynamic way thoroughly: introducing DSM can minimize circular debt, increase grid reliability, and smooth the supply–demand operation. Circular debt is directly linked with the subsidy offered by the government of Pakistan. As the cost of energy utilized by consumers increases, the subsidy also increases due to the direct link between the two entities. Therefore, the subsidy can be controlled by energy-consumption management with the adoption of DSM. This study addresses that by incorporating optimized cost solutions, circular debt can be regulated to improve the economy of the energy sector. A genetic algorithm is used as an optimization tool to manage demand and generate an optimal schedule under a dynamic electricity pricing signal. To support the utility, a solar system is used as a secondary energy source. Finally, the results show a curtailment in the payable costs at both the consumer and government ends, thus reducing the circular debt in the bigger picture. The reduction is 18% without and 41% with renewable energy support. Full article

The world faced COVID-19, which was a threat to public health and disturbed the educational system and economic stability. Educational institutes were closed for a longer period, and students faced difficulty to complete their syllabus. The government adopted a policy of “suspending classes without stopping learning” to continue education activities. However, student satisfaction with online education is a growing concern. Satisfaction of students is an important indicator of academic quality. Therefore, this study attempts to investigate the influencing factors behind learning satisfaction using information from 335 students from various institutes in Pakistan. This research examined the impact of computer and internet knowledge, instructor and course material, and Learning Management Systems (LMS) on learning satisfaction. The path coefficients were obtained via Partial Least Square-Structural Equation Modeling (PLS-SEM). The LMS is a tool that facilitates the learning process with the provision of all types of educational material. The path coefficient was more in the case of LMS (0.489), which indicates its positive and significant role to attain learning satisfaction. The instructor and course material ordered second (0.261), which shows that the quality of an instructor and course material also plays a positive role to attain learning satisfaction. The computer and internet are essential ingredients of online education, showing a significant and positive path coefficient (0.123), implying that computer and internet knowledge could enhance learning satisfaction. The universities should develop their LMS to implement online education with quality course materials. It is also vital that the instructor should be up to date with modern learning techniques while ensuring internet connectivity, especially in rural areas. The government should provide an internet connection to students at discounted rates. Full article

Novel ultrafiltration (UF) polymer membranes were prepared to enhance the antifouling features and filtration performance. Several ultrafiltration polymer membranes were prepared by incorporating different concentrations of water-soluble cationic poly [2-(dimethyl amino) ethyl methacrylate] (PDMAEMA) into a homogenous casting solution of polyethersulfone (PES). After adding PDMAEMA, the effects on morphology, hydrophilicity, thermal stability, mechanical strength, antifouling characteristics, and filtration performance of these altered blended membranes were investigated. It was observed that increasing the quantity of PDMAEMA in PES membranes in turn enhanced surface energy, hydrophilicity, and porosity of the membranes. These new modified PES membranes, after the addition of PDMAEMA, showed better filtration performance by having increased water flux and a higher flux recovery ratio (FRR%) when compared with neat PES membranes. For the PES/PDMAEMA membrane, pure water flux with 3.0 wt.% PDMAEMA and 0.2 MPa pressure was observed as (330.39 L·m⁻²·h⁻¹), which is much higher than that of the neat PES membrane with the value of (163.158 L·m⁻²·h⁻¹) under the same conditions. Furthermore, the inclusion of PDMAEMA enhanced the antifouling capabilities of PES membranes. The total fouling ratio (TFR) of the fabricated PES/PDMAEMA membranes with 3.0 wt.% PDMAEMA at 0.2 MPa applied pressure was 36 percent, compared to 64.9 percent for PES membranes. Full article

Hypotrichosis is an uncommon type of alopecia (hair loss) characterized by coarse scalp hair caused by the reduced or fully terminated activity of the Lipase-H (LIPH) enzyme. LIPH gene mutations contribute to the development of irregular or non-functional proteins. Because several cellular processes, including cell maturation and proliferation, are inhibited when this enzyme is inactive, the hair follicles become structurally unreliable, undeveloped, and immature. This results in brittle hair, as well as altered hair shaft development and structure. Because of these nsSNPs, the protein’s structure and/or function may be altered. Given the difficulty in discovering functional SNPs in genes associated with disease, it is possible to assess potential functional SNPs before conducting broader population investigations. As a result, in our in silico analysis, we separated potentially hazardous nsSNPs of the LIPH gene from benign representatives using a variety of sequencing and architecture-based bioinformatics approaches. Using seven prediction algorithms, 9 out of a total of 215 nsSNPs were shown to be the most likely to cause harm. In order to distinguish between potentially harmful and benign nsSNPs of the LIPH gene, in our in silico investigation, we employed a range of sequence- and architecture-based bioinformatics techniques. Three nsSNPs (W108R, C246S, and H248N) were chosen as potentially harmful. The present findings will likely be helpful in future large population-based studies, as well as in drug discovery, particularly in the creation of personalized medicine, since this study provides an initial thorough investigation of the functional nsSNPs of LIPH. Full article

Bardet–Biedl syndrome (BBS) is a rare clinically and genetically heterogeneous autosomal recessive multi-systemic disorder with 22 known genes. The primary clinical and diagnostic features include six different hallmarks, such as rod–cone dystrophy, learning difficulties, renal abnormalities, male hypogonadism, post-axial polydactyly, and obesity. Here, we report nine consanguineous families and a non-consanguineous family with several affected individuals presenting typical clinical features of BBS. In the present study, 10 BBS Pakistani families were subjected to whole exome sequencing (WES), which revealed novel/recurrent gene variants, including a homozygous nonsense mutation (c.94C>T; p.Gln32Ter) in the IFT27 (NM_006860.5) gene in family A, a homozygous nonsense mutation (c.160A>T; p.Lys54Ter) in the BBIP1 (NM_001195306.1) gene in family B, a homozygous nonsense variant (c.720C>A; p.Cys240Ter) in the WDPCP (NM_015910.7) in family C, a homozygous nonsense variant (c.505A>T; p.Lys169Ter) in the LZTFL1 (NM_020347.4) in family D, pathogenic homozygous 1 bp deletion (c.775delA; p.Thr259Leufs*21) in the MKKS/BBS5 (NM_170784.3) gene in family E, a pathogenic homozygous missense variant (c.1339G>A; p.Ala447Thr) in BBS1 (NM_024649.4) in families F and G, a pathogenic homozygous donor splice site variant (c.951+1G>A; p?) in BBS1 (NM_024649.4) in family H, a pathogenic bi-allelic nonsense variant in MKKS (NM_170784.3) (c.119C>G; p.Ser40*) in family I, and homozygous pathogenic frameshift variants (c.196delA; p.Arg66Glufs*12) in BBS5 (NM_152384.3) in family J. Our findings extend the mutation and phenotypic spectrum of four different types of ciliopathies causing BBS and also support the importance of these genes in the development of multi-systemic human genetic disorders. Full article

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m³/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation. Full article

The current study was designed to formulate ternary solid dispersions (TSDs) of dexibuprofen (Dex) by solvent evaporation to augment the solubility and dissolution profile, in turn providing gastric protection and effective anti-inflammatory activity. Initially, nine formulations (S1 to S9) of binary solid dispersions (BSDs) were developed. Formulation S1 comprising a 1:1 weight ratio of Dex and Syloid 244FP^® was chosen as the optimum BSD formulation due to its better solubility profile. Afterward, 20 TSD formulations were developed using the optimum BSD. The formulation containing Syloid 244FP^® with 40% Gelucire 48/16^® (S18) and Poloxamer 188^® (S23) successfully enhanced the solubility by 28.23 and 38.02 times, respectively, in pH 6.8, while dissolution was increased by 1.99- and 2.01-fold during the first 5 min as compared to pure drug. The in vivo gastroprotective study in rats suggested that the average gastric lesion index was in the order of pure Dex (8.33 ± 2.02) > S1 (7 ± 1.32) > S18 (2.17 ± 1.61) > S23 (1.83 ± 1.04) > control (0). The in vivo anti-inflammatory study in rats revealed that the percentage inhibition of swelling was in the order of S23 (71.47 ± 2.16) > S18 (64.8 ± 3.79) > S1 (54.14 ± 6.78) > pure drug (18.43 ± 2.21) > control (1.18 ± 0.64) after 6 h. ELISA results further confirmed the anti-inflammatory potential of the developed formulation, where low levels of IL-6 and TNF alpha were reported for animals treated with S23. Therefore, S23 could be considered an effective formulation that not only enhanced the solubility and bioavailability but also reduced the gastric irritation of Dex. Full article

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km² and shrank to 152 km² by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km²/year from 1980 to 2020. The area of woodland forest decreased by 87 km² (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km². The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales. Full article

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions. Full article

The regeneration of the high-yielding multilocular types has not been attempted, although successful regeneration and transformation in brassica have been done. Here, we report efficient regeneration and transformation protocols for two B. rapa genotypes; UAF11 and Toria. The B. rapa cv UAF11 is a multilocular, non-shattering, and high-yielding genotype, while Toria is the bilocular type. For UAF11 8 shoots and for Toria 7 shoots, explants were observed on MS supplemented with 3 mg/L BAP + 0.4 mg/L NAA + 0.01 mg/L GA₃ + 5 mg/L AgNO₃ + 0.75 mg/L Potassium Iodide (KI), MS salt supplemented with 1 mg/L IBA and 0.37 mg/L KI produced an equal number of roots (3) in UAF11 and Toria. For the establishment of transformation protocols, Agrobacterium-mediated floral dip transformation was attempted using different induction media, infection time, and flower stages. The induction medium III yielded a maximum of 7.2% transformants on half-opened flowers and 5.2% transformants on fully opened flowers in UAF11 and Toria, respectively, with 15 min of inoculation. This study would provide the basis for the improvement of tissue culture and transformation protocols in multilocular and bilocular Brassica genotypes. Full article

Gender classification based on gait is a challenging problem because humans may walk in different directions at different speeds and with varying gait patterns. The majority of investigations in the literature relied on gender-specific joints, whereas the comparison of the lower-body joints in the literature received little attention. When considering the lower-body joints, it is important to identify the gender of a person based on his or her walking style using the Kinect Sensor. In this paper, a logistic-regression-based model for gender classification using lower-body joints is proposed. The proposed approach is divided into several parts, including feature extraction, gait feature selection, and human gender classification. Different joints’ (3-dimensional) features were extracted using the Kinect Sensor. To select a significant joint, a variety of statistical techniques were used, including Cronbach’s alpha, correlation, T-test, and ANOVA techniques. The average result from the Coronbach’s alpha approach was 99.74%, which shows the reliability of the lower-body joints in gender classification. Similarly, the correlation data show a significant difference between the joints of males and females during gait. As the p-value for each of the lower-body joints is zero and less than 1%, the T-test and ANOVA techniques demonstrated that all nine joints are statistically significant for gender classification. Finally, the binary logistic regression model was implemented to classify the gender based on the selected features. The experiments in a real situation involved one hundred and twenty (120) individuals. The suggested method correctly classified gender using 3D data captured from lower-body joints in real-time using the Kinect Sensor with 98.3% accuracy. The proposed method outperformed the existing image-based gender classification systems. Full article