Modelling the impact of climate change on dengue outbreaks and future spatiotemporal shift in Pakistan.

Climate change has a significant impact on the intensity and spread of dengue outbreaks. The objective of this study is to assess the number of dengue transmission suitable days (DTSD) in Pakistan for the baseline (1976-2005) and future (2006-2035, 2041-2070, and 2071-2099) periods under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Moreover, potential spatiotemporal shift and future hotspots of DTSD due to climate change were also identified. The analysis is based on fourteen CMIP5 models that have been downscaled and bias-corrected with quantile delta mapping technique, which addresses data stationarity constraints while preserving future climate signal. The results show a higher DTSD during the monsoon season in the baseline in the study area except for Sindh (SN) and South Punjab (SP). In future periods, there is a temporal shift (extension) towards pre- and post-monsoon. During the baseline period, the top ten hotspot cities with a higher frequency of DTSD are Karachi, Hyderabad, Sialkot, Jhelum, Lahore, Islamabad, Balakot, Peshawar, Kohat, and Faisalabad. However, as a result of climate change, there is an elevation-dependent shift in DTSD to high-altitude cities, e.g. in the 2020s, Kotli, Muzaffarabad, and Drosh; in the 2050s, Garhi Dopatta, Quetta, and Zhob; and in the 2080s, Chitral and Bunji. Karachi, Islamabad, and Balakot will remain highly vulnerable to dengue outbreaks for all the future periods of the twenty-first century. Our findings also indicate that DTSD would spread across Pakistan, particularly in areas where we have never seen dengue infections previously. The good news is that the DTSD in current hotspot cities is projected to decrease in the future due to climate change. There is also a temporal shift in the region during the post- and pre-monsoon season, which provides suitable breeding conditions for dengue mosquitos due to freshwater; therefore, local authorities need to take adaption and mitigation actions.

Heterologous expression of azoreductase-encoding gene azrS of Bacillus sp. MR-1/2 for enhanced azo dye decolorization and wastewater treatment.

In Pakistan, 55% of textile exports are contributed by textile-units of Faisalabad. The effluents of these textile units, being discharged without any treatment, contain the contamination of a huge amount of synthetic azo dyes. The objective of the current research was to evaluate the contribution of an azoreductase-encoding gene (azrS) from a pre-characterized azo dye decolorizing bacterial strain Bacillus sp. MR-1/2 in a high copy number host system (pUC19-T7-Top-T) of Escherichia coli strain DH5α followed by in-silico prediction of azoreductase enzyme (AzrS) function. The recombinant cells that contained azrS had a significantly higher rate of color removal in congo red and reactive black-5 dyes when compared to wild-type MR-1/2 and E. coli DH5α after 72 h of incubation. Moreover, we were able to show that the recombinant strain significantly reduced the values of all tested parameters (pH, EC, turbidity, TSS, and COD) in actual wastewater. In support of our results, it was also predicted through bioinformatics analysis that the deduced azoreductase protein of strain MR-1/2 is linked with the dye decolorization ability of the strain through NAD(P)H-ubiquinone: oxidoreductase activity. Furthermore, we also found that the deduced protein resembled closely related proteins of protein databank in many features, yet some unique features were predicted in the enzyme activity of strain MR-1/2. It was concluded that the recombinant strain could be examined in pilot-scale experiments for textile wastewater treatment.

Green copper nanoparticles from a native Klebsiella pneumoniae strain alleviated oxidative stress impairment of wheat plants by reducing the chromium bioavailability and increasing the growth.

Chromium (Cr) concentration has been increasing substantially in the environment due to industrial and anthropogenic factors. Plants can absorb Cr and undergo unrestrained oxidation cascades, resulting in cell injury. The ameliorative role of biogenic copper nanoparticles to relieve wheat plants from Cr stress by supporting their growth is still unclear. The present work aims at the biosynthesis and characterization of copper nanoparticles (CuNPs) from a native Klebsiella pneumoniae strain, followed by assessment of wheat growth and physiological responses to CuNPs mixed in Cr-rich soil. The taxonomic rank of K. pneumoniae SN35 was established by the 16 S rRNA gene sequence analysis. The properties of biogenic CuNPs were elucidated by using UV-vis spectroscopy, FTIR, XRD, SEM, and TEM. It was found that 19.01-47.47 nm spherical shaped CuNPs were stabilized by different functional groups produced extracellularly by the strain SN35. The XRD data revealed the crystalline nature of CuNPs as a face-centered cubic structure. Different concentrations of CuNPs (0, 25, 50 and 100 mg kg-1 of soil) were added into the soil mixed with 3.5 mg kg-1 K2Cr2O7 and the pots were placed in a growth chamber for 30 days. The results revealed that the CuNPs, at 25 and 50 mg kg-1 of soil, augmented plant growth, biomass, and cellular antioxidants contents, whereas decreased the reactive oxygen species and Cr translocation from soil to roots and shoots as compared to control plants. Overall, the results revealed that the soil amendment of CuNPs could immobilize the Cr in the soil to prevent its translocation to the upper plant parts and support wheat growth by relieving cellular oxidative stress.

Manure storage operations mitigate nutrient losses and their products can sustain soil fertility and enhance wheat productivity.

Livestock manure is a valuable source of nutrients for plants. However, poor handling practices during storage resulted in nutrient losses from the manure and decrement in its nitrogen (N) fertilizer value. We explored the influence of divergent storage methods on manure chemical composition, carbon (C) and N losses to the environment as well as fertilizer value of storage products after their application to the wheat. Fresh buffalo manure (FM) was subjected to different storage operations for a period of ∼6 months, (i) fermentation by covering with a plastic sheet (CM) (ii) placed under the roof (RM) (iii) heap was unturned (SM) to remain stacked at an open space and (iv) manure heap turned monthly (TM) to make compost. During storage, 8, 24, 45 and 46% of the initial Ntotal was lost from CM, RM, SM, and TM, respectively. The respective C losses from these treatments were 16, 34, 47 and 44% of the initial C content. After stored manures application to the wheat crop, mineral N in the soil remained 27% higher in CM (14.1 vs. 11.1 kg ha-1) and 3% (10.8 vs. 11.1 kg ha-1) lower in SM compared to FM treatment. In contrast, microbial biomass C and N was 35 (509 vs.782 mg C kg-1 soil) and 25% (278 vs.370 mg N kg-1 soil) lower in CM than FM treatment, respectively indicating lower N immobilization of CM in the soil. These findings could result in the highest grain yield (5166 kg ha-1) and N uptake (117 kg ha-1) in CM and the lowest in SM treatments (3105 and 61 kg ha-1, respectively). Similarly, wheat crop recovered 44, 15 and 13% N from CM, TM and SM, respectively. Hence, management operations play a critical role in conserving N during storage phase and after stored manure application to the field. Among the studied operations, storing animal manure under an impermeable plastic sheet is a much better and cheaper option for decreasing N losses during storage and improving wheat yield when incorporated into the soil. Therefore, by adopting this manure storage technique, farmers can improve the agro-environmental value of animal manure in Pakistan.

Entropy Generation on Nanofluid Thin Film Flow of Eyring-Powell Fluid with Thermal Radiation and MHD Effect on an Unsteady Porous Stretching Sheet.

This research paper investigates entropy generation analysis on two-dimensional nanofluid film flow of Eyring-Powell fluid with heat amd mass transmission over an unsteady porous stretching sheet in the existence of uniform magnetic field (MHD). The flow of liquid films are taken under the impact of thermal radiation. The basic time dependent equations of heat transfer, momentum and mass transfer are modeled and converted to a system of differential equations by employing appropriate similarity transformation with unsteady dimensionless parameters. Entropy analysis is the main focus in this work and the impact of physical parameters on the entropy profile are discussed in detail. The influence of thermophoresis and Brownian motion has been taken in the nanofluids model. An optima approach has been applied to acquire the solution of modeled problem. The convergence of the HAM (Homotopy Analysis Method) has been presented numerically. The disparity of the Nusslet number, Skin friction, Sherwood number and their influence on the velocity, heat and concentration fields has been scrutinized. Moreover, for comprehension, the physical presentation of the embedded parameters are explored analytically for entropy generation and discussed.

Mapping of a new stem rust resistance gene Sr49 in chromosome 5B of wheat.

KEY MESSAGE: A new stem rust resistance gene Sr49 was mapped to chromosome 5BL of wheat. Usefulness of the closely linked markers sun209 and sun479 for marker-assisted selection of Sr49 was demonstrated. Landrace AUS28011 (Mahmoudi), collected from Ghardimaou, Tunisia, produced low stem rust response against Australian pathotypes of Puccinia graminis f. sp. tritici (Pgt) carrying virulence for several stem rust resistance genes deployed in modern wheat cultivars. Genetic analysis based on a Mahmoudi/Yitpi F3 population indicated the involvement of a single all-stage stem rust resistance gene and it was temporarily named SrM. Bulked segregant analysis using multiplex-ready SSR technology located SrM on the long arm of chromosome 5B. Since there is no other all-stage stem rust resistance gene located in chromosome 5BL, SrM was permanently designated Sr49. The Mahmoudi/Yitpi F3 population was enhanced to generate F6 recombinant inbred line (RIL) population for detailed mapping of Sr49 using publicly available genomic resources. Markers sun209 and sun479 flanked Sr49 at 1.5 and 0.9 cM distally and proximally, respectively. Markers sun209 and sun479 amplified PCR products different than the Sr49-linked alleles in 146 and 145 common wheat cultivars, respectively. Six and seven cultivars, respectively, carried the resistance-linked marker alleles sun209 148bp and sun479 200bp ; however, none of the cultivars carried both resistance-linked alleles. These results demonstrated the usefulness of these markers for marker-assisted selection of Sr49 in breeding programs.

Unveiling the aesthetic secrets: exploring connections between genetic makeup, chemical, and environmental factors for enhancing/improving the color and fragrance/aroma of Chimonanthus praecox.

Floral color and scent profiles vary across species, geographical locations, and developmental stages. The exclusive floral color and fragrance of Chimonanthus praecox is contributed by a range of endogenous chemicals that distinguish it from other flowers and present amazing ornamental value. This comprehensive review explores the intricate interplay of environmental factors, chemicals and genes shaping the flower color and fragrance of Chimonanthus praecox. Genetic and physiological factors control morpho-anatomical attributes as well as pigment synthesis, while environmental factors such as temperature, light intensity, and soil composition influence flower characteristics. Specific genes control pigment synthesis, and environmental factors such as temperature, light intensity, and soil composition influence flower characteristics. Physiological processes including plant hormone contribute to flower color and fragrance. Hormones, notably ethylene, exert a profound influence on varioustraits. Pigment investigations have spotlighted specific flavonoids, including kaempferol 3-O-rutinoside, quercetin, and rutin. Red tepals exhibit unique composition with cyanidin-3-O-rutinoside and cyanidin-3-O-glucoside being distinctive components. Elucidating the molecular basis of tepal color variation, particularly in red and yellow varieties, involves the identification of crucial regulatory genes. In conclusion, this review unravels the mysteries of Chimonanthus praecox, providing a holistic understanding of its flower color and fragrance for landscape applications. This comprehensive review uniquely explores the genetic intricacies, chemical and environmental influences that govern the mesmerizing flower color and fragrance of Chimonanthus praecox, providing valuable insights for its landscape applications. This review article is designed for a diverse audience, including plant geneticists, horticulturists, environmental scientists, urban planners, and students, offering understandings into the genetic intricacies, ecological significance, and practical applications of Chimonanthus praecox across various disciplines. Its appeal extends to professionals and enthusiasts interested in plant biology, conservation, and industries dependent on unique floral characteristics.

Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches.

Haemophilus parainfluenzae is a Gram-negative opportunist pathogen within the mucus of the nose and mouth without significant symptoms and has an ability to cause various infections ranging from ear, eye, and sinus to pneumonia. A concerning development is the increasing resistance of H. parainfluenzae to beta-lactam antibiotics, with the potential to cause dental infections or abscesses. The principal objective of this investigation is to utilize bioinformatics and immuno-informatic methodologies in the development of a candidate multi-epitope Vaccine. The investigation focuses on identifying potential epitopes for both B cells (B lymphocytes) and T cells (helper T lymphocytes and cytotoxic T lymphocytes) based on high non-toxic and non-allergenic characteristics. The selection process involves identifying human leukocyte antigen alleles demonstrating strong associations with recognized antigenic and overlapping epitopes. Notably, the chosen alleles aim to provide coverage for 90% of the global population. Multi-epitope constructs were designed by using suitable linker sequences. To enhance the immunological potential, an adjuvant sequence was incorporated using the EAAAK linker. The final vaccine construct, comprising 344 amino acids, was achieved after the addition of adjuvants and linkers. This multi-epitope Vaccine demonstrates notable antigenicity and possesses favorable physiochemical characteristics. The three-dimensional conformation underwent modeling and refinement, validated through in-silico methods. Additionally, a protein-protein molecular docking analysis was conducted to predict effective binding poses between the multi-epitope Vaccine and the Toll-like receptor 4 protein. The Molecular Dynamics (MD) investigation of the docked TLR4-vaccine complex demonstrated consistent stability over the simulation period, primarily attributed to electrostatic energy. The docked complex displayed minimal deformation and enhanced rigidity in the motion of residues during the dynamic simulation. Furthermore, codon translational optimization and computational cloning was performed to ensure the reliability and proper expression of the multi-Epitope Vaccine. It is crucial to emphasize that despite these computational validations, experimental research in the laboratory is imperative to demonstrate the immunogenicity and protective efficacy of the developed vaccine. This would involve practical assessments to ascertain the real-world effectiveness of the multi-epitope Vaccine.

Graph data science and machine learning for the detection of COVID-19 infection from symptoms.

Background: COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms. Methods: The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted. Results: We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy. Conclusion: The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

In vitro evaluation of herbal based Lesh Nat B cream against Leishmania tropica.

Pentavalent antimonials continue to be the standard treatment for cutaneous leishmaniasis. But their use is retarded owing to highly-priced, prolonged hospitalization, noxious and poor solubility. Therefore, there is a dire need to characterize new potential compounds possessing anti-leishmanial activity. Topical therapies that are more successful are an essential alternative therapeutic option for the localized self-limiting form of this disease. We tested the herbal-based topical cream Lesh Nat B against Leishmania tropica KWH23 promastigotes and axenic amastigotes in vitro. The anti-leishmanial activity of Lesh Nat B cream was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay against promastigotes and axenic amastigotes. The results of Lesh Nat B cream were concentration and incubation time-dependent. After 72 h of incubation, Lesh Nat B cream efficiently suppresses the promastigote form of the parasite, followed by 48 h and 24 h. At 72 h, the lowest and highest levels of activity were 37% and 90%. Amastigotes had a minimum activity of 34% and a maximum activity of 78.5%, respectively. This formulation was more cytotoxic against promastigote form than amastigotes form at 72 h incubation periods. All the experiments were carried out in triplicates. Half-maximal inhibitory concentration (IC50) values were determined to be (66 ug/ml) and (70 ug/ml) against promastigote and amastigote forms, respectively. Moreover, 1.63% hemolytic activity was observed in Lesh Nat B cream at (10 µg/ml) while 3% hemolytic activity was observed at (37 µg/ml). It can be concluded that Lesh Nat B cream demonstrated effective Leishmanicidal and less hemolytic activity and can be used as an alternative therapeutic option for the treatment of cutaneous leishmaniasis; however, more studies are expected to justify its effectiveness in treating cutaneous leishmaniasis in both humans and animals.

A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods.

In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh's total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2.8 million ha) of the cropland was affected out of the 4.9 million ha of agricultural area in Sindh. The analysis indicated expected production losses of 88% (3.1 million bales), 80% (1.8 million tons), and 61% (10.5 million tons) for cotton, rice, and sugarcane. This assessment provided useful tools to evaluate the L&D of agricultural production and to develop evidence-based policies enabling post-flood recovery, rehabilitation of people and restoration of livelihood.

Genome-wide identification and in-silico expression analysis of carotenoid cleavage oxygenases gene family in Oryza sativa (rice) in response to abiotic stress.

Rice constitutes a foundational cereal and plays a vital role in the culinary sector. However, the detriments of abiotic stress on rice quality and productivity are noteworthy. Carotenoid cleavage oxygenases (CCO) hold vital importance as they enable the particular breakdown of carotenoids and significantly contribute towards the growth and response to abiotic stress in rice. Due to the insufficient information regarding rice CCOs and their potential role in abiotic stress, their utilization in stress-resistant genetic breeding remains limited. The current research identified 16 CCO genes within the Oryza sativa japonica group. These OsCCO genes can be bifurcated into three categories based on their conserved sequences: NCEDs (9-Cis-epoxycarotenoid dioxygenases), CCDs (Carotenoid cleavage dioxygenases) and CCD-like (Carotenoid cleavage dioxygenases-like). Conserved motifs were found in the OsCCO gene sequence via MEME analysis and multiple sequence alignment. Stress-related cis-elements were detected in the promoter regions of OsCCOs genes, indicating their involvement in stress response. Additionally, the promoters of these genes had various components related to plant light, development, and hormone responsiveness, suggesting they may be responsive to plant hormones and involved in developmental processes. MicroRNAs play a pivotal role in the regulation of these 16 genes, underscoring their significance in rice gene regulation. Transcriptome data analysis suggests a tissue-specific expression pattern for rice CCOs. Only OsNCED6 and OsNCED10 significantly up-regulated during salt stress, as per RNA seq analyses. CCD7 and CCD8 levels were also higher in the CCD group during the inflorescence growth stage. This provides insight into the function of rice CCOs in abiotic stress response and identifies possible genes that could be beneficial for stress-resistant breeding.

Compost and humic acid amendments are a practicable solution to rehabilitate weak arid soil for higher winter field pea production.

Arid soils are often weak, low in fertility, and lack essential plant nutrients. Organic amendments might be a feasible solution to counter the detrimental impact and rehabilitate weak arid soil for the growth of legumes. The study aimed to investigate how organic amendments of compost and humic acid may affect winter field pea productivity in arid soil. Over 2 years of field experiments, a range of treatments were applied, including different amounts of compost and humic acid. The results showed higher microbial carbon (C), and nitrogen (N) biomass, root length, shoot length, grains pod-1, and grain yield of pea, gained from the collective application of 8 Mg ha-1 compost and 15 kg ha-1 humic acid compared to all other treatments. Organic amendments increased soil microbial C density by 67.0 to 83.0% and N biomass by 46.0 to 88.0% compared with the control. The combined application of compost and humic acid increased soil microbial N biomass by 57.0 to 60.0% compared to the sole applications of compost-only and humic acid-only. It was concluded that organic amendments of 8 Mg ha-1 compost and 15 kg ha-1 humic acid in arid soil modulated microbial density, resulting in improved winter field pea productivity. This study suggests organic amendments of compost and humic acid might be a practicable solution to rehabilitate weak arid soil to grow legumes.

Biogenic silicon nanoparticles mitigate cadmium (Cd) toxicity in rapeseed (Brassica napus L.) by modulating the cellular oxidative stress metabolism and reducing Cd translocation.

Nano-enabled strategies have emerged as promising alternatives to resolve heavy metals (HMs) related harms in an eco-friendly manner. Here, we explored the potential of biogenic silicon nanoparticles (SiNPs) in alleviating cadmium (Cd) stress in rapeseed (Brassica napus L.) plants by modulating cellular oxidative repair mechanisms. Biogenic SiNPs of spherical shapes with size ranging between 14 nm and 35 nm were synthesized using rice straw extract and characterized through advanced characterization techniques. A greenhouse experiment results showed that SiNPs treatment at 250 mg kg-1 significantly improved growth parameters, including fresh weight (33.3 %) and dry weight (32.6 %) of rapeseed plants than Cd-treated control group. Photosynthesis and leaf gas exchange parameters were also positively influenced by SiNPs treatment, indicating enhanced photosynthetic efficiency. Additionally, SiNPs treatment at 250 mg kg-1 increased the activities of antioxidant enzymes such as superoxide dismutase (19.1 %), peroxidase (33.4 %), catalase (14.4 %), and ascorbate peroxidase (33.8 %), which may play a crucial role in ROS scavenging and reduction in Cd-induced oxidative stress. TEM analysis revealed that SiNPs treatment effectively mitigated Cd-induced damage to leaf ultrastructure, while qPCR analysis showed that SiNPs treatment changed the expressions of the antioxidant defense and stress related genes. Moreover, SiNPs treatment significantly influenced the Cd accumulation and Si contents in plants. Overall, our findings revealed that biogenic SiNPs have great potential to serve as a sustainable, eco-friendly, and non-toxic alternative for the remediation of Cd toxicity in rapeseed plants.

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations.

The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

Effect of management on reproductive performances of the Achai cattle in the Hindu Kush (Northern Pakistan).

This study evaluates the effect of management on the undocumented Achai cattle reproductive performance in transhumant farming systems (TFS) and in sedentary farming systems (SFS) in northwestern Pakistan. Data were collected from 172 households in TFS and 270 households in SFS to analyze the effect of farming systems, parity, and calving season on key reproductive traits. The results show that farming systems significantly affect pubertal age, while parity has no significant effect on any of the key traits. The calving season significantly affects the postpartum anoestrus interval in TFS only. More than 50% of the cows in both systems have postpartum anoestrus intervals and calving intervals within the recommended values for cows in tropical countries. Achai cows have high first-service conception rates (70% and 71% for TFS and SFS, respectively) and require a relatively small number of services per conception (1.53 ± 0.06 and 1.48 ± 0.05 SE for TFS and SFS, respectively). This local breed thus warrants conservation under both farming systems.

Climate change and spatio-temporal trend analysis of climate extremes in the homogeneous climatic zones of Pakistan during 1962-2019.

Climate extremes, such as heat waves, droughts, extreme rainfall can lead to harvest failures, flooding and consequently threaten the food security worldwide. Improving our understanding about climate extremes can mitigate the worst impacts of climate change and extremes. The objective here is to investigate the changes in climate and climate extremes by considering two time slices (i.e., 1962-1990 and 1991-2019) in all climate zones of Pakistan by utilizing observed data from 54 meteorological stations. Different statistical methods and techniques were applied on observed station data to assess changes in temperature, precipitation and spatio-temporal trends of climatic extremes over Pakistan from 1962 to 2019. The Mann-Kendal test demonstrated increasing precipitation (DJF) and decreasing maximum and minimum temperatures (JJA) at the meteorological stations located in the Karakoram region during 1962-1990. The decadal analysis, on the other hand, showed a decrease in precipitation during 1991-2019 and an increase in temperature (maximum and minimum) during 2010-2019, which is consistent with the recently observed slight mass loss of glaciers related to the Karakoram Anomaly. These changes are highly significant at 5% level of significance at most of the stations. In case of temperature extremes, summer days (SU25) increased except in zone 4, TX10p (cold days) decreased across the country during 1962-1990, except for zones 1 and 2. TX90p (warm days) increased between 1991-2019, with the exception of zone 5, and decreased during 1962-1990, with the exception of zones 2 and 5. The spatio-temporal trend of consecutive dry days (CDD) indicated a rising tendency from 1991 to 2019, with the exception of zone 4, which showed a decreasing trend. PRCPTOT (annual total wet-day precipitation), R10 (number of heavy precipitation days), R20 (number of very heavy precipitation days), and R25mm (very heavy precipitation days) increased (decreased) considerably in the North Pakistan during 1962-1990 (1991-2019). The findings of this study can help to address some of the sustainable development goals related climate action, hunger and environment. In addition, the findings can help in developing sustainable adaptation and mitigation strategies against climate change and extremes. As the climate and extremes conditions are not the uniform in all climate zone, therefore, it is suggested to the formers and agriculture department to harvest crops resilient to the climatic condition of each zone. Temperature has increasing trend in the northern Pakistan, therefore, the concerned stakeholders need to make rational plans for higher river flow/flood situation due to snow and glacier melt.

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases.

Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal. Moreover, heterogeneous data could ensure the model's generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.

Phylogenetic analysis and antimicrobial susceptibility profile of uropathogens.

The uropathogens is the main cause of urinary tract infection (UTI). The aim of the study was to isolate bacteria from urine samples of UTI patients and find out the susceptibility of isolated bacteria. Bacteria were identified using both conventional and molecular methods. Sanger sequence procedure used for 16S ribosomal RNA and phylogenetic analysis was performed using Molecular Evolutionary Genetics Analysis (MEGA-7) software. In this study, Escherichia coli, Klebsiella pneumonia, Staphylococcus were reported as 58, 28 and 14.0% respectively. Phylogenetic tree revealed that 99% of sample No. Ai (05) is closely related to E. coli to (NR 114042.1 E. coli strain NBRC 102203). Aii (23) is 99% similar to K. pneumoniae to (NR 117686.1 K. pneumonia strain DSM 30104) and 90% Bi (48) is closely linked to S. aureus to (NR 113956.1 S. aureus strain NBRC 100910). The antibiotic susceptibility of E. coli recorded highest resistance towards ampicillin (90%) and least resistant to ofloxacin (14%). Some of the other antibiotics such amoxicillin, ciprofloxacin, gentamicin, ceftazidime, cefuroxime and nitrofurantoin resistance were observed 86, 62, 24, 55, 48 and 35% respectively. The cefuroxime showed the highest antibiotic resistance against K. pneumoniae with 85% followed by amoxicillin, ciprofloxacin, gentamicin, ceftazidime, ampicillin and nitrofurantoin resulted in 60, 45, 67, 70, 75 and 30% respectively. The resistance of S. aureus against erythromycin, cefuroxime and ampicillin were found with 72%. The resistance against amoxicillin, gentamicin, ceftazidime and ceftriaxone found 57, 43, 43 and 15% respectively. Phylogenetic analysis shows that sequences are closely related with the reference sequences and E. coli is the dominant bacteria among UTI patients and is resistant to the commercially available antibiotics.