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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Compensation for the variation of total number density to improve signal repeatability for laser-induced breakdown spectroscopy.

High signal uncertainty has been regarded as a critical obstacle for the quantitative analysis of laser-induced breakdown spectroscopy (LIBS). One of the most effective ways for uncertainty reduction is to directly compensate for the variation of plasma properties, especially total number density. However, reliable compensation for the variation of total number density is hard to implement. In this work, we propose a data pre-processing method, called total number density compensation (TNDC), to reduce signal uncertainty. It is established on an assumption extended from the internal standard method and utilizes a weighted sum of emission lines from all major elements to reflect the variation of total number density. The TNDC method is tested on 29 brass samples and outperforms common normalization methods based on the spectral area in terms of signal repeatability and analytical performance. For Cu, the mean pulse-to-pulse relative standard deviation (RSD) of signals is greatly decreased from 5.10% to 1.03%, which is almost the best signal repeatability that LIBS can achieve and is comparable to that of ICP-OES. The root mean square error of prediction (RMSEP) and the mean RSD of prediction are decreased from 6.56% to 0.60% and from 12.00% to 1.03%, respectively. While for Zn, the mean RSD of signals improves from 6.43% to 4.12%, and the RMSEP is reduced from 1.57% to 0.59% with the RSD of prediction from 5.41% to 4.18%. Results demonstrate that TNDC can be an effective method for LIBS analysis especially for repeatability improvement.

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.

Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra.

Laser-induced breakdown spectroscopy (LIBS) is a promising multi-elemental analysis technique and has the advantages of rapidness and minimal sample preparation. In traditional LIBS measurement, sample spectra are generally collected based on a single set of fixed experimental parameters, such as laser energy and delay time. When samples have the same main components and similar component concentrations, the difference in their spectral intensities becomes less obvious. This can lower the sensitivity of LIBS measurement and pose a threat to the accuracy and robustness of LIBS qualitative analysis. In this work, we propose a new method to increase the spectral difference between similar samples, namely multiple-setting spectra. For each sample, it adopts different sets of experimental parameters and obtains a group of spectra to increase the fingerprint spectral information. The effectiveness of the proposed method is theoretically verified and then tested on 11 similar coal samples. Specifically, the sample spectra were collected with different laser energy and delay time, and processed by principal component analysis (PCA) and Davies-Bouldin index (DBI). The results show that the use of multiple-settings spectra can significantly improve the sample discrimination accuracy from 81.8% to 96.4%. In addition, the proposed method can maintain the efficiency and cost of LIBS measurement.

Hydrological projections over the Upper Indus Basin at 1.5 °C and 2.0 °C temperature increase.

We analyse an ensemble of statistically downscaled Global Climate Models (GCMs) to investigate future water availability in the Upper Indus Basin (UIB) of Pakistan for the time horizons when the global and/or regional warming levels cross Paris Agreement (PA) targets. The GCMs data is obtained from the 5th Phase of Coupled Model Inter-Comparison Project under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Based on the five best performing GCMs, we note that global 1.5 °C and 2.0 °C warming thresholds are projected in 2026 and 2047 under RCP4.5 and 2022 and 3036 under RCP8.5 respectively while these thresholds are reached much earlier over Pakistan i.e. 2016 and 2030 under RCP4.5 and 2012 and 2025 under RCP8.5 respectively. Interestingly, the GCMs with the earliest emergence at the global scale are not necessarily the ones with the earliest emergence over Pakistan, highlighting spatial non-linearity in GCMs response. The emergence of 2.0 °C warming at global scale across 5 GCMs ranges from 2031 (CCSM4) to 2049 (NorESM) under RCP8.5. Precipitation generally exhibits a progressive increasing trend with stronger changes at higher warming or radiative forcing levels. Hydrological simulations representing the historical, 1.5 °C and 2.0 °C global and region warming time horizons indicate a robust but seasonally varying increase in the inflows. The highest inflows in the baseline and future are witnessed in July. However, the highest future increase in inflows is projected in October under RCP4.5 (37.99% and 65.11% at 1.5 °C and 2.0 °C) and in April under RCP8.5 (37% and 62.05% at 1.5 °C and 2.0 °C). These hydrological changes are driven by increases in the snow and glacial melt contribution, which are more pronounced at 2.0 °C warming level. These findings should help for effective water management in Pakistan over the coming decades.

Regression analysis of hydro-meteorological variables for climate change prediction: A case study of Chitral Basin, Hindukush region.

In the present study, hydro-meteorological variables of Chitral Basin in Hindukush region of Pakistan were studied to predict the changes in climatic components such as temperature, precipitation, humidity and river flow based on observed data from 1990 to 2019. Uncertainties in climate change projection were studied using various statistical methods, such as trend variability analysis via stationarity test and validation of regression assumptions prior to fitting of regression estimates. Also, multiple regression models were estimated for each hydro-meteorological variables for the given 30 years of observed data. Results demonstrated that temperature and, precipitation were inversely related with one another. It was observed from the regression model that temperature is decreases by 0.309 °C on the average increases in precipitation by one unit. Temperature also decreases for the increase in humidity by average 0.086 °C. Since, precipitation is negatively related with temperature, thus for increases in temperature the annual precipitation decreases by 0.278 mm annually. Humidity on the other hand, increases by 0.207% by increasing in precipitation and the temperature that causes humidity to decrease by 0.99%. Thus, it demonstrated that the flow in Chitral river increases due to precipitation by 0.306 m3/s for the change in precipitation by one unit. Findings from the present study negated the general perceptions that flow in the Chitral river has increased due to recession of glaciers with increase in the intensity of temperature.

Rock glacier inventory, permafrost probability distribution modeling and associated hazards in the Hunza River Basin, Western Karakoram, Pakistan.

The destabilization of rock glaciers and permafrost variations is of great importance to the safety of the population and infrastructure in the Karakoram region because of their effects on land stability and river obstructions. In this study, we compiled the first complete rock glacier inventory for the Hunza Basin, western Karakoram, of 616 rock glaciers with an area of 194 km2 between 2800 and 5700 m a.s.l. We categorized the rock glaciers as intact or relict, and their distributions and destabilization were further analyzed and used along with in situ climate and elevation dataset to model the permafrost probability distribution. The modeled areas where the permafrost zonation index (PZI) is 0.5-1.00 indicate that permafrost occurs over 85% of the catchment area and lies above 3525 m a.s.l., which closely matches the zero-degree isotherm of 3800 m a.s.l. Based on the sensitivity analysis of the independent variables, elevation is the most sensitive variable, followed by net radiation, for predicting the probabilities of the presence and absence of permafrost. The model distributions are quite precise, with median posterior areas under the curve of 0.98 and 0.96 for model training and testing, respectively. We analyzed the rock glacier destabilization for 68 rock glaciers that interacted with river channels, of which 50 blocked or diverted river channels. Destabilized rock glaciers can be closely linked to the 0 °C isotherm between 3400 and 4600 m a.s.l. The significant damage caused by periodic floods from the subsequent blockage of river channels by landslides can be attributed to variations in permafrost. Which demolished infrastructure, including a hydropower plant, suspension bridge and water supply system in Hassan-abad catchment. Quantification of rock glacier dynamics and permafrost in the region can further improve policies related to the reduction in disaster risk and mitigation of associated hazards.

Potential Application of CRISPR/Cas9 System to Engineer Abiotic Stress Tolerance in Plants.

Abiotic stresses in plants such as salinity, drought, heavy metal toxicity, heat, and nutrients limitations significantly reduce agricultural production worldwide. The genome editing techniques such as transcriptional activator-like effector nucleases (TALENs) and zinc finger nucleases (ZFNs) have been used for genome manipulations in plants. However, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technique has recently emerged as a promising tool for genome editing in plants to acquire desirable traits. The CRISPR/Cas9 system has a great potential to develop crop varieties with improved tolerance against abiotic stresses. This review is centered on the biology and potential application of the CRISPR/Cas9 system to improve abiotic stress tolerance in plants. Furthermore, this review highlighted the recent advancements of CRISPR/Cas9-mediated genome editing for sustainable agriculture.