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Glacier surges can create ice-dammed lakes when the advancing terminus blocks drainage. Such lakes are inherently unstable and can drain abruptly as glacial lake outburst floods (GLOFs), presenting a hazard to downstream populations and infrastructure in high mountain environments. We present satellite image analysis of the evolution of an ice-dammed lake formed by the 2018–20 surge of Shisper Glacier, western Karakoram. Our analysis identifies six phases of lake evolution. A large lake of up to 33.7 ± 9% million m3 formed in 2018–19, 2019–20, 2020–21 and 2021–22. In each case, the lake began to fill late in the year, reached a maximum size in May, and had completely drained between May and July, typically over 1–2 days. This analysis provides further evidence that GLOF hazards associated with lakes dammed by glacier surges can persist for several years after surge termination.

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.

Although glaciers in High Mountain Asia produce an enormous amount of water used by downstream populations, they remain poorly observed in the field. This study presents a geodetic mass balance of the glaciers in the Astore Basin (with differential GPS (dGPS) measurements on Harcho glacier) between 1999 and 2016. Changes near the terminus of Harcho glacier (below 3800 m a.s.l.) featured heterogeneous surface elevation changes, whereas the middle section shows the most negative changes. The surface elevation changes were positive above 4200 m a.s.l. The average annual mass balance was −0.08 ± 0.07 m w.e. a−1 derived from a dGPS and DEM comparison whereas Advanced Spaceborne Thermal Emission and Reflection Radiometer DEM-based results show a slightly positive, that is 0.03 ± 0.24 m w.e. a−1 in the same period. In contrast, the terminus indicates a substantial retreat of ~368 m (4.5 m a−1) between 1934 and 2016. The average mass balance of 19 glaciers (>2 km2) covering ~60% of the total glaciers in the Basin exhibit no net mass loss in the period of 2000−2016 and follow a pattern similar to adjacent Karakoram glaciers.

Low-resource languages are gaining much-needed attention with the advent of deep learning models and pre-trained word embedding. Though spoken by more than 230 million people worldwide, Urdu is one such low-resource language that has recently gained popularity online and is attracting a lot of attention and support from the research community. One challenge faced by such resource-constrained languages is the scarcity of publicly available large-scale datasets for conducting any meaningful study. In this paper, we address this challenge by collecting the first-ever large-scale Urdu Tweet Dataset for sentiment analysis and emotion recognition. The dataset consists of a staggering number of 1, 140, 821 tweets in the Urdu language. Obviously, manual labeling of such a large number of tweets would have been tedious, error-prone, and humanly impossible; therefore, the paper also proposes a weakly supervised approach to label tweets automatically. Emoticons used within the tweets, in addition to SentiWordNet, are utilized to propose a weakly supervised labeling approach to categorize extracted tweets into positive, negative, and neutral categories. Baseline deep learning models are implemented to compute the accuracy of three labeling approaches, i.e., VADER, TextBlob, and our proposed weakly supervised approach. Unlike the weakly supervised labeling approach, the VADER and TextBlob put most tweets as neutral and show a high correlation between the two. This is largely attributed to the fact that these models do not consider emoticons for assigning polarity.

Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

Snow is a dominant water resource in high-mountain Asia (HMA) and crucial for mountain communities and downstream populations. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote-sensing snow products, mainly underestimation caused by cloud cover and overestimation associated with sensors' limitations, hamper the understanding of snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for high-mountain Asia between 2002 and 2019. An improved MODIS 8 d composite MOYDGL06* product was used as a training data for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8 d composite snow extent of the MOYDGL06* product. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory version 6.0 (RGI 6.0) described as M*D10A1GL06 to make a more complete cryosphere product with 500 m spatial resolution. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a weight of 0.5 and 1 to snow pixels in either or both Terra and Aqua products, respectively, for deriving snow cover statistics from our final snow product. The values 200, 242, and 252 indicate snow pixels in both Terra and Aqua and have a weight of 1, whereas pixels with snow in one of the Terra or Aqua products have a weight of 0.5. On average, the M*D10A1GL06 product reduces 39.1 % of uncertainty compared to the MOYDGL06* product. The uncertainties due to cloud cover (underestimation) and sensor limitations, mainly larger solar zenith angle (SZA) (overestimation) reduced in this product, are approximately 32.9 % and 6.2 %, respectively. The data in this paper are mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.org/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).

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.

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.

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 Os CCO 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.

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.