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Climate change is one of the main consequences of anthropogenic activities. Since the 1950s, gradual changes and an increase in climate warming have been observed. Previous research has been indicating potential associations between climate warming and spatiotemporal changes in precipitation. Moreover, the regional patterns of precipitation have a key role in the continuous monitoring of climate characteristics and natural hazards such as floods and droughts. Therefore, precise and accurate measurements of precipitation concentration and spatiotemporal variability in their patterns are very crucial. In this study, a new method for measuring precipitation concentration is developed and applied to 54 meteorological stations in Pakistan. Furthermore, to assess the precipitation patterns, the proposed method provides solid evidence for considering the effect of temperatures under climate warming. Furthermore, using the spatial correlation between the proposed method and its competitor, a comparative analysis is made to evaluate the performance of the proposed method. Moreover, the spatial variability structure in various precipitation patterns is assessed and compared using spatial predictive maps. Outcomes associated with this research show significant deviations between the proposed method and the existing one. In this paper, regression analysis revealed that the additional input can potentially improve the precipitation estimates under the appropriate sampling estimator. This is the first study that has documented the impact of climate warming on measuring precipitation concentration. These findings can contribute to a better understanding of precipitation concentration in relation to climate warming.

Corona Virus (COVID)- 19 is a pathogenic viral infection that presents as a new worldwide public health crisis. This review article aims at recapitulating waves of COVID-19, vaccination programs, treatment options, and the current scenario of COVID-19 in Pakistan. All available literature on PubMed, Scopus databases and science direct and Google scholar relating to COVID-19 published between 2019 to January 2022 was reviewed. The first wave of COVID-19 presented as severe acute respiratory syndrome. The second wave had a modest intensity, affecting only Sindh's southern province, and peaked in mid-December 2020. The third wave, which primarily disturbed the regions of Khyber Pakhtunkhwa and Punjab, peaked in the late April 2021, when a new strain of SARS-CoV-2 was discovered in over 64 countries, including Pakistan, as of January 27, 2021. Fourth wave qualifying measures was forced in May, after which cases balanced out and most of the mortality was reported in Punjab, followed by Sindh. The 5th wave with a positive ratio in Karachi had the increasing trend from 2% to 6%. In clinical trials, treatment with a combination of Azithromycin and Chloroquine shown efficacy against COVID-19. The Tocilizumab was used to treat COVID-19–related symptoms. Vaccination appeared to be vital to control the COVID-19 disease outbreak with seven licensed vaccines. Currently 72% Pakistani population is either vaccinated or have been infected at least once.

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Aim: The world is affected by the severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) pandemic. This virus has emerged as a human pathogen that can cause symptoms ranging from fever to Pneumonia, but it remains asymptomatic or mild. To better understand the virus's ongoing spread, identify those who have been infected, and track the immune response, accurate and robust immunological monitoring and SARS-CoV-2 detection assays are needed. Methods: The estimation of serology tests to assess the presence of antibodies to SARS-CoV-2 in COVID- 19 patients at Asian Institute of Medical Sciences (AIMS) and Isra University & hospital. 1229 patients were selected including males and females with the age being 25 to 65 years living in the territories from 1st August to 30th November 2020. The anti-SARS-CoV-2 test was performed by an electrochemiluminescence immunoassay analyzer. Results: Out of 1229 participants 206 (17%) were positive with COVID-19, and 1023 (83%) were negative. The results further revealed that a higher percentage of positive COVID-19 were detected in males in all age groups as compared to females, and most of them are affected at age of 46-65 years male 40 (24.69%) and female 14(17.5%). Conclusion: The seroprevalence of SARS-COV-2 antibodies has increased in the old age population, which may aid in determining the true number of infected cases. Although the current study is based on a small sample of participants, the findings suggest a study with a larger population to implement stronger and targeted interventions.

The aim of the present review is to provide basic knowledge about the role of tumour necrosis factor-α 1 and tumor necrosis factor-α receptor 2 in neuro-inflammation diseases. We performed an open-ended, English restricted search of PubMed, Embase, PsychINFO, Web of Science, Scopus, and the Cochrane Library for available literature from 24Feb. 2018–12 May 2021, using terms related to neuroinflammation, tumour necrosis factor-α, tumour necrosis factor II (TNFR-II), TNF-α and related diseases, TNFR-II and inflammation-related diseases, their relationships, and polymorphism. The main outcomes assessed were the presence of plaques and tangles, behaviour and cognition, reduction in brain tissue mass, and synaptic function the majority of studies were documented a beneficial effect in other areas, including the presence of plaques and tangles and synaptic function. The human studies were showed that TNF-αI was beneficial to Alzheimer's disease patients, with one being a small pilot study and the latter being an observational study, with a high risk of bias. It is concluded that the functions and mechanisms of TNF-α and TNFR-II in inflammation-related diseases will provide new viewpoints and theories in the development and treatment of these diseases. They play important roles in the pathogenesis of diseases induced by or related to inflammatory cytokines and signaling pathways.

Continuous and accurate drought monitoring has an important role in early warning drought mitigation policies. This study aims to provide an accurate standardized drought monitoring indicator by enhancing the representative characteristics of precipitation data using advanced statistical methods. We proposed a two-phase statistical procedure index – the Regional Multi-Component Gaussian Hydrological Drought Assessment (RMcGHDA) – for accurate drought monitoring under a multi-auxiliary variable-based sampling estimator and K-Component Gaussian Mixture Distribution (CGMD) model. The first phase of our proposed method increases the regional representativeness of the data under Spatio-temporal settings and the second phase describes the use of the Twelve-Component Gaussian Mixture Distribution (CGMD) model in the standardization stage of SDIs. We applied the proposed framework to 52 meteorological stations in Pakistan and compared the RMcGHDA performance with existing methods using Pearson correlation (r) and spatial patterns of various drought categories. We found significant differences between RMcGHDA and existing methods (i.e., Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)) for drought assessment. By the rationale of the data improvement under-sampling estimator and the use of multi-component Gaussian function, these differences indicate that RMcGHDA provides a practical and accurate way for drought assessment.

The optimization of the supercritical fluid extraction (SFE) of cannabinoids, using supercritical carbon dioxide (scCO2), was investigated in a fractional factorial design study. It is hypothesized that four main parameters (temperature, pressure, dry flower weight, and extraction time) play an important role. Therefore, these parameters were screened at predetermined low, medium, and high relative levels. The density of scCO2 was used as a factor for the extraction of cannabinoids by changing the pressure and temperature. The robustness of the mathematical model was also evaluated by regression analysis. The quantification of major (cannabidiol (CBD), cannabidiolic acid (CBDA), delta 9-tetrahydrocannabinol (Δ9-THC), delta 8-tetrahydrocannabinol (Δ8-THC), and delta 9-tetrahydrocannabinol acid (THCA-A)) and minor (cannabidivann (CBDV), tetrahydrocannabivann (THCV), cannabigerolic acid (CBG), cannabigerol (CBGA), cannabinol (CBN), and cannabichomere (CBC)) cannabinoids in the scCO2 extract was performed by RP-HPLC analysis. From the model response, it was identified that long extraction time is a significant parameter to obtain a high yield of cannabinoids in the scCO2 extract. Higher relative concentrations of CBD(A) (0.78 and 2.41% w/w, respectively) and THC(A) (0.084 and 0.048% w/w, respectively) were found when extraction was performed at high relative pressures and temperatures (250 bar and 45 °C). The higher yield of CBD(A) compared to THC(A) can be attributed to the extract being a CBD-dominant cannabis strain. The study revealed that conventional organic solvent extraction, e.g., ethanol gives a marginally higher yield of cannabinoids from the extract compared to scCO2 extraction. However, scCO2 extraction generates a cleaner (chlorophyll-free) and organic solvent-free extract, which requires less downstream processing, such as purification from waxes and chlorophyll.

In this work, we provide a new generated class of models, namely, the extended generalized inverted Kumaraswamy generated (EGIKw-G) family of distributions. Several structural properties (survival function sf , hazard rate function hrf , reverse hazard rate function rhrf , quantile function qf and median, s th raw moment, generating function, mean deviation md , etc.) are provided. The estimates for parameters of new G class are derived via maximum likelihood estimation MLE method. The special models of the proposed class are discussed, and particular attention is given to one special model, the extended generalized inverted Kumaraswamy Burr XII (EGIKw-Burr XII) model. Estimators are evaluated via a Monte Carlo simulation MCS . The superiority of EGIKw-Burr XII model is proved using a lifetime data applications.

Drought hazard is one of the main consequences of global warming and climate change. Unlike other natural disasters, drought has complex climatic features. Therefore, accurate drought monitoring is a challenging task. This paper proposes a framework for assessing drought classifications at the regional level. The proposed framework provides a new drought monitoring indicator called Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI). MSARSPEI is an amalgam of the Standardized Precipitation Evapotranspiration (SPEI) (Vicente-Serrano et al., 2010) and Regionally Improved Weighted Standardized Drought Index (RIWSDI) (Jiang et al., 2020). In the proposed framework, the Boruta algorithm of feature selection is configured to ensemble monthly time series data of evaporation in various meteorological stations located in specific regions. Further, the framework suggests the standardization of the Cumulative Distribution Function (CDF) of K-Component Gaussian (K-CG) mixture distribution function for obtaining MSARSPEI data. The application of the proposed framework is based on seven different regions of Pakistan. For comparative analysis, this paper compared the performance of MSARSPE with SPEI using Pearson correlation. Outcomes associated with this research show that the proposed regional drought index has a strong correlation with the competing indicator in various time scales. In addition, the study assessed the spatial extent of various drought classifications under MSARSPEI. In summation, this research concludes that the choice of the MSARSPEI is rationally valid and more appropriate for the regional assessment of drought under the global warming scenario.

Pea protein is a very popular source of edible plant-based protein among legumes. In this study, the stability of ultra high temperature (UHT) processed pea protein emulsion prepared from 0.5 and 1.0% (w/v) pea protein concentrate (PPC) by two different homogenisation methods of microfluidisation (500 Bar) and ultrasonication (ultrasonicated for 1, 3 and 5 min) was investigated. In addition, the emulsion properties (particle and droplet size, flocculation, coalescence, zeta potential, hydrophobicity and creaming index) of PPC emulsions before and after UHT treatment were measured. The overall heat transfer coefficient (OHTC) versus time graphs were stable during UHT processing for both microfluidised and ultrasonicated PPC emulsions that indicates no fouling and good stability under the thermal treatment condition. Freshly prepared emulsion using 0.5 and 1.0% PPC and ultrasonicated for 5 min showed creaming index of 5.73 and 8.39%, particle size of 0.96 and 1.53 μm respectively. In addition, the fat droplet size for the above samples measured 1.05 and 1.85 μm for larger fat droplets and 0.51 and 0.72 μm for smaller fat droplets, respectively. However, after UHT treatment this emulsion destabilised due to protein aggregation as indicated by the high flocculation index (13.22 and 103.35%), particle size (1.59 and 3.23 μm) and droplet size (1.30 and 2.53 μm, for large fat droplets and 0.90 and 1.22 μm, for small fat droplets). After UHT treatment the microfluidised PPC emulsion using 0.5 and 1.0% PPC were the most stable with small particle size (2.85 and 0.36 μm), high zeta potential (−56.36 and − 27.30) and low creaming index (3.87% and 4.97%), respectively as compared to ultrasonicated samples. Overall, this study revealed that UHT treatment improved emulsion properties of the microfluidised PPC emulsion compared to the ultrasonicated PPC emulsion.

In this study, the optimal setup of supercritical fluid extraction (SFE) was designed and developed, leading to the quantitation of 11 distinct cannabinoids (cannabidivann (CBDV), tetrahydrocannabivann (THCV), cannabidiol (CBD), cannabigerol (CBG) cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabinol (CBN), delta 9-tetrahydrocannabinol (Δ9-THC), delta 8-tetrahydrocannabinol (Δ8-THC), cannabichomere (CBC) and delta 9-tetrahydrocannabinol acid (THCA-A)) extracted from the flowers of medicinal cannabis (sp. Sativa). Supercritical carbon dioxide (scCO2) extraction was performed at 37 °C, a pressure of 250 bar with the maximum theoretical density of CO2 (893.7 kg/m3), which generated the highest yield of cannabinoids from the flower-derived extract. Additionally, a cold separator (separating chamber) was used and positioned immediately after the sample containing chamber to maximize the yield. It was also found that successive washing of the extract with fresh scCO2 further increased yields. Ultra-high performance liquid chromatography coupled with DAD (uHPLC-DAD) was used to develop a method for the quantification of 11 cannabinoids. The C18 stationary phase was used in conjunction with a two solvent system gradient program resulting in the acquisition of the well-resolved chromatogram over a timespan of 32 min. The accuracy and precision of isolated cannabinoids across inter-and intra-day periods were within acceptable limits (&lt, ±15%). The assay was also fully validated and deemed sensitive from linearity, LOQ, and LOD perspective. The findings of this body of work are expected to facilitate improved conditions for the optimal extraction of select cannabinoids using scCO2, which holds promise in the development of well-characterized medicinal cannabis formulations. As to our best knowledge, this is the first study to report the uHPLC quantification method for the analysis of 11 cannabinoids from scCO2 extract in a single run with more than 1 min peak separation.

Data mining has a significant role in hyrdrologic research. Among several methods of data mining, Bayesian network theory has great importance and wide applications as well. The drought indices are very useful tools for drought monitoring and forecasting. However, the multi-scaling nature of standardized type drought indices creates several problems in data analysis and reanalysis at regional level. This paper presents a novel framework of data mining for hydrological research-the Bayesian Integrated Regional Drought Time Scale (BIRDts). The mechanism of BIRDts gives effective and sufficient time scales by considering dependency/interdependency probabilities from Bayesian network algorithm. The resultant time scales are proposed for further investigation and research related to the hydrological process. Application of the proposed method consists of 46 meteorological stations of Pakistan. In this research, we have employed Standardized Precipitation Temperature Index (SPTI) drought index for 1-, 3-, 6-, 9-, 12-, 24-, and ()month time scales. Outcomes associated with this research show that the proposed method has rationale to aggregate time scales at regional level by configuring marginal posterior probability as weights in the selection process of effective drought time scales.

A pneumonia outbreak caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was reported in December 2019 from Wuhan, China, and named by the World Health Organization (WHO) as COVID-19. SARS-CoV-2 is closely related to the bat coronavirus RaTG13. A mutation in the spike S protein is under the scrutiny for its possible role in an increased degree of contagiousness over earlier coronaviruses. Pathogenicity of SARS-CoV-2 is being understood with every growing number of COVID-19 patients and accumulated clinical data. The scientific and medical world has accelerated the pace of research in search of effective vaccines; as of now, there are 321 vaccine candidates, 32 of which are under trials. Besides a worldwide medical and health emergency, COVID-19 has hit the economies and societies very hard. Hence, there is an urgent need for developing innovative approaches for proper utilization of technologies to control infections, reduce pathogenicity, treat the patients, and develop post-COVID-19 care.

The pharmaceutical importance of cannabis is growing due to the natural non-psychoactive and psychoactive cannabinoids. For medicinal and forensic purposes, the effective extraction and quantification are essential to fully utilise the natural cannabinoids. The supercritical fluid extraction (SFE) process has gained increasing interest due to its selective extraction, short processing time (partly due to the efficient solvent removal process - supercritical fluid to vapour - leaving a solvent free product), low running cost, and low impact on the environment, compared to that of most conventional extraction methods. In this review, the extraction of cannabinoids through SFE methods have been summarised. The advantages of SFE of cannabinoids over conventional extraction procedures; such as microwave-assisted extraction, solid phase microextraction, hard-cap espresso, soxhlet extraction, high-throughput homogenization, ultrasound-assisted extraction, vacuum distillation of lipid-based extract, and liquid-liquid extraction are discussed. Furthermore, this review examines the importance of the SFE of cannabinoids by coupling with various conventional extraction methods, separation techniques, selection of a suitable co-solvent/modifier, and appropriate sample preparation. Additionally, the applications of using SFE technology and cannabinoids are reviewed with a focus on industrial, pharmaceutical, waste by-products, and purification.

Drought has a complex climatic and spatio-temporal feature. Therefore, its accurate monitoring is a great challenge for hydrological research. Recently, the use of standardized drought indices (SDIs) for drought monitoring is common in practice. However, because of the subjective choices of probability distribution, the uncertainty related to extreme events always exists in SDIs-based drought-monitoring tools. The present research extends the generalized non-parametric framework for drought monitoring. The application of the proposed framework is based on seven meteorological stations in Pakistan. The preliminary analysis considered the standardized precipitation temperature index (SPTI) at different time scales. The significance of the proposed framework is to address extreme values with more accuracy under a non-parametric framework. It is concluded that the suitable choice of probability-plotting-position formulas allows greater accuracy when capturing the probability of extreme drought events.

Levels of arsenic (As), chromium (Cr), and copper (Cu) are increasing in the soils worldwide. Such contaminants cause toxicity in the plant systems which adversely affects growth and productivity. The objective of the present investigation was to elucidate individual and combined effects of As, Cr, and Cu (100 μM each) stress in metal hyper-accumulator plant Indian mustard (Brassica juncea L.), exposed for a week. The highest accumulation was in the roots and in decreasing order viz. Cu > As > Cr. The magnitude of oxidative stress was maximal in combined stress, followed by As, Cr, and then Cu stress. Glutathione in conjunction with glutathione reductase, glutathione peroxidase, and glutathione S-transferase increased in all set of stress treatments, notably when exposed to Cr alone. In addition, the level of sulfur-rich compounds like cysteine, phytochelatins, and non-protein thiols increased under each stress indicating efficient coupling of the enzyme system and sulfur-containing compounds during stress conditions. The highest tolerance or growth index of plants was recorded for Cu. Protein profiling of leaf tissues showed modulation of protein patterns in each stress. Mediator of RNA polymerase II transcription subunit 1 isoform X1, RuBisCO (large subunit), and ribosomal protein S3 proteins were more abundant under Cr and Cu stress. Zinc finger A20/AN1 domain-containing stress-associated protein 5–like protein was more abundant under Cu stress. HSP (15.7 kDa) and autophagy protein 5–like were in higher abundance under As and combined stress. Our results suggest that Indian mustard has a differential mode of defense against a particular stressor at the level of protein expression profile.

Since essential micronutrients are not produced within humans, it has to be taken via the food. Genetic engineering provides one such solution to enhance the quality of our food. Since rice is one of the primary staple food across the world, a new variety of rice called Golden Rice is developed as a result of genetic engineering to produce beta-carotene (provitamin-A), which is a source of vitamin-A. The endosperm of rice lacks the provitamin-A, and hence among the rice consuming population, it causes vitamin A deficiency. Therefore to overcome this threat, biofortification method has been used which involves deliberately enriching the endosperm of rice with provitamin-A carotenoids. Golden Rice was developed to distribute it to the farmers of the poor and underprivileged of the developing countries without any restrictions or any charge. It targets a vast population of women and children with or at the risk of vitamin A deficiency in many developing countries of Asia and Africa. The endosperm tissue of the immature rice was examined by a variety of biochemical assays, and it was observed that geranyl, geranyl-pyrophosphate (GGPP) is present and it is the initial precursor that is converted into the alpha- and beta-carotene. However, the GGPP is not further metabolized, and there is transcriptional inactivity of specific genes involved in the production of isoprenoid compounds. Hence transgenic approach was used for the synthesis of beta-carotene through manipulating Psy (Phytoene synthase) and crtI (carotene desaturase) genes. As per the food regulations of genetically modified crops; genetic engineering of food is considered way better than the conventional crop breeding technologies. It provides for modifications subjected to the genetic make-up of the crops thus aiming to enhance the nutrition, production, medicinal traits and resistance (to diseases) of the crops.

Drought is one of the most pronounced threats that impacts plant yield and quality. Approaches to reduce the negative effects of drought are under scrutiny. Nanotechnology scouts and opens large scope for multiple applications in biotechnology and agricultural field. Potential utility of nanotechnology could be capitalized in agricultural production. Nanoparticles can be synthesized from metal or metal oxides by diverse methods. Nanoparticles due to their extreme small size have queer properties which opens new approaches in agricultural sector. However, there is still a long way to develop the technology to establish endurable agriculture. The present chapter reviews the recent advances in nanobiotechnology research related to drought stress mitigation and modulation of crop yield.

Heavy metal–contaminated soils pose a major environmental challenge. Soils are contaminated with heavy metals due to industrial, agricultural and human activities. Heavy metal contamination can cause dysfunction in biodiversity by reducing soil fertility and microbial activity. Heavy metals are persistent in nature and cannot be removed by biological processes, but can be transformed from one chemical form to another. Toxicity of metals can cause physiological changes, such as decrease in water potential, increased production of ROS, enzyme inhibition, oxidation of nucleic acids and proteins, cell death and developmental changes such as growth inhibition. Exposure to toxic levels of heavy metals can trigger stress response and adaptation at physiological, developmental, structural and molecular levels. Agricultural soils affected with heavy metal toxicity need special consideration because heavy metals have the potential to accumulate in crop plants, which can affect all trophic levels. There is a critical need for reliable bioassays for risk assessment of heavy metal toxicity in plant systems. In this chapter, transport of heavy metals, their effect on plant metabolism and sequestration have been discussed.

Banana is considered as a potential source of valuable nutraceutical bioactive compounds. In addition, It is not only considered as a fruit crop but also serve as high energy containing food in various parts of the world. The aim of this review is to evaluate the historical record, uses, biological activities, chemical composition and compositional changes during ripening behavior in both edible and non-edible part of banana. Recent studies have shown that banana as raw materials riches in valuable bioactive compounds including as vitamins, phytosterols, biogenic amines, phenolics, carotenoids, volatile compounds, minerals, starch and carbohydrates which are highly required in the diet as they play important role in the maintenance of human health and well-being. This paper also covers the potential biological activities of banana such as antidiarrheal, antiulcerative, antimicrobial, antioxidant, hypoglycemic, wound healing, antilithiatic and anticancer activity. It also provide an outline of chemical constituents of banana named vitamins, polyphenols, steroids, triterpenes and amines, carotenoids, starch and carbohydrates, volatile compounds and mineral contents. Furthermore, with the development of fruit these beneficial bioactive compounds vary rapidly because of adopting atmospheric and standardized post and pre-harvested conditions. The mainstream sectors responsible for these changes are highlighted.

In this study, supercritical carbon dioxide (scCO2) extractions of cannabinoids were conducted at four different densities (231, 590, 818, and 911 kg/m3) using ethanol (5% w/v) as a co-solvent. The chemical profiles of these cannabinoids were analysed via reverse-phase high-performance liquid chromatography (RP-HPLC). It was determined that scCO2, at low density (231 kg/m3), produced an extract yield of 6.1% w/v. At high scCO2 density (~818 kg/m3), the yield was 16.1% w/v. More specifically, the amounts of tetrahydrocannabinol (THC) and cannabidiol (CBD) in the scCO2 extract at 818 kg/m3 were 10.8 and 15.6% w/v, respectively. It was also found that the use of 5% w/v ethanol increased scCO2 extract yields at both low and high densities (7.6% w/v and 18.2% w/v, respectively). Additionally, the use of co-solvent increased this yield further under both low- and high-density conditions, to 13.7 and 19.1% w/v, respectively. Interestingly, higher scCO2 density (911 kg/m3) with and without ethanol did not improve the scCO2 extract yield or the amount of cannabinoids. Although this study provides new insights into the correlation between scCO2 density and ethanol co-extraction of CBD and THC, more studies are needed to determine how different scCO2 densities and co-solvents influence the extraction of cannabinoids.

Drought is a complex natural hazard. It occurs due to a prolonged period of deficient in rainfall amount in a certain region. Unlike other natural hazards, drought hazard has a recurrent occurrence. Therefore, comprehensive drought monitoring is essential for regional climate control and water management authorities. In this paper, we have proposed a new drought indicator: the Seasonally Combinative Regional Drought Indicator (SCRDI). The SCRDI integrates Bayesian networking theory with Standardized Precipitation Temperature Index (SPTI) at varying gauge stations in various month/seasons. Application of SCRDI is based on five gauging stations of Northern Area of Pakistan. We have found that the proposed indicator accounts the effect of climate variation within a specified territory, accurately characterizes drought by capturing seasonal dependencies in geospatial variation scenario, and reduces the large/complex data for future drought monitoring. In summary, the proposed indicator can be used for comprehensive characterization and assessment of drought at a certain region.