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

Vegetation plays a crucial role in terrestrial ecosystems, and there has been a substantial shift in global vegetation cover in recent decades. China is recognized for its substantial impact on global vegetation cover changes, which are influenced by both climate change and human activities. Therefore, this research aims to assess the respective influences of climate modification and human activities on vegetation variations in China. First, the changes in vegetation cover are explored between 1982 and 2020 using satellite-image derived vegetation index, known as the Normalized Difference Vegetation Index (NDVI). Second, a multiple regression model based on time-lag analysis is used to simulate NDVI. In addition to common climatic factors such as temperature, precipitation, solar radiation intensity and relative humidity, the atmospheric CO2 concentration to directly reflect climate change is considered in this model. Finally, the relative influence of climate variation and human activities on the alteration of vegetation cover is determined based on reconstructed NDVI. Results: (1) Precipitation and solar radiation are the most important influences, while carbon dioxide concentration and relative humidity have the least influence. (2) The simulation error before 2000 was 0.875%, which was considerably lower than the error after 2000. (3) After 2000, human activities favorably affected vegetation recovery in most of the study area, with an average degree of influence of >30%. 

Impacts of climate factors and human activities on NDVI change in China

Crop yield is a major agriculture sector affected by climate change; especially agrometeorological droughts experienced by south Asian countries in past decades. The main goals of this research were to explore the spatiotemporal characteristics of seven agrometeorological drought indices at a regional scale in Pakistan. Secondly, to forecast the wheat yield loss risk (YLR) due to droughts under current and future climate scenarios by employing three machine learning (ML) methods; random forest (RF), gradient boosting machine (GBM), and generalized additive model (GAM). The relationship between detrended wheat yield and a combination of five remote sensing indices Normalized Difference Water Index (NDWI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and Drought Severity Index (DSI)), and two meteorological drought indices (Palmer’s Drought Severity Index (PDSI), and Standardized Precipitation Evapotranspiration Index (SPEI)) was analyzed. Mann-Kendall trend (MK), Sens’s slope, and Sequential Mann-Kendall (SQMK) tests are applied to explore the trend and trend-changing years for all indices over the historical time of 20 years. The YLR and all indices were projected (2021–2050) from the baseline period (2001–2020) using PROPHET time series forecasting and CMIP6 climatic models. YLR was forecasted on present and future projected time series by employing three non-linear ML regression models. The output of the drought analysis revealed that the study area was hit by high to severe drought events in 2001–2004, 2006, 2008, 2010, 2012, and 2017. Trend analysis revealed intersection years breaking the rising trend of drought indices. All drought indices are significantly correlated with meteorological wheat yield with a sequence of NDWI>DSI>VCI>VHI>PDSI>SPEI>TCI. Future projections under high emission scenarios revealed a rise in YLR associated with frequent projected droughts from VHI, DSI, SPEI, and PDSI. YLR forecasting from agrometeorological indices is best predicted by random forest with the lowest RMSE = 0.005314. NDWI (26%) and VCI (19%) are found to be significant relative predictors associated with 51% high YLR in the baseline period and SPEI (20%) and NDWI (17%) as the most important relative predictors associated with 39% high YLR in future. The region is vulnerable to agrometeorological droughts with more susceptibility to less rain and high temperature affecting crop health and a high risk of yield loss in the future. The study provides a direction to stakeholders and policymakers to develop and adapt better strategies to mitigate and prevent drought-related yield loss risk in the future. 

Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan

Due to the complexity of meteorological and hydrological conditions in a changing environment, previous drought indices for monitoring a specific drought type do not reflect the overall regional situation of water scarcity. Therefore, in order to obtain accurate and reliable drought monitoring, a more integrated drought index should be developed to identify drought events comprehensively. In this paper, a non-linear trivariate drought index (NTDI) was constructed based on the joint probability distribution of parametric copulas, combining precipitation (P), potential evapotranspiration (PET), and root zone soil moisture (SM) variables. Subsequently, it was respectively compared with four drought indices, SPEI, SSMI, SC-PDSI and TVDI, and cross-validated with actual recorded drought events and annual crop yield to evaluate its applicability in arid Central Asia (ACA). The results indicated that: (1) Frank copula (1-,3-month scale) and Gumbel copula (6-,12-month scale) were considered to be the best-fitted copula functions for constructing joint probability distributions in the ACA. (2) The NTDI integrated the P-PET and SM drought signals to sensitively capture drought onset and duration, reflecting the combined characteristics of meteorological and agricultural drought. (3) The drought information expressed by NTDI was generally consistent with recorded drought events, and the monitoring results are accurate. (4)The NTDI performed better in agricultural drought monitoring than other drought indices. This study provides a reliable multivariate composite indicator which is significant for drought monitoring, prevention and risk assessment in ACA. 

A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia

Launaea fragilis (Asso) Pau (Family: Asteraceae) is a wild medicinal plant that has been used in the folklore as a potential treatment for numerous ailments such as skin diseases, diarrhea, infected wounds, inflammation, child fever and hepatic pain. This study explored the chemical constitution, in-vivo toxicity, antimicrobial, antioxidant, and enzyme inhibition potential of ethanolic extract of L. fragilis (EELF). Additionally, in-silico docking studies of predominant compounds were performed against in-vitro tested enzymes. Similarly, in-silico ADMET properties of the compounds were performed to determine their pharmacokinetics, physicochemical properties, and toxicity profiles. The EELF was found rich in TFC (73.45 ± 0.25 mg QE/g) and TPC (109.02 ± 0.23 mg GAE/g). GC–MS profiling of EELF indicated the presence of a total of 47 compounds mainly fatty acids and essential oil. EELF showed no toxicity or growth retardation in chicks up to 300 mg/kg with no effect on the biochemistry and hematology of the chicks. EELF gave promising antioxidant activity through the CUPRAC method with an IC50 value of 13.14 ± 0.18 µg/ml. The highest inhibition activity against tyrosinase followed by acetylcholinesterase and α-Glucosidase was detected. Similarly, the antimicrobial study revealed the extract with good antibacterial and antiviral activity. A good docking score was observed in the in silico computational study of the predominant compounds. The findings revealed L. fragilis as a biocompatible, potent therapeutic alternative and suggest isolation and further in vivo pharmacological studies. 

https://doi.org/10.1016/j.jsps.2023.04.028

A detailed biochemical characterization, toxicological assessment and molecular docking studies of Launaea fragilis: An important medicinal xero-halophyte

Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan

Zaleya pentandra L. jaffery is the only species of the genus Zaleya that grows in the Cholistan desert, Pakistan. It is a Xero-halophyte plant with high phenolic and flavonoid content. The present research was designed to investigate the phytochemical composition, biological activities, and in silico molecular docking of the ethanolic extract of Z. pentandra. The phytochemical evaluation was done through preliminary phytochemical testing, estimation of total bioactive content, and gas chromatography–mass spectrometry (GC–MS) analysis for the identification of volatile compounds. For the evaluation of biological activities, antioxidants, and enzyme inhibition (α-glucosidase, cholinesterase, and tyrosinase), antibacterial and antiviral assays were performed. GC–MS analysis revealed the presence of 29 tentative volatile compounds. The ethanolic extract of Z. pentandra contains high phenolic content (119.6 ± 0.12 mg GAE/g extract) and flavonoid content (45.5 ± 0.19 mg QE/g extract), which correlates with the strong DPPH, FRAP, and enzyme inhibition results. The ethanolic extract of Z. pentandra also showed dose-dependent antibacterial activity. Micrococcus luteus and Pseudomonas aeruginosa were found to be most susceptible, with 16 mm and 17 mm zone of inhibitions at a maximum dose of 20 mg/mL. Antiviral results showed that the ethanol extract has excellent activity against H9, IBV, and NDV viral strains. Additionally, in silico molecular docking was performed in order to determine the interaction and binding affinity between the enzymes and compounds identified by GC–MS. α-glucosidase, cholinesterase, and tyrosinase showed the highest binding affinity toward 1,2-benzenedicarboxylic acid, 2-hydroxy-n-(2-phenylethyl) benzamide, γ-sitosterol, and lactose. These findings can serve as a benchmark for anti-diabetic-, neuro-, and skin-protective uses of this plant and can be used for the isolation of pure bioactive compounds in the future.

/pdf/phytochemical-profiling-of-the-ethanolic-extract-of-zaleya-1cri2p5x.pdf

Phytochemical Profiling of the Ethanolic Extract of Zaleya pentandra L. Jaffery and Its Biological Activities by In-Vitro Assays and In-Silico Molecular Docking

Impact assessment of drought monitoring events and vegetation dynamics based on multi-satellite remote sensing data over Pakistan

District Charsadda is a very important center of plant biodiversity in the central plain of Peshawar valley, Pakistan. The present study was carried out during March 2015 to April 2016 to investigate the ethnobotanical profile of common weed flora present in district Charsadda, KP, Pakistan. The study revealed that there were 40 weed species belonging to 21 families. Among them 25 weeds were annual herb, 9 weeds were perennial herb, three were annual grass, one was climbing herb, one was the parasitic weed, and one was rhizomatic grass. The dominant families were Asteraceae, Fabaceae and poaceae having 5 species (12.5%) each followed by Ranunculaceae 3 species (7.5%). plants were systematically arranged into botanical names, local names, families, habit, habitat, partly used, flowering periods, locality and ethnobotanical uses. The main aim of the study is the documentation and ethnobotanical information of the weed flora growing in the area.

/pdf/ethnobotanical-profile-of-weed-flora-of-district-charsadda-12e73uhm64.pdf

Ethnobotanical Profile of Weed Flora of District Charsadda, Khyber Pakhtunkhwa

The present study was targeted to evaluate eight maize advance lines in comparison to one local check for green fodder yield potential and nutritional quality The experiment was conducted at Fodder Research Institute, Sargodha during August 2017 The experiment was sown in a randomized complete block design with three replications having a plot size of 24 m x 6 m The crop was harvested at 50% flowering stage and data regarding plant height, Number of leaves per plant, leaf length, leaf width, leaf area, leaves weight per plant, stem weight per plant, leaves to stem weight ratio, green fodder yield, dry matter percentage, crude protein and crude fibre were recorded The results showed that the performance of the advance lines; No1501 and MS2010 was best among all the studied genotypes in terms of fodder yield respectively Both genotypes exhibited a good nutritional quality

/pdf/assessment-of-maize-genotypes-for-forage-yield-and-quality-x8byjmxy7w.pdf

Assessment of Maize Genotypes for Forage Yield and Quality

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