Genome-wide association study provides new insight into the underlying mechanism of drought tolerance during seed germination stage in soybean.

Drought is one of the major environmental issues that reduce crop yield. Seed germination is a crucial stage of plant development in all crop plants, including soybean. In soybean breeding, information about genetic mechanism of drought tolerance has great importance. However, at germination stage, there is relatively little knowledge on the genetic basis of soybean drought resistance. The objective of this work was to find the quantitative trait nucleotides (QTNs) linked to drought tolerance related three traits using a genome-wide association study (GWAS), viz., germination rate (GR), root length (RL), and whole seedling length (WSL), using germplasm population of 240 soybean PIs with 34,817 SNPs genotype data having MAF > 0.05. It was observed that heritability (H2) for GR, WSL, and RL across both environments (2020, and 2019) were high in the range of 0.76-0.99, showing that genetic factors play a vital role in drought tolerance as compared to environmental factors. A number of 23 and 27 QTNs were found to be linked to three traits using MLM and mrMLM, respectively. Three significant QTNs, qGR8-1, qWSL13-1, and qRL-8, were identified using both MLM and mrMLM methods among these QTNs. QTN8, located on chromosome 8 was consistently linked to two traits (GR and RL). The area (± 100 Kb) associated with this QTN was screened for drought tolerance based on gene annotation. Fifteen candidate genes were found by this screening. Based on the expression data, four candidate genes i.e. Glyma08g156800, Glyma08g160000, Glyma08g162700, and Glyma13g249600 were found to be linked to drought tolerance regulation in soybean. Hence, the current study provides evidence to understand the genetic constitution of drought tolerance during the germination stage and identified QTNs or genes could be utilized in molecular breeding to enhance the yield under drought stress.

A novel statistical framework of drought projection by improving ensemble future climate model simulations under various climate change scenarios.

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling-Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable.

Bioassay-guided isolation of insect repellent compounds from Ligusticum porteri root extract.

In our program of screening natural products against the pests of medical and veterinary importance, ethanolic extract of the roots of Ligusticum porteri J.M.Coult. & Rose showed significant repellency against mosquitoes. The extract was then fractionated to test different fractions to identify the active repellent compounds. This testing resulted in the isolation of different compounds including (Z)-3-butylidenephthalide, (E)-3-butylidenephthalide, and a mixture of (E/Z)-3-butylidenephthalide. Biting deterrence of all these compounds was similar to N,N-diethyl-3-methylbenzamide (DEET) against Aedes aegypti (L.) in Klun and Debboun (K & D) bioassay. (E/Z)-3-butylidenephthalide which is a mixture of the two compounds was further tested in Ali & Khan (A & K) bioassay. Based on these data repellency of this compound was similar whereas the MED values of the mixtures of (E/Z)-3-butylidenephthalide with carotol were lower (6.25 + 6.25 = 12.5 µg/cm2) than individual treatments (25 µg/cm2). In in vivo (direct skin application bioassay), (E/Z)-3-butylidenephthalide showed excellent repellency. The residual repellency of (E/Z)-3-butylidenephthalide at 8 and 16 % application rates was 4.5 and 10-h respectively which was equal to or better than DEET with the residual time of 5 and 9-h, respectively. The mixture of (E/Z)-3-butylidenephthalide with carotol (8 + 8 %) increased the residual repellency by 2-h (44 %) as compared to (E/Z)-3-butylidenephthalide alone at a dose of 8 %. These data indicated that (E/Z)-3-butylidenephthalide is an effective mosquito repellent that is stable and has a long shelf life. The activity of this compound is extraordinary and residual time is comparable to DEET. In vivo data demonstrated an enormous potential of (E/Z)-3-butylidenephthalide as a repellent that can be developed for commercial use. However, (E/Z)-3-butylidenephthalate was found in lower amounts of the L. porteri essential oil.

Optimizing Mechanical and Electrical Performance of SWCNTs/Fe3O4 Epoxy Nanocomposites: The Role of Filler Concentration and Alignment.

The demand for polymer composites with improved mechanical and electrical properties is crucial for advanced aerospace, electronics, and energy storage applications. Single-walled carbon nanotubes (SWCNTs) and iron oxide (Fe3O4) nanoparticles are key fillers that enhance these properties, yet challenges like orientation, uniform dispersion, and agglomeration must be addressed to realize their full potential. This study focuses on developing SWCNTs/Fe3O4 epoxy composites by keeping the SWCNT concentration constant at 0.03 Vol.% and varying with Fe3O4 concentrations at 0.1, 0.5, and 1 Vol.% for two different configurations: randomly orientated (R-) and magnetic field-assisted horizontally aligned (A-) SWCNTs/Fe3O4 epoxy composites, and investigates the effects of filler concentration, dispersion, and magnetic alignment on the mechanical and electrical properties. The research reveals that both composite configurations achieve an optimal mechanical performance at 0.5 Vol.% Fe3O4, while A- SWCNTs/Fe3O4 epoxy composites outperformed at all concentrations. However, at 1 Vol.% Fe3O4, mechanical properties decline due to nanoparticle agglomeration, which disrupts stress distribution. In contrast, electrical conductivity peaks at 1 Vol.% Fe3O4, indicating that the higher density of Fe3O4 nanoparticles enhances the conductive network despite the mechanical losses. This study highlights the need for precise control over filler content and alignment to optimize mechanical strength and electrical conductivity in SWCNTs/Fe3O4 epoxy nanocomposites.

Genome-wide analysis and prediction of chloroplast and mitochondrial RNA editing sites of AGC gene family in cotton (Gossypium hirsutum L.) for abiotic stress tolerance.

BACKGROUND: Cotton is one of the topmost fiber crops throughout the globe. During the last decade, abrupt changes in the climate resulted in drought, heat, and salinity. These stresses have seriously affected cotton production and significant losses all over the textile industry. The GhAGC kinase, a subfamily of AGC group and member of serine/threonine (Ser/Thr) protein kinases group and is highly conserved among eukaryotic organisms. The AGC kinases are compulsory elements of cell development, metabolic processes, and cell death in mammalian systems. The investigation of RNA editing sites within the organelle genomes of multicellular vascular plants, such as Gossypium hirsutum holds significant importance in understanding the regulation of gene expression at the post-transcriptional level. METHODS: In present work, we characterized twenty-eight GhAGC genes in cotton and constructed phylogenetic tree using nine different species from the most primitive to the most recent. RESULTS: In sequence logos analyses, highly conserved amino acid residues were found in G. hirsutum, G. arboretum, G. raimondii and A. thaliana. The occurrence of cis-acting growth and stress-related elements in the promoter regions of GhAGCs highlight the significance of these factors in plant development and abiotic stress tolerance. Ka/Ks levels demonstrated that purifying selection pressure resulting from segmental events was applied to GhAGC with little functional divergence. We focused on identifying RNA editing sites in G. hirsutum organelles, specifically in the chloroplast and mitochondria, across all 28 AGC genes. CONCLUSION: The positive role of GhAGCs was explored by quantifying the expression in the plant tissues under abiotic stress. These findings help in understanding the role of GhAGC genes under abiotic stresses which may further be used in cotton breeding for the development of climate smart varieties in abruptly changing climate.

Detection of toxic cypermethrin pesticides in drinking water by simple graphitic electrode modified with Kraft lignin@Ni@g-C3N4 nano-composite.

The detrimental effects of widespread pesticide application on the health of living organisms highlight the urgent need for technological advancements in monitoring pesticide residues at trace levels. This study involves the synthesis of a distinctive sensing material, KL@Ni@g-C3N4, which comprises nanocomposites of graphitic carbon nitride with Kraft lignin and nickel. The prepared samples were characterized using FT-IR, PXRD, TEM, SEM, and EDX techniques. The KL@Ni@g-C3N4 nanocomposite was drop-cast on a graphite electrode. Subsequently, this fabricated electrode was used to detect cypermethrin (CYP) residues in drinking water. The redox properties of the fabricated sensors were evaluated using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The limit of detection (LOD) of the fabricated sensor was determined to be 0.026 µg mL-1, which is below the maximum residual limits of CYP in the environment (0.5 µg mL-1) and within the acceptable range for food products (∼0.05 to 0.2 µg mL-1). Therefore, this study proposes a promising alternative to conventional methods for detecting pesticides in drinking water.

Unlocking the potential of biofilm-forming plant growth-promoting rhizobacteria for growth and yield enhancement in wheat (Triticum aestivum L.).

Plant growth-promoting rhizobacteria (PGPR) boost crop yields and reduce environmental pressures through biofilm formation in natural climates. Recently, biofilm-based root colonization by these microorganisms has emerged as a promising strategy for agricultural enhancement. The current work aims to characterize biofilm-forming rhizobacteria for wheat growth and yield enhancement. For this, native rhizobacteria were isolated from the wheat rhizosphere and ten isolates were characterized for plant growth promoting traits and biofilm production under axenic conditions. Among these ten isolates, five were identified as potential biofilm-producing PGPR based on in vitro assays for plant growth-promoting traits. These were further evaluated under controlled and field conditions for their impact on wheat growth and yield attributes. Surface-enhanced Raman spectroscopy analysis further indicated that the biochemical composition of the biofilm produced by the selected bacterial strains includes proteins, carbohydrates, lipids, amino acids, and nucleic acids (DNA/RNA). Inoculated plants in growth chamber resulted in larger roots, shoots, and increase in fresh biomass than controls. Similarly, significant increases in plant height (13.3, 16.7%), grain yield (29.6, 17.5%), number of tillers (18.7, 34.8%), nitrogen content (58.8, 48.1%), and phosphorus content (63.0, 51.0%) in grains were observed in both pot and field trials, respectively. The two most promising biofilm-producing isolates were identified through 16 s rRNA partial gene sequencing as Brucella sp. (BF10), Lysinibacillus macroides (BF15). Moreover, leaf pigmentation and relative water contents were significantly increased in all treated plants. Taken together, our results revealed that biofilm forming PGPR can boost crop productivity by enhancing growth and physiological responses and thus aid in sustainable agriculture.

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models.

Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann-Kendall trend test, and Sen's Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience "moderate drought (MD)" in the twenty-first century.

Chia seed-mediated fabrication of ZnO/Ag/Ag2O nanocomposites: structural, antioxidant, anticancer, and wound healing studies.

Plant extract-mediated fabrication of metal nanocomposites is used in cell proliferation inhibition and topical wound treatment, demonstrating significant effectiveness. Salvia hispanica L. (chia) seed extract (CE) is used as the reaction medium for the green fabrication of ecofriendly ZnO(CE) nanoparticles (NPs) and Ag/Ag2O(CE) and ZnO/Ag/Ag2O(CE) nanocomposites. The resultant nanoparticles and nanocomposite materials were characterized using UV-visible, Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive X-ray (EDX) techniques. In the context of antioxidant studies, ZnO/Ag/Ag2O(CE) exhibited 57% reducing power and 86% 2,2, diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging. All three materials showed strong antibacterial activity against Staphylococcus aureus (S. aureus), Escherichia coli (E.coli), and Bacillus subtilis (B. subtilis) bacterial strains. Additionally, ZnO(CE), Ag/Ag2O(CE), and ZnO/Ag/Ag2O(CE) also revealed 64.47%, 42.56%, and 75.27% in vitro Michigan Cancer Foundation-7 (MCF7) cancer cell line inhibition, respectively, at a concentration of 100 µg/mL. Selectively, the most effective composite material, ZnO/Ag/Ag2O(CE), was used to evaluate in vivo wound healing potential in rat models. The study revealed 96% wound closure in 10 days, which was quite rapid healing compared to wound healing using clinically available ointment. Therefore, in conclusion, the ZnO/Ag/Ag2O(CE) nanocomposite material could be considered for further testing and formulation as a good anticancer and wound healing agent.

iso-Guttiferone J and Structure Revision of Guttiferone J from Garcinia gummi-gutta: A Combined Experimental and Integrated QM/NMR Approach.

Many polyprenylated acylphloroglucinols with fascinating chemical structures and intriguing biological activities have been identified as key to phytochemicals isolated from Garcinia, Hypericum, and related genera. In the present work, two chiral, tautomeric, highly-oxygenated polyprenylated acylphloroglucinols tethered with acyl and prenyl moieties on a bicyclo[3.3.1]nonanetrione core were isolated from the 95% ethanolic extract of Garcinia gummi-gutta fruit. The structures of both compounds were elucidated based on the NMR and MS data with ambiguity in the exact position of the enol and keto functions at C-1 and C-3 of the core structure. The structures of both polyprenylated acylphloroglucinols were established as a structurally revised guttiferone J and the new iso-guttiferone J with the aid of gauge-independent atomic orbital NMR calculations, CP3 probability analyses, specific rotation calculations, and electronic circular dichroism calculations in combination with the experimental data. The structures of both compounds resemble hyperforin, a potent activator of the human pregnane X receptor. As expected, both compounds showed strong pregnane X receptor activation at 10 µM [7.1-fold (guttiferone J) and 5.0-fold (iso-guttiferone J)], explained by a molecular docking study, necessitating further in-depth investigation to substantiate the herb-drug interaction potential of G. gummi-gutta upon co-administration with pharmaceutical drugs.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

Phytochemical analysis of Cichorium bottae root and anti-inflammatory potential assessment of isolates.

The Cichorium plants are particularly notable due to their remarkable therapeutic and medicinal properties, besides being used as food and conventional medication. Although Cichorium plants have been studied for their phytoconstituents and biological activities, there is limited knowledge about the constituents of the roots of C. bottae. A phytochemical study of the 90% MeOH extract of C. bottae roots resulted in the isolation of twelve compounds belonging to guaianolide sesquiterpene lactones, sesquiterpene lactone glucosides, and phenolic derivatives, of which two compounds designated as 9α-hydroxycrepediaside B (1) and cichobotinal (2) were previously undescribed. The isolated compounds were assessed for their anti-inflammatory potential through the inhibition of inducible nitric oxide synthase (iNOS) and resultant decrease in nitric oxide generation in LPS-induced macrophages. Among the isolates, compounds 2 and 11 (8-deoxylactucin) inhibited iNOS activity with IC50 values of 21.0 ± 4 and 6.8 ± 0.1 µM, respectively. The methanolic extract of C. bottae inhibited iNOS with an IC50 of 10.5 ± 0.5 µg/mL.

A novel nor-megastigmane from the leaves of Rhaphiostylis beninensis.

A novel nor-megastigmane, normegastigmane-5α,9-epoxy-3ß,8-diol (1), together with 10 known compounds of diverse classes including megastigmanes, sesquiterpenoids, and triterpenoids were isolated from the leaves of Rhaphiostylis beninensis. The structure of 1 was established by 1D and 2D NMR and HRESIMS data analysis. The known compounds were identified as 5,11-epoxy-3,9-megastigmanediol (2), 7-megastigmene-3,6,9-triol (3), 1,3-dihydroxypropan-2-yl stearate (4), (1E,5E)-1,5-dimethyl-8-(propan-2-ylidene)cyclodeca-1,5-diene germacrene (5) 3,5-dihyroxy-6,7-megastigmadien-9-one (6), squalene (7), ß-amyrin (8), ß-amyrone (9), ß-amyrin eicosanoate (10), and ß-sitosterol (11). Compounds 2, 3, and 6 displayed inhibitory effects on acetylcholinesterase enzyme. This is the first report of these compounds from the plant and their anticholinesterase activity.

Phytochemicals and enzymes inhibitory potentials of leaves and rootbarks of Sarcocephallus latifolius (smith): In vitro and in silico investigations.

In this study, bioactive compounds were isolated and characterized from the leaves and root-barks extracts of S.latifolius, with subsequent in vitro experimental investigations for antihyperglycemic potentials on α-amylase and α-glucosidase enzymes. Thirteen bioactive compounds were identified, including 10-Hydroxystrictosamide (2) and Quinovic acid-3-O-α-L-rhamnosyl-28-O-ß-d-glucopyranosyl ester (8), using chromatographic, nuclear magnetic resonance spectroscopy (NMR), and mass spectrometry (MS) techniques. Experimental assays revealed that compounds 1-4 exhibited potent inhibition of α-amylase and α-glucosidase, with compound 2 demonstrating the most potent α-amylase inhibition (IC50 value of 0.52 ± 0.003 µg/mL). Compound 8 showed a lower IC50 value (0.098 ± 0.016 µg/mL) against α-glucosidase compared to compound 2 and acarbose. Synergistic effects among the compounds could enhance their inhibitory actions on the enzymes, positioning them as potential anti-hyperglycemia agents. Compound 2 displayed the highest binding affinity (-7.970 kcal/mol) when docked against α-amylase (PDB ID: 2QV4), comparable to acarbose (-8.515 kcal/mol). It also ranked among the top ligands against α-glucosidase (PDB ID 3TOP), although compound 13 and acarbose had higher docking scores. All compounds exhibited ideal ADMET properties, suggesting good bioavailability and low toxicity. In conclusion, the isolated compounds exhibit promising antihyperglycemic potential and favourable safety profiles for further exploration.

Dissecting wheat above-ground architecture for enhanced water use efficiency and grain yield in the subtropics.

BACKGROUND: Growing wheat under climate change scenarios challenges, scientists to develop drought and heat-tolerant genotypes. The adaptive traits should therefore be explored and engineered for this purpose. Thus, this study aimed to dissect surface traits and optimizing the leaf architecture to enhance water use efficiency (WUE) and grain yield. Twenty-six wheat genotypes were assessed for five novel leaf traits (NLTs: leaf prickle hairs, groove type, rolling, angle and wettability) under normal, drought and heat conditions following triplicated factorial randomized complete block design (RCBD). The data for NLTs, physiological traits (stomatal conductance, WUE, transpiration, and photosynthesis), and standard morphological and yield traits were recorded. Leaves were sampled at the stem elongation stage (Zadoks 34) to measure the leaf water content (%), contact angle, and to obtain pictures through scanning electron microscopy (SEM). The air moisture harvesting efficiency was evaluated for five selected genotypes. The ideotype concept was applied to evaluate the best-performing genotypes. RESULTS: The correlation analysis indicated that long leaf prickle hairs (> 100 µm), short stomatal aperture and density (40-60 mm- 2), inward to spiral leaf rolling, medium leaf indentation, low contact angle hysteresis (< 10°), and cuticular wax were positively associated with WUE. This, in turn, was significantly correlated to grain yield. Thus, the genotypes (E-1) with these traits and alternate leaf wettability had maximum grain yield (502 g m- 2) and WUE supported with high photosynthesis rate, and relative water content (94 and 75% under normal and stress conditions, respectively). However, the genotype (1-hooded) with dense leaf hairs on edges but droopy leaves, spiral leaf rolling, and lighter groove, also performed better in terms of grain yield (450 g m- 2) under heat stress conditions by maintaining high photosynthesis and WUE with low stomatal conductance and transpiration rate. CONCLUSION: The SEM analysis verified that the density of hairs on the leaf surface and epicuticular wax contributes towards alternate wettability patterns thereby increasing the water-use efficiency and yield of the wheat plant. This study paves a way towards screening and and developing heat and drought-tolerant cultivars that are water-saving and climate-resilient.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network.

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

Phytochemical profiling of Striga asiatica; characterisation and anti-microbial assessment of the isolates.

One undescribed compound, striasinol (1), and twelve previously described compounds were isolated from the aerial parts of Striga asiatica. Structure elucidation of isolated compounds was achieved by the interpretation of 1D and 2D NMR and HRESIMS data. The absolute configuration (1S,5S) of 1 was ascertained based on GIAO NMR calculations, DP4+ probability analysis, and a comparison of the experimental and calculated specific rotation values. The isolated compounds were evaluated for their antimalarial action, and none was found to be effective against the chloroquine-sensitive (D6) or chloroquine-resistant (W2) strains of Plasmodium falciparum. The isolates were found non-toxic to the Vero cell line as well. Subsequent testing of these metabolites for antimicrobial activities against various bacterial and fungal strains (up to 20 µg/mL), revealed that compounds 6 (chryseriol) and 7 (apigenin) showed a reasonable activity towards methicillin-resistant Staphylococcus aureus ATCC 1708 (MRSA), with IC50 values of 5.81 and 3.60 µg/mL, respectively.