Comparative taxonomical, biological and pharmacological potential of healthy and geminivirus infected leaves of Hibiscus rosa-sinensis L.: First report.

In the present research project, the first report on comparative analysis of the taxonomical, biological and pharmacological potential of healthy and geminivirus infected Hibiscus rosa sinensis (L.) leaves of the family Malvaceae was done by using different micro and macroscopic techniques. First of all, leaves were characterized for Cotton leaf curl Multan virus (CLCuMuV) and its associated betasatellite (Cotton leaf curl Multan Betasatellite; CLCuMB). Different morphological parameters like shape and size of stem, leaves, seeds and roots, presence and absence of ligule, distance between nodes and internodes and type of inflorescence etc. were analyzed. CLCuMuV infected H. rosa-sinensis revealed systematic symptoms of infection like chlorosis of leaves, stunted growth, decrease in size of roots, shoots and distortion etc. Anatomical investigation was performed under light ad scanning electron microscope. Different anatomical features like length and shape of guard cells, subsidiary cells, presence or absence of stomata, secretory ducts and trichomes were examined. In both plant samples anomocytic types of stomata and elongated, non-glandular and pointed tip trichomes were present, but the size (especially length and width) of trichomes and other cells like epidermal, subsidiary, and guard cells were highest in virus infected plants likened to healthy one. In the antibacterial activity, the maximum antibacterial potentail was seen in methanolic extract of K. pneumonea while antifungal activity was shown by methanolic extract of A. solani. Plants interact with different biological entities according to environmental conditions continuously and evolved. These types of interactions induce changes positively and negatively on plant metabolism and metabolites production. Many plant viruses also attacked various host plants consequently alter their secondary metabolism. To overcome such virus infected plants produces many important and different types of secondary plant metabolites as a defense response. Subsequent analysis of this n-hexane plant extract using Gas chromatography mass spectroscopy technique revealed that Hibiscus eluted contained 10 main compounds in Healthy sample and 13 compounds in infected one. Presence of essential secondary metabolites were also analyzed by FTIR analysis. The present study provides a comprehensive and novel review on taxonomy (morphology, anatomy) and antimicrobial potential of both healthy and geminivirus infected H. rosa-sinensis.

Artificial intelligence-assisted air quality monitoring for smart city management.

Background: The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods: This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. Results: In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.

A kinetic study of ex-situ soil remediation by nickel extraction using natural deep eutectic solvent.

Using natural deep eutectic solvents (NADESs) as a green reagent is a step toward producing environmentally friendly and sustainable technology. This study screened three natural DESs developed using quaternary ammonium salt and organic acid to analyse their capability to extract nickel ions from contaminated mangrove soil, which are ChCl: Acetic Acid (ChCl-AceA), ChCl: Levulinic Acid (ChCl-LevA), and ChCl: Ethylene Glycol(ChCl-Eg) at molar ratio 1:2. The impact of various operating parameters such as washing agent concentration, pH solution, and contact time on the NADES performance in the dissolution of Ni ions batch experiments were performed. The optimal soil washing conditions for metal removal were 30% and 15% concentration, a 1:5 soil-liquid ratio, and pH 2 of ChCl-LevA and ChCl-AceA, respectively. A single removal washing may remove 70.8% and 70.0% Ni ions from the contaminated soil. The dissolution kinetic of Ni ions extraction onto NADES was explained using the linear kinetic pseudo and intraparticle mass transfer diffusion models. The kinetic validation demonstrates a good fit between the experimental and pseudo-second-order Lagergren data. The model's maximum Ni dissolution capacity, Qe are 51.56 mg g-1 and 52.00 mg g-1 of ChCl-LevA and ChCl-AceA, respectively. The synthesised natural-based DES has the potential to be a cost-effective, efficient, green alternative extractant to conventional solvent extraction of heavy metals.

Synthesis and Characterization of Zinc Oxide Nanoparticles at Different pH Values from Clinopodium vulgare L. and Their Assessment as an Antimicrobial Agent and Biomedical Application.

The current study attempts to evaluate the formation, morphology, and physico-chemical properties of zinc oxide nanoparticles (ZnO NPs) synthesized from Clinopodium vulgare extract at different pH values and to investigate their antimicrobial and biomedical application potential. The reduction of zinc ions to ZnO NPs was determined by UV spectra, which revealed absorption peaks at 390 nm at pH 5 and 348 nm at pH 9, respectively. The spherical morphology of the nanoparticles was observed using scanning electron microscopy (SEM), and the size was 47 nm for pH 5 and 45 nm for pH 9. Fourier-transformed infrared spectroscopy (FTIR) was used to reveal the presence of functional groups on the surface of nanoparticles. The antibacterial activity was examined against Staphylococcus aureus, Streptococcus pyogenes, and Klebsiella pneumonia via the agar-well diffusion method. Comparatively, the highest activities were recorded at pH 9 against all bacterial strains, and among these, biogenic ZnO NPs displayed the maximum inhibition zone (i.e., 20.88 ± 0.79 mm) against S. aureus. ZnO NPs prepared at pH 9 exhibited the highest antifungal activity of 80% at 25 mg/mL and antileishmanial activity of 82% at 400 mg/mL. Altogether, ZnO NPs synthesized at pH 9 show promising antimicrobial potential and could be used for biomedical applications.

Efficacy of Naphthyl Acetic Acid Foliar Spray in Moderating Drought Effects on the Morphological and Physiological Traits of Maize Plants (Zea mays L.).

The threat of varying global climates has greatly driven the attention of scientists, as climate change increases the odds of worsening drought in many parts of Pakistan and the world in the decades ahead. Keeping in view the forthcoming climate change, the present study aimed to evaluate the influence of varying levels of induced drought stress on the physiological mechanism of drought resistance in selected maize cultivars. The sandy loam rhizospheric soil with moisture content 0.43-0.5 g g-1, organic matter (OM) 0.43-0.55 g/kg, N 0.022-0.027 g/kg, P 0.028-0.058 g/kg, and K 0.017-0.042 g/kg was used in the present experiment. The findings showed that a significant drop in the leaf water status, chlorophyll content, and carotenoid content was linked to an increase in sugar, proline, and antioxidant enzyme accumulation at p < 0.05 under induced drought stress, along with an increase in protein content as a dominant response for both cultivars. SVI-I & II, RSR, LAI, LAR, TB, CA, CB, CC, peroxidase (POD), and superoxide dismutase (SOD) content under drought stress were studied for variance analysis in terms of interactions between drought and NAA treatment and were found significant at p < 0.05 after 15 days. It has been found that the exogenous application of NAA alleviated the inhibitory effect of only short-term water stress, but yield loss due to long-term osmotic stress will not be faced employing growth regulators. Climate-smart agriculture is the only approach to reduce the detrimental impact of global fluctuations, such as drought stress, on crop adaptability before they have a significant influence on world crop production.

Aconitum lycoctonum L. (Ranunculaceae) mediated biogenic synthesis of silver nanoparticles as potential antioxidant, anti-inflammatory, antimicrobial and antidiabetic agents.

In this study, a polar extract of Aconitum lycoctonum L. was used for the synthesis of silver nanoparticles (AgNPs), followed by their characterization using different techniques and evaluation of their potential as antioxidants, amylase inhibitors, anti-inflammatory and antibacterial agents. The formation of AgNPs was detected by a color change, from transparent to dark brown, within 15 min and a surface resonance peak at 460 nm in the UV-visible spectrum. The FTIR spectra confirmed the involvement of various biomolecules in the synthesis of AgNPs. The average diameter of these spherical AgNPs was 67 nm, as shown by the scanning electron micrograph. The inhibition zones showed that the synthesized nanoparticles inhibited the growth of Gram-positive and negative bacteria. FRAP and DPPH assays were used to demonstrate the antioxidant potential of AgNPs. The highest value of FRAP (50.47% AAE/mL) was detected at a concentration of 90 ppm and a DPPH scavenging activity of 69.63% GAE was detected at a concentration of 20 µg/mL of the synthesized AgNPs. 500 µg/mL of the synthesized AgNPs were quite efficient in causing 91.78% denaturation of ovalbumin. The AgNPs mediated by A. lycoctonum also showed an inhibitory effect on α-amylase. Therefore, AgNPs synthesized from A. lycoctonum may serve as potential candidates for antibacterial, antioxidant, anti-inflammatory, and antidiabetic agents.

Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review.

Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.

In Vivo Glutathione S-Transferases Superfamily Proteome Analysis: An Insight into Aedes albopictus Mosquitoes upon Acute Xenobiotic Challenges.

In this study, the induction of glutathione S-transferase (GST) enzymatic activities in Aedes albopictus under 24 h of xenobiotic challenges was investigated. From LCMS analysis, 23 GST isoforms were identified under Delta, Epsilon, Sigma, Zeta, Omega, and Iota classes, together with one GSTX1-1 isoform, in both treated and untreated samples. Using STRING 11.5, the functional enrichment network of Gene Ontology (GO) analysis, the identified peptides were found to be involved in the glutathione metabolic biological process (GO:0006749, p-value: 1.93 × 10−29), and the molecular functions involved are due to glutathione transferase (GO:0016848, p-value: 2.92 × 10−8) aside from carbon-halide lyase activity (GO:004364, p-value: 1.21 × 10−31). The Protein-Protein Interaction (PPI) network (STRING 11.5) showed significant interactions within the GST superfamily and some of the GST classes interacted with other proteins among the input domain of the identified peptides (p-value < 1.0 × 10−16). In TMT labeling for the quantification of peptide abundance, isoforms from Delta (GSTD1-2, GSTD1-3, GSTD1-4) and Epsilon (GSTE3-1, GSTE4-2) were found to be overexpressed (between 1.5-fold and 2-fold changes). In the PPI analysis, 12 common enriched pathways of Kyoto Encyclopedia of Genes and Genomes (KEGG) were found to be intercorrelated with the identified GSTs at PPI enrichment p-value < 1.0 × 10−16. Overall, this study indicates that distinct GST enzymes, which were identified up to their specific protein isoforms, are involved in the metabolic mechanisms underlying xenobiotic stress.