Efficient performance of InP and InP/ZnS quantum dots for photocatalytic degradation of toxic aquatic pollutants.

In recent years, the growing concern over the presence of toxic aquatic pollutants has prompted intensive research into effective and environmentally friendly remediation methods. Photocatalysis using semiconductor quantum dots (QDs) has developed as a promising technology for pollutant degradation. Among various QD materials, indium phosphide (InP) and its hybrid with zinc sulfide (ZnS) have gained considerable attention due to their unique optical and photocatalytic properties. Herein, InP and InP/ZnS QDs were employed for the removal of dyes (crystal violet, and congo red), polyaromatic hydrocarbons (pyrene, naphthalene, and phenanthrene), and pesticides (deltamethrin) in the presence of visible light. The degradation efficiencies of crystal violet (CV) and congo red (CR) were 74.54% and 88.12% with InP, and 84.53% and 91.78% with InP/ZnS, respectively, within 50 min of reaction. The InP/ZnS showed efficient performance for the removal of polyaromatic hydrocarbons (PAHs). For example, the removal percentage for naphthalene, phenanthrene, and pyrene was 99.8%, 99.6%, and 88.97% after the photocatalytic reaction. However, the removal percentage of InP/ZnS for pesticide deltamethrin was 90.2% after 90 min light irradiation. Additionally, advanced characterization techniques including UV-visible spectrophotometer (UV-Vis), photoluminescence (PL), X-ray diffractometer (XRD), energy-dispersive spectrometer (EDS) elemental mapping, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA) were used to analyze the crystal structure, morphology, and purity of the fabricated materials in detail. The particle size results obtained from TEM are in the range of 2.28-4.60 nm. Both materials (InP and InP/ZnS) exhibited a spherical morphology, displaying distinct lattice fringes. XRD results of InP depicted lattice planes (111), (220), and (311) in good agreement with cubic geometry. Furthermore, the addition of dopants was discovered to enhance the thermal stability of the fabricated material. In addition, QDs exhibited efficacy in the breakdown of PAHs. The analysis of their fragmentation suggests that the primary mechanism for PAHs degradation is the phthalic acid pathway.

Melatonin-Induced Stress Enhanced Biomass and Production of High-Value Secondary Cell Products in Submerged Adventitious Root Cultures of Stevia rebaudiana (Bert.).

Stress is one of the important factors that directly or indirectly affects the plant architecture, biochemical pathways, and growth and development. Melatonin (MEL) is an important stress hormone; however, the exogenous addition of melatonin to culture media stimulates the defense mechanism and releases higher quantities of secondary metabolites. In this study, submerged adventitious root cultures (SARCs) of diabetically important Stevia rebaudiana were exposed to variable concentrations (0.5-5.0 mg/L) of MEL in combination with 0.5 mg/L naphthalene acetic acid (NAA) to investigate the biomass accumulation during growth kinetics with 07 days intervals for a period of 56 days. The effects of exogenous MEL on the biosynthesis of stevioside (Stev.), total phenolics content (TPC), total flavonoids content (TFC), total phenolics production (TPP), total flavonoids production (TFP), total polyphenolics content (TPPC), fresh and dry weight (FW & DW), and antioxidant potential were also studied. Most of the SARCs displayed lag, exponential, stationary, and decline phases with variable biomass accumulation. The maximum fresh (236.54 g/L) and dry biomass (28.64 g/L) was observed in SARCs exposed to 3.0 mg/L MEL and 0.5 mg/L NAA. The same combination of MEL and NAA also enhanced the accumulation of TPC (18.96 mg/g-DW), TFC (6.33 mg/g-DW), TPP (271.51 mg/L), TFP (90.64 mg/L), and TPPC (25.29 mg/g-DW). Similarly, the highest stevioside biosynthesis (91.45 mg/g-DW) and antioxidant potential (86.15%) were observed in SARCs exposed to 3.0 mg/L MEL and NAA. Moreover, a strong correlation was observed between the biomass and the contents of phenolics, flavonoids, antioxidants, and stevioside. These results suggest that MEL is one of multidimensional stress hormones that modulate the biosynthetic pathways to release higher quantities of metabolites of interest for various industrial applications.

Fractionation and Characterization of Metallic Elements in Soils in Land Use Systems.

Land use has a great impact on soil dynamics. The soils of various land use systems in Central Karakoram have been under immense pressure in the recent past due to certain anthropogenic activities such as land use practices and land use cover changes. These influences have an impact on the spatial distribution of metallic elements (MEs) in the soils of various land uses. Herein, we investigated the occurrence of the MEs, copper (Cu), zinc (Zn), and nickel (Ni), in soils of various land uses such as the permafrost, pasture, forest, and agricultural lands of the Central Karakorum region. The MEs were extracted in exchangeable, adsorbed, organically bound, carbonated, precipitated, and residual forms. The concentrations of MEs showed a significant dependence on the extraction method used, and the extraction trend followed the order of EDTA > HNO3 > KNO3 > NaOH > H2O. Zn showed the highest concentration compared to Ni and Cu in all extractions, whereas the land uses' ME concentration followed the order of agricultural land > permafrost > forest > pasturelands. The highest values of total Zn, Ni, and Cu were 712 ± 01 mg/kg, 656 ± 02 mg/kg, and 163 ± 02 mg/kg, respectively, in agricultural soil. The ME concentration showed significant variations between different land uses, and the highest concentration was noted in agricultural soil. Zn was found to be a dominant ME compared to Ni and Cu. We believe this effort will provide opportunities for scholars to investigate MEs around the globe.

Application of manganese oxide-based materials for arsenic removal: A review.

In the context of growing arsenic (As) contamination in the world, there is an urgent need for an effective treatment approach to remove As from the environment. Industrial wastewater is one of the primary sources of As contamination, which poses significant risks to both microorganisms and human health, as the presence of As can disrupt the vital processes and synthesis of crucial macromolecules in living organisms. The global apprehension regarding As presence in aquatic environments persists as a key environmental issue. This review summarizes the recent advances and progress in the design, strategy, and synthesis method of various manganese-based adsorbent materials for As removal. Occurrence, removal, oxidation mechanism of As(III), As adsorption on manganese oxide (MnOx)-based materials, and influence of co-existing solutes are also discussed. Furthermore, the existing knowledge gaps of MnOx-based adsorbent materials and future research directions are proposed. This review provides a reference for the application of MnOx-based adsorbent materials to As removal.

Keeping their own and integrating the other: medicinal plant use among Ormurs and Pathans in South Waziristan, Pakistan.

BACKGROUND: In multicultural societies, traditional knowledge among minorities faces several challenges. Minority groups often face difficult situations living in specific peripheral geographies and striving to retain their biocultural heritage, including medicinal plant knowledge and practices. Folk medicinal plant knowledge is a dynamic eco-cultural complex influenced by various environmental, socio-cultural, and political factors. Examining medicinal plant knowledge among minorities has been an increasingly popular topic in cross-cultural ethnobiology. It also helps understand the dynamics of local/traditional ecological knowledge (LEK/TEK) change within a given community. The current study was designed to investigate the status of medicinal plant knowledge among two linguistic groups, i.e. Ormurs and Pathans, living in a remote valley of West Pakistan. METHODS: We recruited 70 male study participants from the studied groups for semi-structured interviews to record the medicinal plant use of their communities. Data were compared among the two studied communities using the stacked charts employing the presence or absence of data with Past 4.03 and Venn diagrams. Use reports (URs) were counted for each recorded taxon. RESULTS AND DISCUSSION: A total of seventy-four medicinal plants were quoted as used as ethnomedicines by the researched communities. Most of the reported plants were used to treat digestive and liver problems. The cross-cultural comparison revealed a considerable homogeneity of medicinal plant knowledge (the two groups commonly used more than seventy plants); however, comparing uses recorded for the widely utilised medicinal plants showed numerous idiosyncratic uses among Ormurs but very few among Pathans. Ormurs reported a higher number of cultivated, wild, and imported plant uses than did Pathans. These results indicate that, compared to Pathans, the Ormur linguistic minority retain more folk medicinal plant knowledge, which may be explained by the fact that they have incorporated different folk remedies: their "own knowledge" plus that of Pathans, with whom they have lived together for centuries. Moreover, the local plant nomenclature among Ormurs was highly affected by the plant nomenclature of Pathans. CONCLUSION: The current study revealed that living together for a few centuries has not implied sharing plant knowledge (as the Pathans do not seem to have learnt from the Ormurs) or, in other words, that plant knowledge exchanges have been unidirectional. The findings show that the Pashto dominant culture may have possibly put pressure on the minority groups and affected local plant-centred cultural practices, as we see in the case of local plant nomenclature hybridisation among Omuri speakers. Hence, it is imperative to employ diverse educational strategies to revitalise the decline of medicinal plant knowledge in the studied communities, especially among Ormurs, who need more attention as they face more challenges than the other group. Locally based strategies should be devised to restore the fading connection with nature, which will be advantageous for revitalising plant knowledge.

Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models.

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 µg/L, with an average of 11.64 µg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 µg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

Assessment of heavy metals among auto workers in metropolitan city: a case study.

In recent decades, heavy metals (HMs) have emerged as a global health concern. Unfortunately, in Pakistan, there is a general lack of awareness regarding the potential health risks associated with HMs pollution among automobile workers. Herein, we investigated the concentration of heavy metals such as lead (Pb), cadmium (Cd), and chromium (Cr) among automobile workers who were occupationally exposed in Mingora City, Khyber Pakhtunkhwa, Pakistan. Three different automobile groups, i.e., battery recyclers, spray painters, and mechanics were studied in detail. A total of 40 blood samples were collected from automobile workers groups while 10 blood samples were collected as control individuals from different locations in the study area. We investigated heavy metals concentration with a standard method using an atomic absorption spectrometer AAS (PerkinElmer Analyst 700, United States). Based on our findings, the battery recycling group displayed the most elevated Pb levels (5.45 ± 2.11 µg/dL), exceeding those of both the spray painters' group (5.12 ± 1.98 µg/dL) and the mechanics' group (3.79 ± 2.21 µg/dL). This can be attributed to their higher exposure to Pb pollution resulting from the deterioration, dismantling, grinding, or crushing of old batteries. In the context of chromium (Cr) exposure, a similar trend was observed among the battery recycling group, as well as the spray painters and mechanics groups. However, in the case of cadmium (Cd), the mechanics' group exhibited the highest level of exposure (4.45 ± 0.65 µg/dL), surpassing the battery recycling group (1.17 ± 0.45 µg/dL) and the spray painters' group (1.35 ± 0.69 µg/dL), which was attributed to their greater exposure to welding fumes and other activities in their workplace. We believe that our findings will encourage regulatory measures to improve the health of automobile workers. However, further work is needed to determine various health-related issues associated with heavy metal exposure among automobile workers.

Integrated approach to hydrogeochemical appraisal of groundwater quality concerning arsenic contamination and its suitability analysis for drinking purposes using water quality index.

Arsenic (As), contamination in drinking groundwater resources is commonly environmental problem in many developing countries including Pakistan, with significant human health risk reports. In order to examine the groundwater quality concerning As contamination, its geochemical behavior along with physicochemical parameters, 42 samples were collected from community tube wells from District Bahawalpur, Punjab, Pakistan. The results showed the concentration of elevated As, its source of mobilization, and associated public health risk. The As concentration detected in groundwater samples varied from 0.12 to 104 µg/L with an average value of 34.7 µg/L. Among 42 groundwater samples, 27 samples were beyond the permitted limit of 10 µg/L recommended by World Health Organization (WHO), for drinking purposes. Statistical analysis result show that the groundwater cations values are in decreasing order such as: Na+ > Mg2+ > Ca2+ > K+, while anions were HCO3- > SO42- > Cl- > NO3-. Hydrochemical facies result depict that the groundwater samples of the study area, 14 samples belong to CaHCO3 type, 5 samples belong to NaCl type, 20 samples belong to Mixed CaMgCl type, and 3 samples belong to CaCl2 type. It can be accredited due to weathering and recharge mechanism, evaporation processes, and reverse ion exchange. Gibbs diagram shows that rock water interaction controls the hydrochemistry of groundwater resources of the study area. Saturation Index (SI) result indicated the saturation of calcite, dolomite, gypsum, geothite, and hematite mineral due their positive SI values. The principal component analysis (PCA) results possess a total variability of 80.69% signifying the anthropogenic and geogenic source of contamination. The results of the exposure-health-risk-assessment method for measuring As reveal significant potential non-carcinogenic risk (HQ), exceeding the threshold level of (> 1) for children in the study area. Water quality assessment results shows that 24 samples were not suitable for drinking purposes.

Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms.

Children's health is one of the most significant fields in medicine. Most diseases that result in children's death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children's urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child's medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms.

DC-AAE: Dual channel adversarial autoencoder with multitask learning for KL-grade classification in knee radiographs.

Knee osteoarthritis (OA) is a frequent musculoskeletal disorder that leads to physical disability in older adults. Manual OA assessment is performed via visual inspection, which is highly subjective as it suffers from moderate to high inter-observer variability. Many deep learning-based techniques have been proposed to address this issue. However, owing to the limited amount of labelled data, all existing solutions have limitations in terms of performance or the number of classes. This paper proposes a novel fully automatic Kellgren and Lawrence (KL) grade classification scheme in knee radiographs. We developed a semi-supervised multi-task learning-based approach that enables the exploitation of additional unlabelled data in an unsupervised as well as supervised manner. Specifically, we propose a dual-channel adversarial autoencoder, which is first trained in an unsupervised manner for reconstruction tasks only. To exploit the additional data in a supervised way, we propose a multi-task learning framework by introducing an auxiliary task. In particular, we use leg side identification as an auxiliary task, which allows the use of more datasets, e.g., CHECK dataset. The work demonstrates that the utilization of additional data can improve the primary task of KL-grade classification for which only limited labelled data is available. This semi-supervised learning essentially helps to improve the feature learning ability of our framework, which leads to improved performance for KL-grade classification. We rigorously evaluated our proposed model on the two largest publicly available datasets for various aspects, i.e., overall performance, the effect of additional unlabelled samples and auxiliary tasks, robustness analysis, and ablation study. The proposed model achieved the accuracy, precision, recall, and F1 score of 75.53%, 74.1%, 78.51%, and 75.34%, respectively. Furthermore, the experimental results show that the suggested model not only achieves state-of-the-art performance on two publicly available datasets but also exhibits remarkable robustness.

Antibacterial Efficacy of Green Synthesized Silver Nanoparticles Using Salvia nubicola Extract against Ralstonia solanacearum, the Causal Agent of Vascular Wilt of Tomato.

Ralstonia solanacearum is a phytopathogen causing bacterial wilt diseases of tomato and affecting its productivity, which leads to prominent economic losses annually. As an alternative to conventional pesticides, green synthesized nanoparticles are believed to possess strong antibacterial activities besides being cheap and ecofriendly. Here, we present the synthesis of silver nanoparticles (Sn-AgNPs) from medicinally important aqueous plant extracts of Salvia nubicola. Characterization of biologically synthesized nanoparticles was performed through UV-vis spectrophotometry, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy (TEM), and thermogravimetric analysis. The antibacterial activity of the biosynthesized silver nanoparticles was tested against the phytopathogen R. solanacearum through in vitro experiments. Preliminary phytochemical analysis of the plant extracts revealed the presence of substantial amounts of flavonoids (57.08 mg GAE/g), phenolics (42.30 mg GAE/g), tannins, and terpenoids. The HPLC phenolic profile indicated the presence of 25 possible bioactive compounds. Results regarding green synthesized silver nanoparticles revealed the conformation of different functional groups through FTIR analysis, which could be responsible for the bioreduction and capping of Ag ions into silver NPs. TEM results revealed the spherical, crystalline shape of nanoparticles with the size in the range of 23-63 nm, which validates SEM results. Different concentrations of Sn-AgNPs (T1 (500 µg/mL) to T7 (78.1 µg/mL)) with a combination of plant extracts (PE-Sn-AgNPs) and plant extracts alone exhibited an efficient inhibition of R. solanacearum. These findings could be used as an effective alternative preparation against the bacterial wilt of tomato.

Geochemical control, water quality indexing, source distribution, and potential health risk of fluoride and arsenic in groundwater: Occurrence, sources apportionment, and positive matrix factorization model.

Fluoride (F-), and arsenic (As) in the groundwater cause health problems in developing countries, including Pakistan. We evaluated the occurrence, distribution, sources apportionment, and health hazards of F-, and As in the groundwater of Mardan, Pakistan. Therefore, groundwater samples (n = 130) were collected and then analyzed for F-, and As by ion-chromatography (IC) and Inductively-coupled plasma mass-spectrometry (ICP-MS). The F-, and As concentrations in groundwater were 0.7-14.4 mg/L and 0.5-11.2 µg/L. Relatively elevated F-, and As coexists with higher pH, Na+, HCO3-, SO4-2, and depleted Ca+2 due to fluoride, sulfide-bearing minerals, and anthropogenic inputs. Both F-, and/or As are transported in subsurface water through adsorption and desorption processes. Groundwater samples 45%, and 14.2% exceeded the WHO guidelines of 1.5 mg/L and 10 µg/L. Water quality indexing (WQI-model) declared that 35.7% samples are unfit for household purposes. Saturation and undersaturation of minerals showed precipitation and mineral dissolution. Groundwater contamination by PCA-MLR and PMF-model interpreted five factors. The fitting results and R2 values of PMF (0.52-0.99)>PCA-MLR (0.50-0.95) showed high accuracy of PMF-model. Human health risk assessment (HHRA-model) revealed high non-carcinogenic and carcinogenic risk for children than adults. The percentile recovery of F- and As was recorded 98%, and 95% with reproducibility ± 5% error.

TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma.

The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with TP53 followed by CDKN2A being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed that TP53, MDM2, CDKN2A, and TP53BP1 were overexpressed, while MDM4 and CDKN2B were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, CDKN2A was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the MDM4 was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. MDM2 expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. TP53 showed a positive correlation with B cells, MDM2 with CD8+T cells, macrophages and neutrophils, and MDM4 with neutrophils. CDKN2A/B had a non-significant correlation with all six types of immune cells. However, TP53BP1 was positively correlated with all five types of immune cells except B cells. Only TP53, MDM2, and CDKN2A had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.

Antimicrobial efficacy of Mentha piperata-derived biogenic zinc oxide nanoparticles against UTI-resistant pathogens.

Misuse of antibiotics leads to the worldwide spread of antibiotic resistance, which motivates scientists to create new antibiotics. The recurring UTI due to antibiotics-resistant microorganism's challenges scientists globally. The biogenic nanoparticles have the potential to meet the escalating requirements of novel antimicrobial agents. The green synthesis of nanoparticles (NPs) gained more attention due to their reliable applications against resistant microbes. The current study evaluates the biogenic ZnO NPs of Mentha piperata extract against resistant pathogens of urinary tract infections by agar well diffusion assay. The biogenic ZnO NPs revealed comparatively maximum inhibition in comparison to synthetic antibiotics against two bacterial strains (Proteus mirabilis, Pseudomonas aeruginosa) and a fungal strain (Candida albicans).The synthesized biogenic ZnO NPs alone revealed maximum activities than the combination of plant extract (PE) and ZnO NPs, and PE alone. The physiochemical features of ZnO NPs characterized through UV-Vis spectroscopy, FTIR, XRD, SEM, and EDX. The UV-Vis spectroscopy revealed 281.85 nm wavelengths; the XRD pattern revealed the crystalline structure of ZnO NPs. The FTIR analysis revealed the presence of carboxylic and nitro groups, which could be attributed to plant extract. SEM analysis revealed spherical hollow symmetry due to electrostatic forces. The analysis via EDX confirmed the presence of Zn and oxygen in the sample. The physiochemical features of synthesized ZnO NPs provide pivotal information such as quality and effectiveness. The current study revealed excellent dose-dependent antimicrobial activity against the pathogenic isolates from UTI-resistant patients. The higher concentration of ZnONPs interacts with the cell membrane which triggers oxidative burst. They may bind with the enzymes and proteins and brings epigenetic alteration which leads to membrane disruption or cell death.

Comparative Effects of Hydropriming and Iron Priming on Germination and Seedling Morphophysiological Attributes of Stay-Green Wheat.

Seed priming is considered to play an essential role in the overall improvement of agricultural crops. The current research work was carried out in order to investigate the comparative effects of hydropriming and iron priming on the germination behavior and morphophysiological attributes of wheat seedlings. The experimental materials consisted of three wheat genotypes including a synthetically derived wheat line (SD-194), stay-green wheat genotype (Chirya-7), and conventional wheat variety (Chakwal-50). The treatments included hydro (distilled and tap water)- and iron priming (10 and 50 mM) of wheat seeds for 12 h duration. Results indicated that both priming treatment and wheat genotypes exhibited highly different germination and seedling characteristics. These included germination percentage, root volume, root surface, root length, relative water content, chlorophyll content, membrane stability index, and chlorophyll fluorescence attributes. Furthermore, the synthetically derived line (SD-194) was the most promising in majority of the studied attributes by exhibiting a high germination index (2.21%), root fresh weight (7.76%), shoot dry weight (3.36%), relative water content (19.9%), chlorophyll content (7.58%), and photochemical quenching coefficient (2.58%) when compared with stay-green wheat (Chirya-7). The study also found that hydropriming with tap water and priming wheat seeds with low concentrations of iron yielded better results when a comparison was made with wheat seeds primed at high concentrations of iron. Therefore, wheat seed priming with tap water and iron solution for 12 h is recommended for optimum wheat improvement. Furthermore, current findings suggest that seed priming may have the prospect of an innovative and user-friendly approach for wheat biofortification with the aim of enhanced iron acquisition and accumulation in grains.

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques.

With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants' privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants' privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods-AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)-are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.

SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation.

Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.

Biochemical and phenological characterization of diverse wheats and their association with drought tolerance genes.

Drought is one of the most important wheat production limiting factor, and can lead to severe yield losses. This study was designed to examine the effect of drought stress on wheat physiology and morphology under three different field capacities (FC) viz. 80% (control), 50% (moderate) and 30% (severe drought stress) in a diverse collection of wheat germplasm including cultivars, landraces, synthetic hexaploid and their derivatives. Traits like grain weight, thousand grain weight and biomass were reduced by 38.23%, 18.91% and 26.47% respectively at 30% FC, whereas the reduction rate for these traits at 50% FC were 19.57%, 8.88% and 18.68%. In principal component analysis (PCA), the first two components PC1 and PC2 accounted for 58.63% of the total variation and separated the cultivars and landraces from synthetic-based germplasm. Landraces showed wide range of phenotypic variations at 30% FC compared to synthetic-based germplasm and improved cultivars. However, least reduction in grain weight was observed in improved cultivars which indicated the progress in developing drought resilient cultivars. Allelic variations of the drought-related genes including TaSnRK2.9-5A, TaLTPs-11, TaLTPs-12, TaSAP-7B-, TaPPH-13, Dreb-B1 and 1fehw3 were significantly associated with the phenological traits under drought stress in all 91 wheats including 40 landraces, 9 varieties, 34 synthetic hexaploids and 8 synthetic derivatives. The favorable haplotypes of 1fehw3, Dreb-B1, TaLTPs-11 and TaLTPs-12 increased grain weight, and biomass. Our results iterated the fact that landraces could be promising source to deploy drought adaptability in wheat breeding. The study further identified drought tolerant wheat genetic resources across various backgrounds and identified favourable haplotypes of water-saving genes which should be considered to develop drought tolerant varieties.

Unveiling Lathyrus aphaca L. as a Newly Identified Host for Begomovirus Infection: A Comprehensive Study.

The Begomovirus genus of the family Geminiviridae comprises the largest group of geminiviruses. Begomoviruses are transmitted by the whitefly complex (Bemisia tabaci) and infect dicotyledonous plants in tropical and subtropical regions. The list of begomoviruses is continuously increasing as a result of improvements in the methods for identification, especially from weed plants, which are considered a source of new viruses and reservoirs of economically important viruses but are often neglected during diversity studies. Lathyrus aphaca L. weed plants (yellow-flowered pea) with varicose veins and discoloration of the leaves were found. Amplified genomic DNA through rolling circular amplification was subjected to PCR analysis for the detection of the viral genome and associated DNA-satellites (alphasatellites and betasatellites). A full-length sequence (2.8 kb) of a monopartite begomovirus clone was determined; however, we could not find any associated DNA satellites. The amplified full-length clone of Rose leaf curl virus (RoLCuV) reserved all the characteristics and features of an Old World (OW) monopartite begomovirus. Furthermore, it is the first time it has been reported from a new weed host, yellow-flowered pea. Rolling circle amplification and polymerase chain reaction analysis of associated DNA satellites, alphasatellite, and betasatellite, were frequently accomplished but unable to amplify from the begomovirus-infected samples, indicating the presence of only monopartite Old World begomovirus. It is observed that RoLCuV has the capability to infect different hosts individually without the assistance of any DNA satellite component. Recombination in viruses is also a source of begomovirus infection in different hosts.

Water stress effects on stay green and chlorophyll fluorescence with focus on yield characteristics of diverse bread wheats.

MAIN CONCLUSION: The study provided an insight toward better understanding of stay-green mechanisms for drought tolerance improvement and identified that synthetic-derived wheats proved as a promising germplasm for improved tolerance against water stress. Stay-green (SG) trait is considered to be related with the ability of wheat plants to maintain photosynthesis and CO2 assimilation. The present study explored the interaction of water stress with SG expression through physio-biochemical, agronomic and phenotypic responses among diverse wheat germplasm comprising of 200 synthetic hexaploids, 12 synthetic derivatives, 97 landraces and 16 conventional bread wheat varieties, for 2 years. The study established that variation of SG trait existed in the studied wheat germplasm and there was positive association between SG trait and tolerance to water stress. The relationship of SG trait with chlorophyll content (r = 0.97), ETR (r = 0.28), GNS (r = 0.44), BMP (r = 0.34) and GYP (r = 0.44) was particularly promising under water stress environment. Regarding chlorophyll fluorescence, the positive correlation of фPSII (r = 0.21), qP (r = 0.27) and ETR (r = 0.44) with grain yield per plant was noted. The improved ΦPSII and Fv/Fm of PSII photochemistry resulted in the high photosynthesis activity in SG wheat genotypes. Regarding relative water content and photochemical quenching coefficient, synthetic-derived wheats were better by maintaining 20.9, 9.8 and 16.1% more RWC and exhibiting 30.2, 13.5 and 17.9% more qP when compared with landraces, varieties and synthetic hexaploids, respectively, under water stress environment. Synthetic derived wheats also exhibited relatively more SG character with good yield and were more tolerant to water stress in terms of grain yield, grain weight per plant, better photosynthetic performance through chlorophyll fluorescence measurement, high leaf chlorophyll and proline content, and hence, may be used as novel sources for breeding drought tolerant materials. The study will further facilitate research on wheat leaf senescence and will add to better understanding of SG mechanisms for drought tolerance improvement.