Transition Metal Sensing with Nitrogenated Holey Graphene: A First-Principles Investigation.

The toxicity of transition metals, including copper(II), manganese(II), iron(II), zinc(II), hexavalent chromium, and cobalt(II), at elevated concentrations presents a significant threat to living organisms. Thus, the development of efficient sensors capable of detecting these metals is of utmost importance. This study explores the utilization of two-dimensional nitrogenated holey graphene (C2N) nanosheet as a sensor for toxic transition metals. The C2N nanosheet's periodic shape and standard pore size render it well suited for adsorbing transition metals. The interaction energies between transition metals and C2N nanosheets were calculated in both gas and solvent phases and were found to primarily result from physisorption, except for manganese and iron which exhibited chemisorption. To assess the interactions, we employed NCI, SAPT0, and QTAIM analyses, as well as FMO and NBO analysis, to examine the electronic properties of the TM@C2N system. Our results indicated that the adsorption of copper and chromium significantly reduced the HOMO-LUMO energy gap of C2N and significantly increased its electrical conductivity, confirming the high sensitivity of C2N towards copper and chromium. The sensitivity test further confirmed the superior sensitivity and selectivity of C2N towards copper. These findings offer valuable insight into the design and development of sensors for the detection of toxic transition metals.

Emergency esophagectomy: Experience of a high volume esophageal cancer center.

Objective: The objective of the study was to review the experience of dealing oncological emergency esophagectomies at a dedicated Cancer hospital. Methods: We performed a retrospective review of data of eleven esophagectomies at the Department of Surgical Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore (SKMCH&RC) Pakistan, from 1st January, 2009 to 30th June, 2019. Out of 590 oncological esophagectomies, eleven patients had emergency resection. We collected the data of demographics, primary disease, comorbidities, location of tumor and perforation, cause of perforations, radiological and endoscopic findings, clinical findings and follow-up visits after discharge. Data was analyzed by SPSS version 21 for windows. Result: All 11 patients out of five hunded ninety had esophageal cancer. At the time of initial staging, eight (72%) had locally advanced stage (stage III and IV). Open transhiatal approach was used in six (55%) patients, and the rest had three stage esophagectomies. Primary reconstructions with gastric conduit were performed in all, except in two (18%) patients, Respiratory complications were the most common of the encountered complications, seven (63%) of the patients had palliative resection. Ninety day mortality was observed in 3(27.3%) patients. On long term follow up, six patients had recurrence, with median Disease-Free Survival (DFS) 5.88 months and Median Overall Survival (OS) was 6.37 months. Out of 11, only three patients are alive without disease, while one patient is lost during follow-up. Conclusion: Emergency esophagectomy is a lifesaving procedure; there should be multidisciplinary team approach towards the management. Early diagnosis and management is of paramount importance.

Characterization of functional genes GS3 and GW2 and their effect on the grain size of various landraces of rice (Oryza sativa).

BACKGROUND: Grain size is an essential factor of grain quality and yield in rice. The genetic studies have substantially contributed to enhancing yield and maintaining a good quality of rice. The two major genes GS3 (a negative regulator of grain length) and GW2 (a negative regulator of grain width) with functional mutation play a significant role in controlling the grain size of rice. METHODS AND RESULTS: In the study, 17 different widely grown Pakistani landraces of various genetic and geographic backgrounds were evaluated for grain phenotypic traits (1000-grain weight, length, width, and thickness) and also screened for genotypic mutation in GS3 and GW2 genes. Phenotypic data revealed the range for grain weight from 16.86 g (Lateefy) to 26.91 g (PS2), grain length ranged from 7.27 mm (JP-5) to 12.18 mm (PS2), grain width ranged from 2.01 mm (Lateefy) to 3.51 mm (JP5), and grain thickness ranged from 1.79 mm to 2.19. Correlation revealed a negative and significant correlation between grain width and length. There was no significant correlation between grain length and 1000-grain weight and grain width. LSD test displayed that the means of three variables grain length, grain width, and 1000-grain weight were statistically different from one another except grain width and grain breadth. Fifteen accessions carried the domesticated allele of GS3 while JP5 and Fakhr-e-Malakand carried the dominant allele. Similarly, fifteen accessions carried the dominant allele of GW2 while JP-5 and Fakhr-e-Malakand carried the mutant allele. CONCLUSIONS: The study shows that the mutant alleles of both genes are of significance to pyramid them in any breeding program. However, just incorporating favorable alleles is not the sole solution for improving the grain size. Therefore, further elucidation of GS3 and GW2 genes regulatory network, their interaction, trade-off, and pathways will better coordinate their marker-assisted selection in the future breeding program. Additionally, the study concluded that the selection of grain size was not dependent on 1000-grain weight in the selected germplasm.

Evaluation of Jatropha curcas L. leaves mulching on wheat growth and biochemical attributes under water stress.

BACKGROUND: Organic mulches are widely used in crop production systems. Due to their benefits in improving soil fertility, retention of soil moisture and weed control. Field experiments were conducted during wheat growing seasons of 2018-2019 and 2019-2020 to evaluate the effects of Jatropha leaves mulch on the growth of wheat varieties 'Wadan-17' (rainfed) and 'Pirsabaq-2013' (irrigated) under well irrigated and water stress conditions (non-irrigated maintaining 40% soil field capacity). Jatropha mulch was applied to the soil surface at 0, 1, 3 and 5 Mg ha-1 before sowing grains in the field. Under conditions of water stress, Jatropha mulch significantly maintained the soil moisture content necessary for normal plant growth. RESULTS: We noted a decrease in plant height, shoot and root fresh/dry weight, leaf area, leaf relative water content (LRWC), chlorophyll, and carotenoid content due to water stress. However, water stress caused an increase in leaf and root phenolics content, leaf soluble sugars and electrolytes leakage. We observed that Jatropha mulch maintained LRWC, plant height, shoot and root fresh/dry weight, leaf area and chlorophyll content under water stress. Moreover, water stress adverse effects on leaf soluble sugar content and electrolyte leakage were reversed to normal by Jatropha mulch. CONCLUSION: Therefore, it may be concluded that Jatropha leaves mulch will minimize water stress adverse effects on wheat by maintaining soil moisture and plant water status.

Chronic exposure of bisphenol S (BPS) affect hypothalamic-pituitary-testicular activities in adult male rats: possible in estrogenic mode of action.

BACKGROUND: The industrial revolution has resulted in increased synthesis and the introduction of a variety of compounds into the environment and their potentially hazardous effects have been observed in the biota. The present study was aimed to evaluate the potential endocrine-disrupting effects of chronic exposure to the low concentrations of bisphenol S (BPS) in male rats. METHODS: Weaning male Sprague-Dawley rats (22 days old) were either exposed to water containing 0.1% ethanol for control or different concentrations of BPS (0.5, 5, and 50 µg/L) in drinking water for 48 weeks in the chronic exposure study. After completion of the experimental period, animals were dissected and different parameters (hormone concentrations, histology of testis and epididymis, oxidative stress and level of antioxidant enzymes in the testis, daily sperm production (DSP), and sperm parameters) were determined. RESULTS: Results of the present study showed a significant alteration in the gonadosomatic index (GSI) and relative reproductive organ weights. Oxidative stress in the testis was significantly elevated while sperm motility, daily sperm production, and the number of sperm in epididymis were reduced. Plasma testosterone, luteinizing hormone (LH), and follicle-stimulating hormone (FSH) concentrations were reduced and estradiol levels were high in the 50 µg/L-exposed group. Histological observations involved a significant reduction in the epithelial height of the testis along with disrupted spermatogenesis, an empty lumen of the seminiferous tubules, and the caput region of the epididymis. CONCLUSION: These results suggest that exposure to 5 and 50 µg/L of BPS for the chronic duration started from an early age can induce structural changes in testicular tissue architecture and endocrine alterations in the male reproductive system which may lead to infertility in males.

Nutritional value of Sesamum indicum L. was improved by Azospirillum and Azotobacter under low input of NP fertilizers.

BACKGROUND: Sesame (Sesame indicum L.) is well-known as a versatile industrial crop having various usages and contains 50-55% oil, 20% protein, 14-20% carbohydrate and 2-3% fiber. Several environmental factors are known to adversely affect yield and productivity of sesame. Our overall aim was to improve the growth, yield and quality of sesame cv. TS-3 using plant growth promoting rhizobacteria (PGPR) and saving the nitrogen and phosphate fertilizers (NP) by 50%. Field experiment (randomized complete block design) was conducted during the months of July to October of two consecutive years 2012-2013. Azospirillum (AL) and Azotobacter (AV) were applied as seed inoculation alone as well as along with half of the recommended dose of nitrogen (N) and phosphate (P) fertilizers (urea and diammonium phosphate) at the rate of 25 kg/ha and 30 kg/ha respectively. RESULTS: Here we report that A. lipoferum along with half dose of NP fertilizers (ALCF) were highly effective in increasing the agronomic and yield traits of sesame as compared to the control. A. vinelandii plus NP fertilizers (AVCF) exhibited higher seed oil content. Minimum acid value, optimum specific gravity and modified fatty acid composition were observed in ALCF treatment. Increase in oleic acid by ALCF is directly linked with improved oil quality for health benefits as oleic acid is the fatty acid which creates a balance between saturation and unsaturation of oil and for the hypotensive (blood pressure reducing) effects. CONCLUSION: It is inferred that ALCF treatment improved plant growth, seed yield and oil quality of sesame pertaining to good quality edible oil production.

Bioinoculants: A sustainable approach to maximize the yield of Ethiopian mustard (Brassica carinata L.) under low input of chemical fertilizers.

This study aimed to find out the effect of plant growth-promoting rhizobacteria (PGPR; Azospirillum brasilense and Azotobacter vinelandii) either alone or in combination with different doses of nitrogen and phosphate fertilizers on growth, seed yield, and oil quality of Brassica carinata (L.) cv. Peela Raya. PGPR were applied as seed inoculation at 10(6) cells/mL(-1) so that the number of bacterial cells per seed was 2.6 × 10(5) cells/seed. The chemical fertilizers, namely, urea and diammonium phosphate (DAP) were applied in different doses (full dose (urea 160 kg ha(-1) + DAP 180 kg ha(-1)), half dose (urea 80 kg ha(-1) + DAP 90 kg ha(-1)), and quarter dose (urea 40 kg ha(-1) + DAP 45 kg ha(-1)). The chemical fertilizers at full and half dose significantly increased the chlorophyll, carotenoids, and protein content of leaves and the seed yield (in kilogram per hectare) but had no effect on the oil content of seed. The erucic acid (C22:1) content present in the seed was increased. Azospirillum performed better than Azotobacter and its effect was at par with full dose of chemical fertilizers (CFF) for pigments and protein content of leaves when inoculated in the presence of half dose of chemical fertilizers (SPH). The seed yield and seed size were greater. Supplementing Azospirillum with SPH assisted Azospirillum to augment the growth and yield, reduced the erucic acid (C22:1) and glucosinolates contents, and increased the unsaturation in seed oil. It is inferred that A. brasilense could be applied as an efficient bioinoculant for enhancing the growth, seed yield, and oil quality of Ethiopian mustard at low fertilizer costs and sustainable ways.

Comparative impact of genetically modified and non modified maize (Zea mays L.) on succeeding crop and associated weed.

This research work documents the comparative impact of genetically modified (GM) (insect resistance) and non modified maize (Zea mays L.) on growth and germination of succeeding crop wheat (Triticum aestivum L.) and associated weed (Avena fatua L.). The aqueous extracts of both the GM and non-GM maize exhibited higher phenolic content than that of methanolic extracts. Germination percentage and germination index of wheat was significantly decreased by GM methanolic extract (10%) as well as that of non-GM maize at 3% aqueous extract. Similarly germination percentage of weed (Avena fatua L.) was significantly reduced by application of 3% and 5% methanolic GM extracts. All extracts of GM maize showed non-significant effect on the number of roots, root length and shoot length per plant but 5% and 10% methanolic extracts of non-GM maize significantly increased the number of roots per plant of wheat seedling. Similarly, 10% methanolic extract of GM maize significantly increased the number of roots per plant of weed seedling. Methanolic extracts of GM and non-GM maize (3% and 5%) significantly decreased the protease activity in wheat as compared to untreated control.

In vitro antibacterial activity of selected medicinal plants from lower Himalayas.

The present studies cover antibacterial activity of the crude methanolic extracts of 11 medicinal plants viz. Adhatoda vasica, Bauhenia variegate, Bombax ceiba, Carrisa opaca, Caryopteris grata, Debregeasia salicifolia, Lantana camara, Melia azedarach, Phyllanthus emblica, Pinus roxburghii and Olea ferruginea collected from lower Himalayas against two Gram positive (Staphylococcus aureus, Micrococcus luteus) and two Gram negative (Escherichia coli, Pseudomonas aureginosa) bacterial strains. The extracts were applied at four different concentrations (120 mg/mL, 90mg/mL, 60mg/mL and 30mg/mL) in dimethyl sulfoxide (DMSO) by using agar well diffusion method. Antibacterial activities against Staphylococcus aureus and Micrococcus luteus were observed formethanolic extracts of all the above mentioned plants. Greater antibacterial activity against Pseudomonas aeruginosa was only exhibited by Phyllanthus emblica, Pinus roxburghii, Debregeasia salicifolia and Lantana camara. Escherichia coli was highly resistant to all the plant extracts at all concentrations. It is inferred that methanolic crude extracts of the above mentioned plantsexhibitantibacterial activities against pathogenic bacteria, which proved the ethnobotanical importance of the selected plants that indigenous people use for cure against various diseases.

Extended dipeptide composition framework for accurate identification of anticancer peptides.

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.

Efficient prediction of anticancer peptides through deep learning.

Background: Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective: This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods: A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results: The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion: The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work: Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Molecular detection of Toxoplasma gondii in water sources of district Nowshehra, Khyber Pakhtunkhwa, Pakistan.

Toxoplasmosis is spread through contamination of water sources and results in morbidity globally. In the current study 300 water samples were processed by polymerase chain reaction (PCR) for detection of Toxoplasma gondii. The overall prevalence in different water sources was 6.6% (17/300). Among different water sources the highest prevalence was recorded in drain water at 7% (7/100), followed by tube well water at 7.5% (3/40) and open well water at 5% (5/100) ,and the lowest was recorded in tap water at 3.33% (2/60). The highest prevalence was recorded in summer. Evidence indicates that cleaning and filtration need to be adopted to avoid the health hazards of waterborne zoonotic parasites.

Robust and facile detection of formaldehyde through transition metals doped olympicene sensors: a step forward DFT investigation.

Formaldehyde, a volatile organic compound (VOC) released by building and decoration materials, has many applications in the chemical feedstock industry. Excessive release of formaldehyde can cause serious health issues, such as chest tightness, cough, cancer, and tissue damage. Therefore, detection of formaldehyde is required. Herein transition metal (Fe, Ni, and Pd) doped olympicene is evaluated as a gas sensor for the detection of formaldehyde. The performance of the designed electrochemical sensor is evaluated through interaction energy, natural bond orbital (NBO) non-covalent interaction (NCI), electron density differences (EDD), electrostatic potential (ESP), quantum theory of atom in molecule (QTAIM), frontier molecular orbital (FMO), and density of states (DOS) analysis. Interaction energies obtained at B3LYP-D3/def-2 TZVP level of theory shows that formaldehyde is physiosorbed over the surface of transition metal doped olympicene. The trend for interaction energy is OLY(Ni)/HCHO > OLY(Fe)/HCHO > OLY(Pd)/HCHO. The presence of non-covalent interactions is confirmed by the QTAIM and NCI analyses, while transfer of charges is confirmed by natural bond orbital analysis. The reduced density gradient (RDG) approach using noncovalent interaction (NCI) analysis demonstrates that electrostatic hydrogen bonding interactions prevail in the complexes. Recovery time is calculated to check the reusability of the sensor. This study may provide a deep insight for the designing of highly efficient electrochemical sensor against formaldehyde with transition metals doped on olympicene.

Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network.

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance.

Phytotoxic responses of wheat to an imidazolium based ionic liquid in absence and presence of biochar.

Research on ionic liquids (ILs) and biochars (BCs) is a novel site of scientific interest. An experiment was designed to assess the effect of 1-propanenitrile imidazolium trifluoro methane sulfonate ([C2NIM][CF3SO3]) ionic liquid (IL) and IL-BC combination on the wheat plant. Three working standards of the IL; 50, 250, 500 and 1000 mg/L, prepared in deionized water, were tested in the absence and presence of BC on wheat seedling. Results indicated significant decrease in seed germination (%), length, fresh weight, chlorophyll a, b and carotenoid contents of wheat seedlings at 250, 500 and 1000 mg/L of the IL. An admirable increase in phenolic and 2,2-diphenyl-1-picrylhydrazyl (DPPH) contents of wheat seedlings was noted at 250, 500 and 1000 mg/L of the IL. The application of BC significantly ameliorated the negative effects of IL on the selected parameters of wheat. It is inferred that the undesirable effects of the IL on wheat growth can be positively restored by addition of BC.

Inclusive DFT insight into sensing mechanism of cyclotetrapyrole towards lung irritants.

The development of smart sensing devices for toxic analytes detection especially lung irritants is much essential. The cyclic conducting polymers having infinite π-conjugation are proved to be highly sensitive for toxic analytes. Herein, by using the DFT approach, we investigated the sensing mechanism of cyclotetrapyrole (CTPy) for accurate detection of phosgene, diphosgene, chloropicrin and chlorine at the B3LYP-D3/6-31 + G (d, p) level. The calculated interaction energies show the physisorption of analytes over the CTPy surface. Natural bond orbital (NBO) and charge decomposition (CDA) analyses predict charge transfer interactions in the complexes. The reduced density gradient (RDG) approach reveals that hydrogen bonding interactions dominate in the complexes. The sensitivity of CTPy towards lung irritants is further illustrated by the reduction in HOMO-LUMO energy gaps, red shifting of [Formula: see text] in UV-Visible spectra. Density of state (DOS) analysis affirm that enhanced conductivity upon complexation is due to the origination of new energy states in occupied and virtual orbitals nearer to the Fermi level. Moreover, PDOS spectra show that CTPy primarily contributes to the energy of HOMO. The outcome of the current study depicts appreciable sensitivity of CTPy towards lung irritants. Moreover, the competing role of naturally occurring atmospheric water is also investigated. We believe that the upshot of the current findings and their forecasts will provide useful guidelines for an experimentalist to design highly sensitive sensors for toxic analytes using CTPy. HIGHLIGHTS: • The highest QNBO transfer towards the analyte (- 0.121) is seen in the chlorine@CTPy complex. • The highest reduction in Eg (61%) between occupied and virtual orbitals is noticed in chlorine@CTPy. • The orbital overlap results in a 41% red shifting of [Formula: see text] in chlorine@CTPy. • Cyclotetrapyrole is highly sensitive for chlorine.

Adsorption of Industrial Gases (CH4, CO2, and CO) on Olympicene: A DFT and CCSD(T) Investigation.

Olympicene C19H12, an organic semiconductor, is investigated as an adsorption material for toxic industrial gas molecules such as CH4, CO2, and CO. A deep insight of complexation of CH4, CO2, and CO with olympicene (analyte@OLY) was obtained by interaction energy, symmetry-adopted perturbation theory (SAPT2+), quantum theory of atoms in molecules (QTAIM), density of states (DOS), noncovalent interaction (NCI), and frontier molecular orbital and natural bond orbital analysis. Domain-based local pair natural orbital coupled cluster theory single-point energy calculations were performed using the cc-pVTZ basis set in combination with corresponding auxiliary cc-pVTZ/JK and cc-pVTZ/C basis sets. For all property calculations of doped olympicene complexes, the ωB97M-V functional was employed. The stability trend for interaction energies is CO2@OLY > CH4@OLY > CO@OLY. QTAIM and NCI analysis confirmed the presence of NCIs, where the dispersion factor (in CH4@OLY) has the highest contribution, as revealed from SAPT2+. The chemical sensitivity of the system was evidenced by the origination of new energy states in DOS spectra. The recovery time for the analyte@OLY complex was calculated at 300 K, and an excellent recovery response was observed. All results evidently indicated weak interactions of the olympicene surface with CH4, CO2, and CO.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Mulching impact of Jatropha curcas L. leaves on soil fertility and yield of wheat under water stress.

In present studies we have evaluated mulching impact of Jatropha curcas leaves on soil health and yield of two wheat (Triticum aestivum L.) varieties Wadan-2017 (rainfed) and Pirsabak-2013 (irrigated) under imposed water stress. Mulch of Jatropha leaves was spread on the soil surface at the rate of 0, 1, 3 and 5 Mg ha-1 after seed germination of wheat. Water stress was imposed by skipping irrigations for one month at anthesis stage of wheat maintaining 40% soil field capacity. We found a significant decline in soil microbial biomass carbon (30.27%), total nitrogen (22.28%) and organic matter content (21.73%) due to imposed water stress in non-mulch plots. However, mulch application at 5 Mg ha-1 significantly improved soil organic matter (38.18%), total nitrogen (37.75%), phenolics content (16.95 mg gallic acid equivalents/g) and soil microbial biomass carbon (26.66%) as compared to non-mulch control. Soil health indicators like soil carbonates, bicarbonates, electrical conductivity, chloride ions and total dissolved salts were decreased by 5 Mg ha-1 mulch application. We noted a decline in yield indicators like spike weight (14.74%), grain spike-1 (7.02%), grain length (3.79%), grain width (3.16%), 1000 grains weight (6.10%), Awn length (9.21%), straw weight (23.53%) and total grain yield (5.98%) of wheat due to imposed water stress. Reduction in yield traits of wheat due to water stress was higher in Pirsabak-2013 than Wadan-2017. Jatropha leaves mulch application at 5 Mg ha-1 significantly minimized the loss in yield traits of wheat crop caused by water stress. Jatropha curcas leaves mulch application at 5 Mg ha-1 is recommended for the successful establishment of wheat crop under water deficit conditions.

Remarkable static and dynamic NLO response of alkali and superalkali doped macrocyclic [hexa-]thiophene complexes; a DFT approach.

In this study, the nonlinear optical (NLO) response of alkali metal atom (Li, Na and K) and their corresponding superalkali (Li3O, Na3O and K3O) doped six membered cyclic thiophene (6CT) has been explored. The optimized geometries of complexes; Li@6CT, Na@6CT, K@6CT, Li3O@6CT, Na3O@6CT and K3O@6CT depict that the superalkalis and alkali metals interact through the active cavity of 6CT. Interaction energies reveal that superalkalis have higher interaction with 6CT than alkali metals. The nonlinear optical (NLO) response of the reported complexes is estimated via both static and dynamic hyperpolarizabilities which are further rationalized by the HOMO-LUMO gap, natural bond orbital (NBO) charge transfer, dipole moment, polarizabilities and ß vec. A remarkably high NLO response is computed for Na3O@6CT among all of the complexes. The static hyperpolarizability of the Na3O@6CT complex is 5 × 104 au along with the highest ß vec value (2.5 × 104 au). High charge transfer (1.53e -) and small E H-L gap (2.96 eV) is responsible for such a large NLO response. For dynamic NLO responses, electro-optic Pockel's effect (EOPE) and second-harmonic generation (SHG) are explored. A very large quadratic nonlinear optical response (3.8 × 10-12 au) is observed for the Na3O@6CT complex. Moreover, the absorption spectrum of the Na3O@6CT complex shows ultra-high transparency in the ultraviolet and visible regions unlike any other of its counterparts.