Search

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. [ABSTRACT FROM AUTHOR]

Isolated dynamic sign language recognition (IDSLR) has the potential to change accessibility and inclusion by enabling speech and/or hearing-impaired people to engage more completely in a variety of spheres of life, including social interactions, work, and more. IDSLR is a challenging task due to considering a sequence of image frame analysis with multiple linguistic features for a single gesture in cluttered backgrounds and an illumination variation environment. We have proposed a Hybrid Efficient Convolution (HEC) model that ensembles EfficientNet-B3 and a few modified layers as an alternative to traditional machine learning techniques with improved performances in cluttered backgrounds with illumination variation environments. The architecture of the HCE integrates pre-trained layers of EfficientNet-B3 loaded with customized weights and a new custom dense layer featuring 256 units, followed by batch normalization, dropout, and the final output layer. To enhance the robustness of the system, we employed the augmentation technique during pre-processing. Then, the system executes channel-wise feature transformation through point-wise convolution that reduces the computational complexity and increases the accuracy. The updated dense layer with 256 units processes the output from the standard EfficientNet-B3, shaping the model into a hybrid form to achieve better performance. We have created our own gesture dataset, called "BdSL_OPA_23_GESTURES," which consists of 6000 video clips of 100 isolated dynamic Bangla Sign Language words, with 60 videos for each word from 20 different people in the cluttered background with illumination variation environments to train and evaluate the performances of the proposed model. We have considered 80% of the total dataset for training purpose, while the remaining 20% is dedicated to testing and validation. In a small number of epochs, our proposed HEC model achieves a superior accuracy of 93.17% on our created "BdSL_OPA_23_GESTURES" dataset. All the information of the proposed model with the dataset has been shared along with the scientific community to provide access publicly at: https://github.com/Prothoma2001/Bangla-Continuous-Sign-Language-Recognition.git. [ABSTRACT FROM AUTHOR]

Lung Cancer remains a significant health concern, necessitating the exploration of novel therapeutic avenues due to the limited efficacy and adverse effects of current treatments. In this study, we utilized a thorough in-silico and in-vitro methodology to develop prospective drugs for the treatment of lung cancer. The active components of Citrus latifolia were identified through the utilization of a variety of pharmacological instruments, such as Gene Ontology, GeneCards, DrugBank, the Chinese Traditional Drug Database, and GeneMANIA. Subsequent molecular docking studies using GOLD software revealed Hesperidin as the most promising candidate, exhibiting a remarkable binding affinity (GOLD score: 60.98 kcal/mol) towards the epidermal growth factor receptor (EGFR), a pivotal target in lung cancer therapy. Further validation through Schrodinger-Glide redocking reaffirmed the robust interaction between Hesperidin and EGFR. Pharmacokinetic profiling of top-scoring ligands indicated favorable drug-like properties, supporting their therapeutic potential. Molecular dynamics simulations employing Desmond software demonstrated the structural stability and persistence of the Hesperidin-EGFR complex over a 100-ns trajectory, corroborating its efficacy. Additionally, cytotoxicity analysis revealed a potent inhibitory effect of Hesperidin with an IC50 value of 34.25 µg/ml. Collectively, our findings underscore Hesperidin from Citrus latifolia as a promising candidate for lung cancer therapy, warranting further investigation through in-vivo studies for clinical translation. [ABSTRACT FROM AUTHOR]

Sclerotinia sclerotiorum stands out as the most destructive pathogen affecting oilseed Brassica crops. Our study unveils the proteomic basis of a novel resistance mechanism, termed "Stem-Physical-Strength-Mediated-Resistance (SPSMR)," against S. sclerotiorum in Brassicaceae through a comparative proteomic analysis. Field assessments highlight significant differences in stem-physical strength attributes between the resistant (R) and susceptible (S) genotypes, emphasizing the importance of SPSMR. Field evaluation revealed that the resistant genotype S. alba SA1 demonstrates significantly (P ≤ 0.01) superior stem traits at various time points post-inoculation as compared to susceptible genotypes. Pearson's correlation analysis establishes significant associations between lesion length and stem attributes, with stem breaking strength emerging as a key contributor to resistance. Proteomic profiling at different infection stages reveals temporal dynamics, showcasing the resistant genotype's robust and adaptive defense response. KEGG enrichment analysis underscores the significance of phenylalanine metabolism and phenylpropanoid biosynthesis pathways. Differentially Expressed Proteins (DEPs) in resistant and susceptible genotypes revealed intricate expression profiles, particularly in lignin biosynthesis. Proteins associated with cell wall fortification, especially in the lignin biosynthetic pathway, exhibit nuanced expression profiles. Specific proteins, including phenylalanine ammonia-lyase, shikimate dehydrogenase, cinnamyl alcohol dehydrogenase 5, and peroxidase, show significantly higher expression in the resistant genotype across infection stages. Additionally, proteins involved in plant-pathogen, intracellular pH regulation, and antioxidant defense exhibit differential expression, contributing to a comprehensive understanding of the complex regulatory network during S. sclerotiorum infection. This research not only enhances our understanding of the molecular mechanisms underlying resistance but also underscores the varied strategies utilized by Brassicaceae to combat pathogenic intrusion, emphasizing the potential for developing resistant cultivars against S. sclerotiorum. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases Dataset, Corn Dataset, and Coffee Dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess the neuroimaging abnormalities and their progression in patients with Subacute sclerosing panencephalitis (SSPE) and identify clinical predictors of these imaging findings. Methods: This prospective observational study evaluated clinical and neuroimaging features in patients with SSPE. Patients were categorized using Dyken's criteria, Jabbour's staging system, and the definition of fulminant SSPE. They underwent comprehensive clinical assessments, cerebrospinal fluid examination, Electroencephalogram (EEG), and Magnetic Resonance Imaging (MRI) scans. Treatment involved intrathecal interferon‑α and antiepileptic medications. Functional disability was assessed using the modified Barthel index. Follow-ups were performed at 6 months, including reassessment of Modified Barthel Index (MBI) and Jabbour's staging and EEG and MRI scans. Results: The mean age was 13.9 ± 6.7 years, with males comprising 81.5% (44/54) of the cohort. Fulminant SSPE was noted in 33% (18/54) of cases. Disease duration before presentation varied significantly between fulminant and non-fulminant forms (p = 0.001). Neuroimaging abnormalities were more prevalent in JS III stage patients, with diffuse cerebral atrophy being a significant finding (p = 0.011). Basal ganglia involvement correlated with movement disorders. The 6‑month follow-up showed increased cerebral atrophy (p = 0.004). Increasing disease duration was an independent predictor of cerebral atrophy. An Intercomplex interval (ICI) of more than 10 minutes correlated with normal neuroimaging, 10 patients died within the study period, 8 of whom had fulminant SSPE. Conclusion: Parieto-occipital White matter hyperintensity (WMH) is the most prevalent and sensitive neuroimaging finding for the diagnosis of SSPE. Despite interferon treatment, cerebral atrophy progressed in both aggressive and fulminant SSPE. Increasing disease duration is an independent predictor of cerebral atrophy. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly growing, providing convenience in daily life. However, ensuring the security of IoT systems is crucial, especially as they generate vast amounts of data, often containing personal and private information. To address these issues, we propose a quantum-safe blockchain attested encryption scheme that uses lattice-based encryption with blockchain technology. This consensus process removes reliance on third-party central service providers and uses consensus nodes as proxy service nodes to encrypt data and aggregate translated ciphertext. This approach mitigates the limitations of using a decentralized network for secure storage and dissemination of private encrypted data. Moreover, the security analysis demonstrates security against attacks from polynomial-time adversaries and probabilistic polynomial-time adversaries. Additionally, the performance evaluation shows that the proposed scheme outperforms existing ones in terms of computational and communication costs. The encryption and decryption costs are minimized to T ∗ 1 + (T ∗ 2 + T ∗ 3 + T + 1) ≈ 3.096 , and T ∗ 6 ≈ 0.010 , respectively, demonstrating its efficiency. A comparison of properties and features with other protocols highlights the proposed scheme’s advantages, such as quantum resistance, multi-authority support, and collision resistance. This combination establishes the proposed scheme as a robust solution. The proposed protocol employs lattice-based encryption and blockchain consensus algorithms to facilitate IoT data exchange, making it highly efficient and secure for the transfer of information in IoT systems. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic-associated mucor epidemic, acute antifungal drug shortage necessitated the exploration of other antifungals based on culture sensitivity. Itraconazole is a cheap, safe, and effective antifungal in sensitive cases. Methodology: We enrolled itraconazole-sensitive COVID-19-associated mucormycosis during the mucormycosis pandemic. After the intensive phase course of liposomal amphotericin B, Itraconazole was offered in susceptible cases during the maintenance phase along with standard of care. These patients were clinically and radiologically followed for 6 months. Results: We enrolled 14 patients (Male: Female–11:3) of Rhino-orbito-cerebral mucormycosis (ROCM) which included 12 diabetics. All patients had facial swelling, orbital swelling, visual impairment, and headache. MRI showed involvement of bilateral sinus (10/14), orbital extension (13/14), cavernous sinus (5/14), cerebral part of the internal carotid artery (3/14), and brain infarcts (4/14). All 14 patients showed sensitivity to Itraconazole with 12 having minimum inhibitory concentration (MIC) ≤ 1 μg/ml and 2 having MIC ≤ 2 μg/ml. Follow-up at 6 months showed clinical improvement in the majority (11/14) and radiological improvement in six out of seven scanned patients. Conclusion: Our study shows the potential therapeutic role of oral Itraconazole in ROCM. [ABSTRACT FROM AUTHOR]

Grass carp intestinal waste-mediated biosynthesized nanosilver (AgNPs) was valorized using guaran and zeolite matrices, resulting in AgNPs-guaran, AgNPs-zeolite, and AgNPs-guaran -zeolite composites. The valorized products were examined using Environmental Scanning Electron Microscopy, Energy Dispersive X-ray analysis and X-ray Diffraction analysis to confirm uniform dispersion and entrapment of AgNPs within the matrixes. These valorized products were evaluated for their efficacy in detoxifying the ubiquitous and toxic hexavalent chromium (Cr6+) in aquatic environments, with Anabas testudineus exposed to 2 mg l−1 of Cr6+ for 60 days. Remarkable reduction of Cr6+ concentration to 0.86 ± 0.007 mg l−1 was achieved with AgNPs-guaran-zeolite composite, indicating successful reclamation of contaminated water and food safety assurance. Consistency in results was further corroborated by minimal stress-related alterations in fish physiological parameters and integrated biomarker response within the experimental group treated with the AgNPs-guaran-zeolite composite. Despite observed chromium accumulation in fish tissues, evidence of physiological stability was apparent, potentially attributable to trivalent chromium accumulation, serving as an essential nutrient for the fish. Additionally, the challenge study involving Anabas testudineus exposed to Aeromonas hydrophila exhibited the lowest cumulative mortality (11.11%) and highest survival rate (87.5%) within the same experimental group. The current study presents a novel approach encompassing the valorization of AgNPs for Cr6+ detoxification under neutral to alkaline pH conditions, offering a comprehensive framework for environmental remediation. [ABSTRACT FROM AUTHOR]

Measles inclusion-body encephalitis (MIBE) is rare, with insights largely from case studies. We systematically analyzed subacute Sclerosing Panencephalitis (SSPE) cases in immunocompromised patients, identifying distinctive clinical and neuroimaging features. These findings could facilitate MIBE diagnosis without the need for brain biopsies. Our systematic review on MIBE and HIV-related SSPE adhered to PRISMA guidelines and was registered with PROSPERO. We searched multiple databases and followed a detailed inclusion process with independent reviews and quality assessment. Data on patient demographics, clinical features, and outcomes were compiled. A review of 39 studies on 49 MIBE patients and 8 reports on HIV-positive SSPE patients was conducted. Acute lymphoblastic leukemia, HIV, organ transplants, and malignancies were common precursors to MIBE. Perinatal HIV was prevalent among SSPE cases. Seizures were the primary symptom in MIBE, often drug-resistant and progressing to status epilepticus or epilepsia partialis continua, whereas periodic myoclonus was universal in SSPE. Neuroimaging showed distinct patterns for each group, and histopathology confirmed measles virus presence in 39% of MIBE cases. MIBE patients typically progressed to coma and death. In conclusion, MIBE and SSPE in HIV-infected patients present with distinct clinical pictures but identical brain pathological abnormalities. [ABSTRACT FROM AUTHOR]

Objective: To compare the diagnostic efficacy of multiplex polymerase chain reaction (PCR), Mycobacterium leprae-specific repetitive element (RLEP) PCR and loop-mediated isothermal amplification (LAMP) PCR in the diagnosis of pediatric leprosy as an alternative to slit-skin smear (SSS) examination. Methods: A cross-sectional study was performed on 26 children aged 0–18 years with characteristic skin lesions of leprosy. SSS examination for acid fast bacilli (AFB) was performed for all children. Additionally, urine, stool and blood samples were tested by three PCR techniques - multiplex, RLEP and LAMP. The results of these tests were compared with each other and with results of SSS examination for acid fast bacilli (AFB) using appropriate statistical tests. Results: Out of 26 patients studied, SSS examination was positive for AFB in 7 cases (26.9%). In blood samples, the positivity of multiplex PCR, RLEP PCR and LAMP PCR was 84.6%, 80.8%, and 80.8%, respectively. Multiplex PCR in blood samples was positive in 100% (n = 7) of SSS positive cases and 84.2% (16 out of 19) of the SSS negative cases (P < 0.001). The positivity of all PCR methods in urine and stool samples was significantly lesser than in blood. Conclusion: Multiplex PCR in blood sample is a superior diagnostic tool for pediatric leprosy compared to RLEP PCR and LAMP PCR as well as SSS examination. [ABSTRACT FROM AUTHOR]

We study the thermodynamics of black holes (BHs) with a Gauss–Bonnet correction term in ()-dimensional AdS space-time. It is known that this term has no effect on the equations of motion, however, it modifies the entropy which is calculated using Wald's formula. The corrections to the area law appear in the form of a term involving the Gauss–Bonnet parameter. We study charged BHs, namely, Reissner-Nordström and Born–Infeld, under this regime. The first thing we encounter are divergences in heat capacity. After eliminating the possibility of first-order phase transition, we apply two well trusted methods from standard thermodynamics, namely, the Ehrenfest scheme and the Ruppeiner state space geometry analysis to ensure the second-order nature of the phase transition points. Our main focus in this study is on the effects of the Gauss–Bonnet and Born–Infeld parameters on the phase transition points. [ABSTRACT FROM AUTHOR]

With the rise in technology especially the integration of internet of Things (IoT) with Wireless Sensor Networks (WSNs) an extensive network of interconnected devices and sensors is formed to frequently collect and transmit sensitive data. However, this data may get accessed by unauthorized persons during transmission leading to erroneous decisions and potentially disastrous consequences in domains like healthcare and industrial automation.Considering the importance of protecting data, a security-based route selection model for IoT-WSN is proposed. It involves pre-identification of malicious nodes and calculation of trust using the proposed ITrust mechanism. The IGWO (Improved Grey Wolf Optimization) model weighs nodes according to their trust, energy, and connection request factors. This judgment determines the node's next hop, guaranteeing secure communication. Using MATLAB software, the suggested trust-based approach's effectiveness is assessed through Energy consumption, Packet Delivery ratio (PDR) and delay factors. Experimental results show that our approach accurately identifies malicious nodes, ensuring integrity of data transmission. The efficient routing scheme significantly reduces delays and maximizes the PDR, resulting in improved communication efficiency and reliability. [ABSTRACT FROM AUTHOR]

The persistent challenges posed by pollution and climate change are significant factors disrupting ecosystems, particularly aquatic environments. Numerous contaminants found in aquatic systems, such as ammonia and metal toxicity, play a crucial role in adversely affecting aquaculture production. Against this backdrop, fish feed was developed using quinoa husk (the byproduct of quinoa) as a substitute for fish meal. Six isonitrogenous diets (30%) and isocaloric diets were formulated by replacing fish meal with quinoa husk at varying percentages: 0% quinoa (control), 15, 20, 25, 30 and 35%. An experiment was conducted to explore the potential of quinoa husk in replacing fish meal and assess its ability to mitigate ammonia and arsenic toxicity as well as high-temperature stress in Pangasianodon hypophthalmus. The formulated feed was also examined for gene regulation related to antioxidative status, immunity, stress proteins, growth regulation, and stress markers. The gene regulation of sod, cat, and gpx in the liver was notably upregulated under concurrent exposure to ammonia, arsenic, and high-temperature (NH3 + As + T) stress. However, quinoa husk at 25% downregulated sod, cat, and gpx expression compared to the control group. Furthermore, genes associated with stress proteins HSP70 and DNA damage-inducible protein (DDIP) were significantly upregulated in response to stressors (NH3 + As + T), but quinoa husk at 25% considerably downregulated HSP70 and DDIP to mitigate the impact of stressors. Growth-responsive genes such as myostatin (MYST) and somatostatin (SMT) were remarkably downregulated, whereas growth hormone receptor (GHR1 and GHRβ), insulin-like growth factors (IGF1X, IGF2X), and growth hormone gene were significantly upregulated with quinoa husk at 25%. The gene expression of apoptosis (Caspase 3a and Caspase 3b) and nitric oxide synthase (iNOS) were also noticeably downregulated with quinoa husk (25%) reared under stressful conditions. Immune-related gene expression, including immunoglobulin (Ig), toll-like receptor (TLR), tumor necrosis factor (TNFα), and interleukin (IL), strengthened fish immunity with quinoa husk feed. The results revealed that replacing 25% of fish meal with quinoa husk could improve the gene regulation of P. hypophthalmus involved in mitigating ammonia, arsenic, and high-temperature stress in fish. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel Swastik photonic sensor (SPS) to detect cancerous cells using human blood samples. The proposed structure has sharp bends at the corner of central rods by introducing line and point defects to achieve better interaction of light with the analytes. The analytes such as Basal, HeLa, Jurkat, PC-12, MDA-MB-231, and MCF-7 are placed individually at the center of the Swastik structure and electric field distribution has been analyzed at ports 1, 2, and 3. We have only obtained a linear relationship between electric field intensity and analytes refractive index at port 2 and calculated all the sensing parameters at the same port using the finite difference time domain (FDTD) method. We have observed the highest sensitivity of 3648.214 nm/RIU at the resonance wavelength of 1566.173 nm for MCF-7 cancerous cells, whereas the highest quality factor (Q) and figure of merit (FoM) is obtained for Jurkat cancerous cell at 1522.189 nm. The highest limit of detection (LoD) and signal-to-noise ratio (SNR) have been achieved at resonance wavelengths of 1505.745 nm and 1524.568 nm, respectively, for Basal and HeLa cancerous cells. We have compared all our calculated values with the available results in the literature. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is crucial for deploying a novel Artificial Intelligence (AI) model for both network and application management. However, using classical centralized learning algorithms in the IoT environment is challenging, given massively distributed private datasets. Advancements in AI have helped us solve various use cases, but it operates under two significant challenges. Firstly, the data exists in separate clusters, and secondly, the current AI has limited data privacy and security. Federated learning (FL) aims to preserve data privacy through distributed learning methods that keep the data in storage silos. Likewise, differential privacy improves data privacy by measuring the privacy loss in communication among the elements of FL. The paper proposes two adaptive approaches for making model training differentially private in a vertical federated environment. The first one uses random feature selection to train different machine learning models, and performance improvement is also proposed. The second approach uses a tree structure, i.e., Classification and Regression Trees, using some defined constraints. Further, we created a scheme to help identify malicious users/devices in a federated network cluster using parity checks for every FL iteration. [ABSTRACT FROM AUTHOR]

The periodontal tissue initiates an immune-inflammatory response against bacteria and their products at the site of periodontal infection, and systemic attack by these pathogens triggers an acute phase response that contributes to the systemic inflammatory burden. Obstructive sleep apnea (OSA) and periodontal diseases share many common risk factors and are therefore disorders associated with, and possibly related to, systemic inflammation. Objectives 1.) To evaluate the severity of infection-causing chronic periodontitis subjects leading to Coronary Artery Disease (CAD) risk. 2.) To evaluate specific Inflammatory marker TNF-α in coronary artery disease (CAD) subjects with chronic periodontitis and obstructive sleep apnea. Methods A total of 5 ml of the venous blood was collected from each participant which was separated out by centrifugation at 3000 rpm for 5–10 min at room temperature for the estimation of inflammatory markers. Collected samples were labeled and stored at − 20 °C in a deep freezer. The estimation of human TNF-α (tumor necrosis factor-alpha) levels was determined using an ELISA kit based on the Sandwich-ELISA principle. Conclusion TNF-α may serve as important markers for the diagnosis of chronic periodontitis and obstructive sleep apnea and the prediction of the severity of cardiovascular diseases. Cardiovascular disease can be efficiently circumvented with a biomarker-based approach to treatment, which also benefits patients' quality of life. Results The result of our study showed that TNF-α is involved in the onset and progression of obstructive sleep apnea leading to coronary artery disease since the expression levels of TNF in the case group were considerably higher than those in the control group. [ABSTRACT FROM AUTHOR]

The stagnant crop productivity and declining factor productivity especially under rice-wheat system in changing climate scenario demand the adoption of nutrient responsive high yielding climate-resilient varieties. Considering these challenges, the present study was conducted during two consecutive Rabi seasons of 2020-21 and 2021-22 with an aim to improve wheat productivity through appropriate combinations of genotypes, nutrient management and plant growth regulators (PGRs). The experiment was conducted in split-plot design with two nutrient levels {recommended dose of fertilizer (RDF) as 150:60:40 kg N, P2O5 and K2O ha-1, respectively, and 150% RDF + 15 t FYM (farm yard manure) ha-1 + two sprays of PGRs consisting of tank-mix of chlormequat chloride @ 400 g + tebuconazole @ 172 g ha-1 at the first node and flag leaf stage} in main-plots and nine genotypes (DBW187, DBW303, DBW327, DBW332, DBW370, DBW371, DBW372, HD3086 and PBW872) in sub-plots. The plant height significantly reduced while earheads m-2 and grains m-2 improved with PGRs application under high fertility which led to increased (12.6%) mean grain yield over RDF. Among genotypes, maximum yield was observed for DBW370 (67.84 q ha-1) followed by PBW872, DBW371 and DBW372. Nutrient management and genotype interaction revealed that more grains m-2 in DBW370 led to maximum yield (66.2 q ha-1) at RDF while bolder grains in PBW872 made it top yielder (71.16 q ha-1) under high fertility condition. It is concluded that productivity of modern wheat cultivars can be improved through higher fertility and tank-mix application of chlormequat + tebuconazole. [ABSTRACT FROM AUTHOR]

The current investigation explores the mechanisms of ammonia and arsenic toxicity, along with high-temperature stress, which other researchers rarely addressed. Pangasianodon hypophthalmus was exposed to low doses of ammonia and arsenic (1/10th of LC50, 2.0 and 2.68 mg L−1, respectively) and high temperature (34 °C) for 105 days. The following treatments were applied: control (unexposed), arsenic (As), ammonia (NH3), ammonia + arsenic (NH3 + As), ammonia + temperature (NH3 + T), and NH3 + As + T. Cortisol levels significantly increased with exposure to ammonia (NH3), arsenic (As), and high temperature (34 °C) compared to the unexposed group. Heat shock protein (HSP 70), inducible nitric oxide synthase (iNOS), and metallothionein (MT) gene expressions were notably upregulated by 122–210%, 98–122%, and 64–238%, respectively, compared to the control. Neurotransmitter enzymes (acetylcholine esterase, AChE) were significantly inhibited by NH3 + As + T, followed by other stressor groups. The apoptotic (caspase, Cas 3a and 3b) and detoxifying (cytochrome P450, CYP P450) pathways were substantially affected by the NH3 + As + T group. Immune (total immunoglobulin, Ig; tumor necrosis factor TNFα; and interleukin IL) and growth-related genes (growth hormone, GH; growth hormone regulator, GHR1 and GHRβ; myostatin, MYST and somatostatin, SMT) were noticeably upregulated by NH3 + As + T, followed by other stress groups, compared to the control group. Weight gain %, protein efficiency ratio, feed efficiency ratio, specific growth rate, and other growth attributes were significantly affected by low doses of ammonia, arsenic, and high-temperature stress. Albumin, total protein, globulin, A:G ratio, and myeloperoxidase (MPO) were highly affected by the As + NH3 + T group. Blood profiling, including red blood cells (RBC), white blood count (WBC), and hemoglobin (Hb), were also impacted by stressor groups compared to the control group. Genotoxicity, as DNA damage, was significantly higher in groups exposed to NH3 + As + T (89%), NH3 + T (78%), NH3 (73), NH3 + As (71), and As (68%). The bioaccumulation of arsenic was substantially higher in liver and kidney tissues. The present study contributes to understanding the toxicity mechanisms of ammonia and arsenic, as well as high-temperature stress, through different gene expressions, biochemical attributes, genotoxicity, immunological status, and growth performance of P. hypophthalmus. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent healthcare framework by incorporating modern computing technologies like machine learning and deep learning. The sole motivation of this paper is to predict diabetes status intelligently so that patients can be made aware of their diabetes status regularly. A novel ensemble machine learning-based diabetes prediction framework, namely "iDP", has been proposed in this paper. Six different machine learning approaches have been used for the iDP framework, namely random forest, decision tree, neural network, AdaBoost, support vector machine, and XGBoost. Training and testing of ensemble modeling have been performed based on these learning techniques. A comprehensive preliminary data analysis and screening of statistical analysis have been systematically demonstrated in this paper. The recursive feature elimination and fivefold cross-validation methods have been used to select the best model. All experiments have been performed using R-Studio 1.2.5 with the help of the Rattle library 5.4.0. The preliminary data analytic and Pearson correlation coefficient values have been computed to verify the linearity relationship among the data. Results have been reported in eight performance metrics, including accuracy, specificity, sensitivity, area under curve, area under convex hull, minimum error rate, minimum weighted loss, and precision. The proposed iDP framework improves the results on different aspects in order to compare them with individual learning approaches. Despite this, a comparative performance analysis of the iDP framework has also been performed with state-of-the-art prevailing models. As per experiment, the iDP framework gives better results in most cases, like accuracy: 95.26%, AUC: 91.15%, sensitivity: 96.81%, specificity: 97.72% and precision: 90.72%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), an enlarged Internet-based network, is a key element of the next information technology revolution. With the evolution of numerous IoT based smart applications like smart city, healthcare, huge amount of heterogenous data called big data with varying volume, velocity and variety is getting space on various storage systems. Data redundancy is a serious problem that wastes a lot of storage capacity and network bandwidth with less data security in setups that blend cloud integrated IoT data storage. Data deduplication/redundancy elimination strategies can effectively decrease and control this issue by removing duplicate data in cloud-integrated IoT storage systems. Security and privacy of data is another major concern. To maximise the storage effectively and extremely securely, with maintained data integrity, confidentiality, minimal storage cost and increased storage use, data deduplication (DD) over encrypted data is also a key problem in cloud integrated IoT storage and computing environment. With the dynamic nature of big data, the majority of current data deduplication techniques which primarily revolve around the static scenes like the backup and archive systems, are inappropriate to be applicable for real time scenarios in IoT environment. To overcome the aforementioned issues this survey presents an analysis of literature on conventional deduplication techniques. It is highlighting the need of deduplication in IoT oriented big data, its parameters and properties of effectiveness, with taxonomy for secure and conventional deduplication and scope of implementing it with new technologies; Blockchain. Further, it elaborates on issues and challenges along with future scope of deduplication schemes in various application domains. [ABSTRACT FROM AUTHOR]

Pollution and climate change pose significant threats to aquatic ecosystems, with adverse impacts on aquatic animals, including fish. Climate change increases the toxicity of metal in aquatic ecosystems. To understand the severity of metal pollution and climate change, an experiment was conducted to delineate the mitigation potential of selenium (Se) and selenium nanoparticles (Se-NPs) against lead (Pb) and high temperature stress in Pangasianodon hypophthalmus. For the experiment, five isonitrogenous and isocaloric diets were prepared, varying in selenium supplementation as Se at 0, 1, and 2 mg kg−1 diet, and Se-NPs at 1 and 2 mg kg−1 diet. The fish in stressor groups were exposed to Pb (1/20th of LC50 concentration, 4 ppm) and high temperature (34 °C) throughout the experiment. The results demonstrated that dietary supplementation of Se at 1 and 2 mg kg−1 diet, as well as Se-NPs at 1 mg kg−1 diet, significantly reduced (p < 0.01) the levels of lactate dehydrogenase and malate dehydrogenase in both liver and muscle tissues. Additionally, the levels of alanine aminotransferase and aspartate aminotransferase in both gill and liver tissues were significantly decreased (p < 0.01) with the inclusion of Se and Se-NPs in the diets. Furthermore, the enzymes glucose-6-phosphate dehydrogenase in gill and liver tissues, fructose 1,6-bisphosphatase in liver and muscle tissues, and acid phosphatase in liver tissue were remarkably reduced (p < 0.01) due to the supplementation of Se and Se-NPs. Moreover, dietary supplementation of Se and Se-NPs significantly enhanced (p < 0.01) the activity of pyruvate kinase, glucokinase, hexokinase, alkaline phosphatase, ATPase, protease, amylase, lipase, and RNA/DNA ratio in the fish. Histopathological examination of gill and liver tissues also indicated that Se and Se-NPs protected against structural damage caused by lead and high-temperature stress. Moreover, the study examined the bioaccumulation of selenium and lead in muscle, water, and diets. The aim of the study revealed that Se and Se-NPs effectively protected the fish from lead toxicity and high-temperature stress, while also improving the function of cellular metabolic enzymes in P. hypophthalmus. [ABSTRACT FROM AUTHOR]

Public highways are, in reality, the cornerstone of the country's transportation system. Accidents are unavoidable with this mode of transportation. Collisions involving resting livestock on national highways occur in most countries around the world. It endangers both the drivers and the animals. This paper proposes a method for mitigating the risk of accidents caused by deceased animals, notably cattle that are generating traffic and congestion on national highways and may constitute a safety risk. We have proposed an Internet of Things (IoT) fog-based framework for reclining livestock identification techniques for roadways, data are collected using the IoT-enabled video recording surveillance cameras. We use feature extraction, characteristic expression, assessment criteria, and an unrestricted approach for detecting deceased livestock (such as cows or buffalos), as well as recommendations on whether their placement is harmful to highway traffic. In this study, you only look once (YOLO) image recognition algorithm is implemented for reclining cattle on roadways using the fog layer for training and evaluating datasets. The performance parameters of the proposed framework, such as accuracy, recall, precision, mean average precision (mAP), and interference time, have been measured, and a comparison with existing state-of-the-art techniques has been presented. The obtained findings indicate that the suggested framework surpasses the present approaches, with a higher accuracy of 98% and an interference time of 4.68 ms. Artificially intelligent surveillance system can spot reclined livestock utilizing surveillance videos on roadways. This will ensure passenger safety as well as the safety of roadside cattle. [ABSTRACT FROM AUTHOR]

Despite the effectiveness of COVID-19 vaccination in reducing the severity of the disease, the demand for booster is increasing in vulnerable populations like elderly and immunocompromised individuals especially with each new wave of COVID-19 in different countries. There is limited data on the sustained immunity against COVID-19 in patients with liver cirrhosis. The study was aimed to compare the T cell and humoral immune response after 1 year of ChAdOx1nCoV-19 Vaccine in patients with liver cirrhosis and healthy health care workers (HCW). This was a prospective observational study including 36 HCW, 19 liver cirrhosis patients and 10 unvaccinated individuals. Anti-SARS-CoV-2S antibody, neutralizing antibody and memory T cell subsets were evaluated by ELISA and flow cytometry, respectively, in all three groups after 1 year of initial vaccination. Compared to HCW and unvaccinated individuals, liver cirrhosis patients had significantly depleted T cells, although CD4:CD8 + T cell ratio was normal. Both cirrhotic patients and HCW developed memory T cell subset [effector memory RA (P = 0.141, P < 0.001), effector memory (P < 0.001, P < 0.001), central memory (P < 0.001, P < 0.01), stem cell memory (P = 0.009, P = 0.08) and naïve (P < 0.001, P = 0.02)] compared to unvaccinated unexposed individuals of CD4 + T and CD8 + T, respectively. However, among HCW and cirrhotic group no difference was noted on central memory and stem cell memory cells on T cells. Patients with liver cirrhosis developed comparable memory T cells after vaccination which can evoke sustainable immune response on reinfection. Therefore, additional vaccine doses may not be necessary for cirrhosis patients. [ABSTRACT FROM AUTHOR]

It is an urgent needs to address climate change and pollution in aquatic systems using suitable mitigation measures to avoid the aquatic animals' extinction. The vulnerability and extinction of the aquatic animals in the current scenario must be addressed to enhance safe fish food production. Taking into consideration of such issues in fisheries and aquaculture, an experiment was designed to mitigate high temperature (T) and low pH stress, as well as arsenic (As) pollution in fish using copper (Cu) containing diets. In the present investigation, the Cu-containing diets graded with 0, 4, 8, and 12 mg kg-1 were prepared and fed to Pangasianodon hypophthalmus reared under As, low pH, and high-temperature stress. The gene expression was highly affected in terms of the primary, secondary, and tertiary stress response, whereas supplementation of Cu-containing diet mitigates the stress response. Oxidative stress genes such as catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) were significantly upregulated by stressors (As, As + T, and As + pH + T). Whereas, heat shock protein (HSP 70), inducible nitric oxide synthase (iNOS), metallothionine (MT), caspase 3a (Cas 3a), and cytochrome P450 (CYP 450) were highly upregulated by stressors, while dietary Cu at 8 mg kg-1 diet significantly downregulated these gene expressions. Indeed, the immunity-related genes viz. TNFα, Ig, TLR, and immune-related attributes viz. albumin, globulin, total protein, A:G ratio, blood glucose, NBT, and myeloperoxidase (MPO) were also improved with Cu-containing diets. Cu containing diets substantially improved neurotransmitter enzyme (AChE) and vitamin C (Vit C). DNA damage was also reduced with supplementation of Cu at 8 mg kg-1 diet. The growth index viz. final body weight gain (%), specific growth rate, protein efficiency ratio, food conversion ratio, relative feed intake, and daily growth index were noticeably enhanced by Cu diets (4 and 8 mg kg-1 diet). The growth-related genes expressions viz. growth hormone (GH), growth hormone regulator 1 (Ghr1), growth hormone regulator β (Ghrβ,) myostatin (MYST), and somatostatin (SMT) supported the growth enhancement with Cu at 8 mg kg-1 diet. The bioaccumulation of As was reduced with Cu-containing diets. The fish were infected with Aeromonas hydrophila at the end of the 105 days experimental trial. Cu at 8 mg kg-1 diet improved immunity, reduced the cumulative mortality, and enhanced the relative percentage survival of the fish. The results revealed that the innovative Cu diets could reduce the extinction of the fish against climate change and pollution era and produce the safest production that is safe to humans for consumption. [ABSTRACT FROM AUTHOR]

The ongoing challenges of climate change and pollution are major factors disturbing ecosystems, including aquatic systems. They also have an impact on gene regulation and biochemical changes in aquatic animals, including fish. Understanding the mechanisms of gene regulation and biochemical changes due to climate change and pollution in aquatic animals is a challenging task. However, with this backdrop, the present investigation was conducted to explore the effects of arsenic (As) and ammonia (NH3) toxicity and high-temperature (T) stress on gene regulation and biochemical profiles, mitigated by dietary manganese (Mn) in Pangasianodon hypophthalmus. The fish were exposed to different combinations of As, NH3, and T, and fed with dietary Mn at 4, 8, and 12 mg kg−1 to evaluate the gene expression of immunity, antioxidative status, cytokine, and NfKB signaling pathway genes. HSP 70, cytochrome P450 (CYP 450), metallothionein (MT), DNA damage-inducible protein (DDIP), caspase (CAS), tumor necrosis factor (TNFα), toll-like receptor (TLR), interleukin (IL), inducible nitric oxide synthase (iNOS), catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) were noticeably highly upregulated by As + NH3 + T stress, whereas Mn diet at 8 mg kg−1 downregulated these genes. Further, total immunoglobulin (Ig), myostatin (MYST), somatostatin (SMT), growth hormone (GH), growth hormone regulator 1 and β, insulin-like growth factors (IGF1X1 and IGF1X2) were significantly upregulated by Mn diets. The biochemical profiles were highly affected by stressors (As + NH3 + T). The bioaccumulation of arsenic in different tissues was also notably reduced by Mn diets. Furthermore, the infectivity of the fish was reduced, and survival against pathogenic bacteria was enhanced by Mn diet at 8 mg kg−1. The results of the present investigation revealed that dietary Mn at 8 mg kg−1 controls gene regulation against multiple stressors (As, NH3, As + NH3, NH3 + T, As + NH3 + T) in fish. [ABSTRACT FROM AUTHOR]

Due to changing lifestyles, human physical exercise has dropped rapidly which leads to several health-related problems. It is not easy to detect heart-related diseases, but with the emergence and advancement in technologies like Edge, Fog computing, Machine learning, Cloud computing, and the Internet of Things (IoT), it's a cinch to track heart diseases. Cloud computing provides the resources for computation as well as online storage over the internet, but it does not support latency-sensitive and real-time applications. To overcome this bottleneck, a new emerging technology named Edge Computing is used to bring the computation resource to local nodes for the services and reduces the latency as compared to the cloud. Current Edge computing-based models failed to achieve the output of real-time applications along with high accuracy and low latency simultaneously. Hence, we have proposed an autonomic Edge-assisted Cloud-IoT framework for smart healthcare that used a Random Forest and Logistic Regression Grid (RF-LRG) approach at edge nodes for analysis of heart disease and improves the various influential parameters such as accuracy (3.88%, 7.66% and 14.18%), precision (3.7%, 9%, and 16.6%), F1 Score (5%, 7.7%, and 16.9%.), recall (3.7%, 10.5%, and 15.06%.) compared with LR, RF and KNN algorithm. The simulation results ensured that the proposed framework using the RF-LRG algorithm predicted and diagnosed heart diseases with more accuracy and reduced the latency and energy consumption significantly when compared between the cloud and edge paradigms. [ABSTRACT FROM AUTHOR]

Aquatic animals are prone to extinction due to metal pollution and global climate change. Even though the fish and their products are also unsafe for human consumption, their exports have been rejected due to inorganic and organic contaminants. Nickel (Ni) is a metal that induces toxicity and accumulates in the aquatic ecosystem, posing health threats to humans, animals, and fish. In light of the above, our present investigation aimed to determine the median lethal concentration (96 h-LC50) of nickel alone and concurrent with high temperature (34 °C) (Ni + T) using static non-renewable bioassay toxicity test in Pangasianodon hypophthalmus. The groups treated under exposure to Ni reared under control condition (25–28.9 °C) and Ni + T exposure group reread under 34 °C. In this study, chose the definitive dose of Ni and Ni + T as 17, 18, 19, and 20 mg L−1 after the range finding test. The median lethal concentration of Ni and Ni + T was determined as 19.38 and 18.75 mg L−1, respectively at 96 h. Oxidative stress viz. catalase (CAT), superoxide dismutase (SOD), glutathione-s-transferase (GST), and glutathione peroxidase (GPx) in the liver, gill, and kidney were noticeably elevated with Ni and Ni + T during 96 h. Whereas, the CAT, GPx, and SOD gene expressions were significantly upregulated with Ni and Ni + T. Trilox equivalent anti-oxidant capacity (TEAC), cupric reducing anti-oxidant capacity (CUPRIC), ferric reducing ability of plasma (FRAP), ethoxy resorufin-O-deethylase (EROD), and acetylcholine esterase (AChE) were reduced due to exposure to Ni and Ni + T. Cellular metabolic stress and lipid peroxidation were highly affected due to Ni and Ni + T exposure. The immunological status, as indicated by total protein, albumin, globulin, A:G ratio, and nitro blue tetrazolium chloride (NBT), was severely affected by the toxicity of Ni and Ni + T. Moreover, the gene expression of interleukin (IL), tumor necrosis factor (TNFα), toll-like receptor (TLR), and total immunoglobulin (Ig) was remarkably downregulated following exposure to Ni and Ni + T. HSP 70, iNOS expression, ATPase, Na + /K + -ATPase, cortisol, and blood glucose was significantly elevated with Ni and Ni + T in P. hypophthalmus. The bioaccumulation of Ni in fish tissues and experimental water was determined. The kidney and liver tissues were highly accumulated with Ni, whereas DNA damage was reported in gill tissue. Interestingly, depuration study revealed that at the 28th day, the Ni bioaccumulation was below the maximum residue limit (MRL) level. Therefore, the present study revealed that Ni and Ni + T led to dysfunctional gene and metabolic regulation affecting physiology and genotoxicity. The bioaccumulation and depuration results also indicate higher residual occurrence of Ni in water and aquatic organisms for longer periods. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.)–wheat (Triticum aestivum L.) cropping system in north-western Indo-Gangetic Plains performed a crucial role in the national food security. However, the widespread and intensive cultivation of this system has led to serious problems such as declining groundwater table (~1 meter year−1) with sharp increase in number of districts under over-exploitation category, residue burning, higher greenhouse gases emission and herbicide resistance in weeds, causing stagnant crop productivity and lesser profitability. In this review article, an attempt has been made to discuss the major issues pertaining to intensive rice–wheat cultivation amidst climate vagaries and futuristic approach to address these challenges. Different tillage- and crop-specific recommendations such as adoption of direct seeded rice, diversification with lesser resource guzzling crops such as maize (Zea mays L.) at least on the periodic manner especially in light-medium soils, inclusion of summer legumes and alternative tillage systems (permanent beds and zero tillage with residue retention) have been suggested to address these issues. However, crop performance under these techniques has been found to be location, soil and cultivar specific. The absence of aerobic tailored genotypes and weeds have been identified as the major constraints in adoption of direct seeded rice. The integrated strategies of conservation tillage, crop breeding program and resource conserving region- and soil-specific agronomic measures with crop diversification would be helpful in tackling the sustainability issues. It requires future efforts on developing crop genotypes suited to conservation tillage, effective weed control strategies and trainings and demonstrations to farmers to switch from conventional rice–wheat system to alternative cropping systems. [ABSTRACT FROM AUTHOR]

Depression is a mental illness that negatively affects a person's thinking, action, and feeling. Thus the rate of depression is identified by analysing Electroencephalogram (EEG) signals. Because of noise, the problem of classifying depression rate has some issues, such as low accuracy and required high training time. In this research work, a novel Bat-based U-NET Signal Analysis (BUSA) architecture is developed to estimate the patient's depression rate with an EEG dataset. This technique involves pre-processing, feature selection, feature extraction, and classification. After the data training, the pre-processing function was activated to neglect the noise in the brain signal. Hereafter, the noiseless Signal is used for the further process. Here, the bat algorithm mimics the behaviour of the bat's frequency and loudness, increasing the accuracy of prediction and classification. This fitness function is upgraded in the U-NET classification phase. Moreover, the brain signal's feature selection and depression rate were classified using the bat fitness that has helped to gain the desired output. Finally, the performance metrics of the proposed BUSA technique are compared with other existing methods regarding the accuracy, AUC, precision, recall, and power. The proposed BUSA framework attained a high accuracy rate of about 99.64%, a maximum precision level of approximately 99.98%, a high recall rate of approximately 99.95%, and a high AUC of approximately 99.2%. The developed framework has attained better results in classifying depression rates. [ABSTRACT FROM AUTHOR]

More than six and half million people have died as a result of the COVID-19 pandemic till Dec 2022. Vaccination is the most effective means to prevent mortality and infection attributed to COVID-19. Identifying public attitudes and perceptions on COVID-19 vaccination is essential to strengthening the vaccination programmes. This study aims to identify attitudes and perceptions of twitter users towards COVID-19 vaccinations in four different countries. A sentiment analysis of 663,377 tweets from October 2020 to September 2022 from four different countries (i.e., India, South Africa, UK, and Australia) was conducted. Text mining using roBERTA (Robustly Optimized Bert Pretraining approach) python library was used to identify the polarity of people's attitude as "negative", "positive" or "neutral" based on tweets. A sample of 2000 tweets (500 from each country) were thematically analysed to explore the people's perception concerning COVID-19 vaccines across the countries. The attitudes towards COVID-19 vaccines varied by countries. Negative attitudes were observed to be highest in India (58.48%), followed by United Kingdom (33.22%), Australia (31.42%) and South Africa (28.88%). Positive attitudes towards vaccines were highest in the United Kingdom (21.09%). The qualitative analysis yielded eight themes namely (i) vaccine shortages, (ii) vaccine side-effects, (iii) distrust on COVID-19 vaccines, (iv) voices for vaccine equity, (v) awareness about vaccines, (vi) myth busters, (vii) vaccines work and (viii) vaccines are safe. The twitter discourse reflected the evolving situation of COVID-19 pandemic and vaccination strategies, lacunae and positives in the respective countries studied. [ABSTRACT FROM AUTHOR]

The Brassicaceae family includes many economically important species that are grown worldwide and used mainly for vegetable oils. Sclerotinia stem and leaf rot, caused by Sclerotinia sclerotiorum, is a devastating disease that affects many Brassicaceae species and threatens their long-term productivity and profitability. There are currently no genetic sources known to have a high level of resistance to this pathogen. In this study, 338 accessions from 13 cultivated and wild Brassicaceae species, including 266 accessions from Brassica juncea, were evaluated for Sclerotinia rot resistance and stem physical strength attributes. Large variation of resistance was found in Brassicaceae, with maximum differences of 23.08- and 8.25-folds in stem and leaf resistance, respectively. B. juncea IC 589660, D. tenuisiliqua and S. alba SA 1 showed high level of resistance to stem rot while B. carinata HC 0214, B. juncea IC 589660, B. juncea IC 491160, B. juncea IC 73231, and B. juncea IC 491463 exhibited high level of resistance to leaf rot. These accessions showed significantly lower disease scores than the resistant control. Among all the evaluated accession, B. juncea IC 589660 is the only genotype exhibited high resistance to both stem and leaf rot and could prove useful for S. sclerotiorum resistance breeding programs. The lack of association between leaf lesion size and stem lesion length revealed separate genetic regulation for stem vs leaf resistance. While, significant negative correlation detected between stem lesion length, and stem physical strength attributing traits indicated that stem physical strength-mediated resistance (SPSMR) factors were responsible for Sclerotinia stem rot resistance in Brassicaceae. Hard and woody stem is a prominent resistance factor that could be targeted in durable resistance breeding programs against stem rot. These findings pave the door for further research into the molecular basis of SPSMR's response to Sclerotinia stem rot in Brassica oilseeds and other hosts. Sclerotinia stem and leaf rot resistant accessions identified in this study will be of considerable benefit not only in increasing our understanding of resistance mechanisms across different Brassicaceae species, but also in generating resistant cultivars against Sclerotinia sclerotiorum. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches' future directions and existing drawbacks are discussed in detail. [ABSTRACT FROM AUTHOR]

Arsenic pollution, water temperature, and pH are the major concern for aquaculture. Moreover, the aim of the present investigation was to delineate the role of nano-copper (Cu-NPs) in the mitigation of arsenic toxicity, high temperature (34 °C) and low pH (6.5) stress on Pangasianodon hypophthalmus. Four isonitrogenous and isocaloric experimental diets of Cu-NPs at 0, 1.0, 1.5 and 2.0 mg kg−1 were formulated and prepared. Arsenic pollution, low pH and high temperature stress significantly reduced the anti-oxidative status (super oxide dismutase, catalase, glutathione peroxidase and glutathione-s-transferase), lipid peroxidation, total anti-oxidative capacity and lipid profiling (cholesterol, total lipid, phospholipid, very low-density lipoprotein and triglyceride). Further, the supplementation of Cu-NPs at 1.5 and 1.0 mg kg−1 diets noticeably improve the anti-oxidant status and capacity. The stressors groups (As + pH + T, As + T and As) significantly reduced fish immunity viz. albumin, globulin, total protein, albumin globulin ratio (A:G ratio), myeloperoxidase, respiratory burst activities, tumor necrosis factor, total immunoglobulin, and interleukin. Whereas supplementation of Cu-NPs at 1.5 and 1.0 mg kg−1 diets improved the immunity of the fish reared under multiple stresses (As + pH + T). Tail DNA %, DNA damage-inducible protein (DDIP) and inducible nitric oxide (iNOS) synthase gene expression were significantly enhanced with exposure to arsenic, low pH and high temperature but supplementation of Cu-NPs protects the tissues against DNA damage and improved the gene expression of iNOS and DDIP. Cu-NPs at 1.5 and 1.0 mg kg−1 diets significantly enhanced the body weight gain %, protein efficiency ratio, specific growth rate, daily growth index, relative feed intake and reduced the feed conversion ratio. Whereas, the growth-related gene expression such as myostatin (MYST), somatostatin (SMT) was downregulated by Cu supplementation and upregulated the gene expression of growth hormone regulator 1 and β (GHR1 and GHR β) and growth hormone (GH) gene in fish. Dietary Cu-NPs supplementation protects the fish against bacterial infection and enhances arsenic detoxification in different tissues. The present investigation revealed that supplementation of Cu-NPs at 1.5 and 1.0 mg kg−1 diet has the potential to mitigate multiple stress (As + pH + T) in fish. [ABSTRACT FROM AUTHOR]

Ammonia and arsenic pollution, along with the impact of climate change, represent critical factors influencing both the quantity and quality of aquaculture production. Recent developments have underscored the significance of these issues, as they not only disrupt aquatic ecosystems but also have far reaching consequences for human health. To addressed above challenges, an experiment was conducted to delineate the potential of manganese nanoparticles (Mn-NPs) to mitigate arsenic and ammonia pollution as well as high temperature stress in Pangasianodon hypophthalmus. The fish were exposed to different combination of arsenic and ammonia pollution as well as high temperature stress, while simultaneously incorporating diets enriched with Mn-NPs. The inclusion of Mn-NPs at 3 mg kg−1 in the diet led to a noteworthy downregulation of cortisol and HSP 70 gene expression, indicating their potential in mitigating stress responses. Furthermore, immune related gene expressions were markedly altered in response to the stressors but demonstrated improvement with the Mn-NPs diet. Interestingly, the expression of inducible nitric oxide synthase (iNOS), caspase (CAS), metallothionine (MT) and cytochrome P450 (CYP450) genes expression were prominently upregulated, signifying a stress response. Whereas, Mn-NPs at 3 mg kg−1 diet was significantly downregulated theses gene expression and reduces the stress. In addition to stress-related genes, we evaluated the growth-related gene expressions such as growth hormone (GH), growth hormone regulator 1 (GHR1 and GHRβ), Insulin like growth factor (IGF1 and IGF2) were significantly upregulated whereas, myostatin and somatostatin were downregulated upon the supplementation of dietary Mn-NPs with or without stressors in fish. The gene expression of DNA damage inducible protein and DNA damage in response to head DNA % and tail DNA % was protected by Mn-NPs diets. Furthermore, Mn-NPs demonstrated a capacity to enhance the detoxification of arsenic in different fish tissues, resulting in reduced bioaccumulation of arsenic in muscle and other tissues. This finding highlights Mn-NPs as a potential solution for addressing bioaccumulation associated risks. Our study aimed to comprehensively examined the role of dietary Mn-NPs in mitigating the multiple stressors using gene regulation mechanisms, with enhancing the productive performance of P. hypophthalmus. [ABSTRACT FROM AUTHOR]

Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our learned model using the mean absolute error margin (MAE-margin), a modified version of mean absolute error that handles data with censored outcomes. We show that our tool can provide helpful information for patients and clinicians in planning future treatment. [ABSTRACT FROM AUTHOR]

Nowadays, high speed and high power density Brushless Direct Current (BLDC) motors have been widely utilized in the industrial area. Moreover, the design of motor simulation strategies is used in the drive system, which controls the complicated problems in the BLDC motors. However, speed regulation is a vital challenge since it affects the controller performance; the Proportional-Integral-Derivative (PID) controller is used in mechanical concerns. Therefore, this study introduces the novel Wavelet-based Fuzzy Adaptive Hybrid Bat-Vulture PID (WFA-HBVPID) controller to control the BLDC motor acceleration. Also, the developed WFA-HBVPID controller organizes the loads in the BLDC motor while verifying the gain scheduling conditions. Furthermore, this proposed PID controller is implemented using MATLAB/Simulink. Here, the performance of the motor is assessed in two ways, i.e., with hybrid optimization and without hybrid optimization. In addition, the efficiency of the developed controller has been checked over the time domain specifications like settling time, rise time, peak overshoot, and gain. To calculate the presented controller efficiency, the performances of the controller were compared with existing techniques. From the comparison of the outcomes, it is found that the proposed controller has less computation time and error rate. [ABSTRACT FROM AUTHOR]

In this work, we have fabricated hybrid heterojunction solar cells with structure ITO/ZnO/P3HT:PCBM:CTSe NCs (optimized weight percentage 10 wt%)/MoO3/Ag (device SM10). A thin layer of molybdenum oxide MoO3 prepared by peroxomolybdic acid organosol as the precursor was used as hole transport layers (HTLs). The performance of the device was compared with ITO/ZnO/P3HT:PCBM:CTSe NCs/Ag(device S10). The important parameters such as global charge carrier mobility (µ), effective charge carriers lifetime (τn) and charge diffusion time (τd) have been extracted from electrochemical impedance spectroscopy (EIS) performed at the bias of 0.1 V in the dark. The Nyquist plot obtained from EIS has demonstrated effective carriers lifetime, global mobility and charge diffusion time for a device SM10 as 6.46 ms, 12.6 × 10−4 cm2 V−1 S−1 and 5.2 µs and for device S10 as 5.65 ms, 9.58 × 10−4 cm2 V−1 S−1 and 6.8 µs, respectively. [ABSTRACT FROM AUTHOR]

The usage of intelligent IoT devices is exponentially rising, and so the possibility of attacks in the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary investigation of IoT systems but have not upgraded the analysis of potential attacks in IoT. This paper aims to advance deep learning methods to create upgraded security strategies for IoT frameworks quickly. The study of the IoT security threats identified with inalienable or recently presented risks is done. Also, this paper does a quick examination of different possible attack surfaces for the IoT framework, and the potential risks identified with each character. The systematic survey of deep learning methods for IoT security and the existence of the chances, focal points, and weaknesses of every strategy opens the door significant for future research. [ABSTRACT FROM AUTHOR]

Central venous access devices (chemoport) are nowadays most commonly used for delivering chemotherapy drugs. As the chemoport insertion procedures are increasing in number, there are documented complications which are common, life threatening if not immediately treated, and rare. The common complications are pneumothorax, infection, thrombosis, hemothorax, drug extravasation, and malfunction of chemoport. A rare and dangerous complication is fracture embolization of catheter. Another rare complication is flipping of chemoport. In this article, we would like to present images of two patients with one image showing catheter embolized to right ventricle and another patient with port flipping and their management. [ABSTRACT FROM AUTHOR]

Despite the intense worldwide efforts towards the identification of potential anti-CoV therapeutics, no antiviral drugs have yet been discovered. Numerous vaccines are now approved for use, but they all serve as preventative measures. To effectively treat viral infections, it is crucial to find new antiviral drugs that are derived from natural sources. Various compounds with potential activity against 3 chymotrypsin-like protease (3CLpro) were reported and some are validated by bioassay studies. Therefore, we performed the computational screening of phytoconstituents of Codonopsis lanceolata to search for potential antiviral hit candidates. The curated compounds of the plant C. lanceolata were collected and downloaded from the literature. The binding affinity of the curated datasets was predicted for the target 3CLpro. Stigmasterol exhibits the highest docking score for the 3CLpro target. In addition, molecular dynamics (MD) simulations were conducted for the validation of docking results using root mean square deviation and root mean square fluctuation plots. The MD results indicated that the docked complex was stable and retained hydrogen bonding and non-bonding interactions. Furthermore, the calculation of pharmacokinetic parameters and Lipinski's rule of five suggest that C. lanceolata has the potential for drug-likeness. In order to develop new medicines for this debilitating disease, we will focus on the primary virus-based and host-based targets that can direct medicinal chemists to identify novel treatments to produce new drugs for it. [ABSTRACT FROM AUTHOR]

The microscopic mechanism of heavy band formation, relevant for unconventional superconductivity in CeCoIn5 and other Ce-based heavy fermion materials, depends strongly on the efficiency with which f electrons are delocalized from the rare earth sites and participate in a Kondo lattice. Replacing Ce3+ (4f1, J = 5/2) with Sm3+ (4f5, J = 5/2), we show that a combination of the crystal electric field and on-site Coulomb repulsion causes SmCoIn5 to exhibit a Γ7 ground state similar to CeCoIn5 with multiple f electrons. We show that with this single-ion ground state, SmCoIn5 exhibits a temperature-induced valence crossover consistent with a Kondo scenario, leading to increased delocalization of f holes below a temperature scale set by the crystal field, Tv ≈ 60 K. Our result provides evidence that in the case of many f electrons, the crystal field remains the dominant tuning knob in controlling the efficiency of delocalization near a heavy fermion quantum critical point, and additionally clarifies that charge fluctuations play a general role in the ground state of "115" materials. In heavy fermion metals, exotic states such as superconductivity depend to a large extent on whether unpaired electrons can become delocalized from the magnetic sites. The authors demonstrate that the primary driving force behind delocalization is the local environment of the rare earth ions and the strength of the resulting charge fluctuations. [ABSTRACT FROM AUTHOR]

Pixel-value ordering (PVO) and prediction-error expansion (PEE) are the two most popular strategies of reversible data hiding (RDH) as PVO provides high-fidelity stego-images with decent embedding capacity (EC) and PEE provides high EC with limited distortion. Further, pairwise embedding scheme introduced by Ou et al. again boosts the EC and reduces distortion of both the strategies. However, there has been a dearth of RDH schemes which can optimally utilize both the pairwise PVO and pairwise PEE strategies to provide a least trade-off between EC and visual quality. In this paper, we propound an adaptive RDH (ARDH) scheme which optimally selects the embedding strategy based on image block category. The proposed scheme reads the image in the block-wise manner using a sliding window of 4 × 4 size to get the image block of same size, then divides the block into inner and outer sub-block. The outer sub-block is considered as a reference block for the inner sub-block to determine statistical properties of the inner sub-block using standard deviation. An enhanced pairwise PEE is adopted for embedding when the standard deviation of outer sub-block's pixels is smaller than a first user-defined threshold. In case the standard deviation is greater than the first threshold but lower than a second user-defined threshold, then pairwise PVO is adopted. Otherwise, the sub-block is skipped without embedding the secret data. As a result, the ARDH scheme utilizes both the PEE and PVO strategies in optimum manner, which in turn provides higher EC and image quality than the most of the existing RDH schemes as validated by experimental results. [ABSTRACT FROM AUTHOR]

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC 50 values in the low micromolar range: 2.95 ± 0.0017 μ M and 3.41±0.0015 μ M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets. [ABSTRACT FROM AUTHOR]