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This paper presents a study on residual stress measurement in wire-arc additively manufactured (WAAM) titanium samples using the non-destructive method of phased array ultrasonics. The contour method (CM) was used for the verification of the phased array ultrasonic results. This allowed for a comparison of measurement methods to understand the effects on the distribution of residual stress (RS) within Ti-6Al-4V samples and the effectiveness of measurement of residual stress using phased array ultrasonics. From the results of the experiments, the phased array ultrasonic data were found to be in good agreement with the CM results and displayed similar residual stress distributions in the samples. The results of the individual elements of the phased array were also compared and an improvement in accuracy was found. From per-element results, anomalies were found and could be mitigated with the ability to average the results by using phased array ultrasonics. Therefore, based on these results, there is a strong case for the benefits of using phased array ultrasonics as a method of residual stress measurement for WAAM Ti-6Al-4V components over other existing residual stress measurement techniques. [ABSTRACT FROM AUTHOR]

Simple Summary: Gastric cancer is a major worldwide health concern, underscoring the importance of early detection to enhance patient outcomes. Traditional histological analysis, while considered the gold standard, is labour intensive and manual. Deep learning (DL) is a potential approach, but existing models fail to extract all of the visual data required for successful categorization. This work overcomes these constraints by using ensemble models that mix different deep-learning architectures to improve classification performance for stomach cancer diagnosis. Using the Gastric Histopathology Sub-Size Images Database, the ensemble models obtained an average accuracy of more than 99% at various resolutions. ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet, with the ensemble model continuously delivering higher accuracy. These findings show that ensemble models may accurately detect important characteristics from smaller picture patches, allowing pathologists to diagnose stomach cancer early and increasing patient survival rates. Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates. [ABSTRACT FROM AUTHOR]

Autoimmunity refers to an organism's immune response against its own healthy cells, tissues, or components, potentially leading to irreversible damage to vital organs. Central and peripheral tolerance mechanisms play crucial roles in preventing autoimmunity by eliminating self-reactive T and B cells. The disruption of immunological tolerance, characterized by the failure of these mechanisms, results in the aberrant activation of autoreactive lymphocytes that target self-tissues, culminating in the pathogenesis of autoimmune disorders. Genetic predispositions, environmental exposures, and immunoregulatory disturbances synergistically contribute to the susceptibility and initiation of autoimmune pathologies. Within the realm of immune therapies for autoimmune diseases, cytokine therapies have emerged as a specialized strategy, targeting cytokine-mediated regulatory pathways to rectify immunological imbalances. Proinflammatory cytokines are key players in inducing and propagating autoimmune inflammation, highlighting the potential of cytokine therapies in managing autoimmune conditions. This review discusses the etiology of autoimmune diseases, current therapeutic approaches, and prospects for future drug design. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care. [ABSTRACT FROM AUTHOR]

A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the quality of 3D reconstruction from WCE images. In this paper, we employ a single-image-based 3D reconstruction method on an artificial colon captured with an endoscope that behaves like WCE. The shape from shading (SFS) algorithm can reconstruct the 3D shape using a single image. Therefore, it has been employed to reconstruct the 3D shapes of the colon images. The camera of the endoscope has also been subjected to comprehensive geometric and radiometric calibration. Experiments are conducted on well-defined primitive objects to assess the method's robustness and accuracy. This evaluation involves comparing the reconstructed 3D shapes of primitives with ground truth data, quantified through measurements of root-mean-square error and maximum error. Afterward, the same methodology is applied to recover the geometry of the colon. The results demonstrate that our approach is capable of reconstructing the geometry of the colon captured with a camera with an unknown imaging pipeline and significant noise in the images. The same procedure is applied on WCE images for the purpose of 3D reconstruction. Preliminary results are subsequently generated to illustrate the applicability of our method for reconstructing 3D models from WCE images. [ABSTRACT FROM AUTHOR]

Green silver nanoparticles (AgNPs) possess tremendous promise for diverse applications due to their versatile characteristics. Coriander and other plant extracts have become popular for greenly synthesizing AgNPs as an economical, biocompatible, cost-effective, and environmentally beneficial alternative to chemical processes. In this study, we synthesized AgNPs from coriander leaves and evaluated their antibacterial, anti-inflammatory, antioxidant, and wound-healing acceleration properties in comparison to chemically synthesized AgNPs. The zeta potentials of AgNPs extracted from green and chemical processes were −32.4 mV and −23.4 mV, respectively. TEM images showed a cuboidal shape of green and chemical AgNPs with a diameter of approximately 100 nm. The FTIR spectra of green AgNPs showed an extreme absorption peak at 3401 cm−1, which signifies O-H stretching vibrations, typically linked to hydroxyl groups. In vitro results elaborated that AgNPs from coriander exerted a stronger effect on anti-Klebsiella pneumoniae (KP) through interrupting cell integrity, generating ROS, depleting ATP, and exhibiting significant antioxidant activity, compared with AgNPs synthesized chemically. In vivo experiments showed that AgNPs from coriander, as opposed to chemically manufactured AgNPs, greatly accelerated the healing of wounds contaminated with Klebsiella pneumoniae bacteria by effectively eliminating the bacteria on the wounds and stimulating skin regeneration and the deposition of dense collagen. In vivo assays further demonstrated that green AgNPs effectively enhanced Klebsiella pneumoniae-infected wound healing by extenuating local inflammatory responses and up-regulating VEGF and CD31 expression. In conclusion, green AgNPs significantly alleviated the inflammation without significantly harming the organism. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer is a significant cause of male cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed a new online tool that combines human expertise and smart AI predictions to grade prostate cancer. Experienced doctors helped us improve the tool by answering questions in a survey and a test called the NASA TLX Usability Test. This helped ensure that the tool meets the needs of intended medical users and proves a valuable addition in clinics. The feedback obtained was positive for three themes that included acceptability, ease of use, and understanding with suggested application feature improvements as feedback for real world hospital use. The task completion process was not very demanding overall. The web application has potential to serve well upon minor improvements based on feedback. Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low–moderate perceived workload, suggesting room for improved explanations and data visualization. [ABSTRACT FROM AUTHOR]

This study evaluates the earthquake-induced movement of mechanically stabilized earth (MSE) walls. A thorough investigation was conducted on an MSE wall model, utilizing a comprehensive finite element (FE) analysis. This research focuses on investigating and designing MSE walls made of reinforcement concrete and hollow precast concrete panels. It also involves comparative studies such as on the vertical pressure of the wall, horizontal pressure of the wall, lateral pressure of the wall, settlement of the wall, settlement of the backfill reinforcement, vertical pressure of the backfill, horizontal pressure of the backfill, lateral pressure of the backfill, vertical settlement of the foundation, and settlements of soil layers across the height of the MSE walls. The FE simulations used a three-dimensional (3D) nonlinear dynamic FE model of full-scale MSE walls. The seismic performance of MSE walls has also been examined in terms of wall height. It was found that the seismic motion significantly impacts the height of the walls. In addition, the validity of the proposed study model was assessed by comparing it to the reinforcement concrete wall and ASSHTO guidelines using finite element (FE) simulation results. Based on the findings, the hollow prefabricated MSE wall was the most practical alternative due to its lower displacement and settlement. The specifics of the modeling approach used in this study and the lessons learned serve as benchmarks for future comparable lines of inquiry and practitioners, especially as the computational power of desktop computers continues to rise. [ABSTRACT FROM AUTHOR]

A thermomechanical model of the friction stir welding (FSW) of high-density polyethylene (HDPE) was developed by incorporating a Coupled Eulerian–Lagrangian (CEL) approach. A Johnson Cook (JC) material model of HDPE was developed through experimentally generated strain-rate- and temperature-dependent stress strain data. Two sets of FSW process parameters with minimum and maximum weld defects were numerically modeled. The numerically calculated temperature distribution, material flow and flash and potential defects were validated and discussed with the experimental results. Tracer particles allowed to visualize the material movement during and after the tool had traversed from the specified region of the workpiece. Both numerical models presented similar maximum temperatures on the upper surface of the workpiece, while the model with high traverse speed and slow rotational speed had narrower shoulder- and heat-affected zones than the slow traverse, high rotational speed model. This contributed to the lack of material flow, hence the development of voids and worm holes in the high traverse speed model. Flash and weld defects were observed in models for both sets of process parameters. However, slow traverse, high rotational speeds exhibited smaller and lesser weld defects than high traverse, slow rotational speeds. The numerical results based on the CEL approach and JC material model were found to be in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

The Trihelix is a plant-specific transcription factor family and has critical roles in plant growth and development and stress resistance. There is less information about Trihelix transcription factor genes and their potential functions in strawberries (Fragaria vesca). In the present study, we performed a detailed bioinformatics analysis of the Trihelix family in strawberries including physicochemical properties, chromosomal location, exon–intron distribution, domain arrangement, and subcellular localization. Thirty Trihelix family members were identified and divided into five subfamilies. The expression of FvTrihelix genes in different tissues/organs, i.e., root, stolon, leaf, flower, and fruit, was measured in strawberries after infection with Colletotrichum. gloeosporioides and foliar applications of salicylic acid (SA) and jasmonic acid (JA). Most of the genes showed differential expression responses following C. gloeosporioides infection and hormone treatments (SA and JA), suggesting critical roles in disease resistance and hormonal signaling pathways. As anticipated, the ectopic expression of FvTrihelix6 in Arabidopsis thaliana increased resistance against Colletotrichum. higginsianum infection. FvTrihelix6 protein was localized in the nucleus. We surmise that FvTrihelix6 enhances resistance against pathogens through the SA and JA signaling pathways. This study provides novel insights into the strawberry Trihelix transcription factor genes and new candidates for disease-resistance breeding of strawberries. [ABSTRACT FROM AUTHOR]

Community forestry is a strategy in which communities are, to some degree, responsible for managing the forests, using a more participatory approach to replace the traditional top-down model. Various forms of policies and governance have been developed to balance goals to ensure the community's socioeconomic resilience and the landscape's biological sustainability. The reinvestment of community forest (CF) income back into forest regeneration is not well documented, and there is a lack of research comparing forest income to the costs associated with forest regeneration. This research examines how changes in timber income and forest-regeneration costs affected CF social and ecological viability. We conducted expert elicitation interviews for CFs (n = 33) under three zones of management in Chitwan, Nepal (Zone 1: buffer zone, Zone 2: forest corridor, and Zone 3: community forest). To examine how CFs differ financially, we asked questions regarding timber income and forest-regeneration expenditures and then posed 22 questions regarding socioeconomic and biological aspects of the CF. Finally, a Kruskal–Wallis rank-sum test was performed to determine whether there were statistically significant differences in perceptions between groups, including zone, timber income (high, medium, low), and forest-regeneration expenditures (high, medium, low). The amount of income from timber had a substantial impact on the communities' biological benefits and financial stability. Lower timber-income areas were thought to be less economically stable, lack the resources to enforce rules and regulations necessary to meet the CF's socioeconomic or biological goals, and place more significant restrictions on the amount of wood members can harvest from the forest. Communities that spent less money on forest regeneration reported poorer levels of forest regeneration, economic sustainability, and community rights. Our research shows that community-forest user groups in the Chitwan district have a significant income and expenditure gap between their forests' biological and socioeconomic advantages and resilience. [ABSTRACT FROM AUTHOR]

Plant chitinases (EC 3.2.1.14) are pathogenesis-related (PR) proteins and are well studied in many plant species. However, little is known about the genomic organization and expression of chitinase genes in strawberries (Fragaria vesca). Here, 23 FvChi genes were identified in the genome of strawberry (F. vesca) and divided into GH18 and GH19 subfamilies based on phylogenetic relationships. A detailed bioinformatics analysis of the FvChi genes was performed, including gene physicochemical properties, chromosomal location, exon–intron distribution, domain arrangement, and subcellular localization. Twenty-two FvChi genes showed upregulation after Colletotrichum gloeosporioides infection. Following the exogenous application of SA, FvChi-3, 4, and 5 showed significant changes in expression. The ectopic expression of FvChi-14 in Arabidopsis thaliana increased resistance to C. higginsianum via controlling the SA and JA signaling pathway genes (AtPR1, AtICS1, AtPDF1.2, and AtLOX3). The FvChi-14 protein location was predicted in the cell wall or extracellular matrix. We speculate that FvChi-14 is involved in disease resistance by regulating the SA and JA signaling pathways. The findings of this study provide a theoretical reference for the functional studies of FvChi genes and new candidates for strawberry stress resistance breeding programs. [ABSTRACT FROM AUTHOR]

Loss of habitat and human disturbance are major factors in the worldwide decline of shorebird populations, including that of the threatened migratory piping plover (Charadrius melodus). From 2013 to 2018, we conducted land-based surveys of the shorebird community every other week during the peak piping plover season (September to March). We assessed the ability of a thin plate spline occupancy model to identify hotspot locations on Whiskey Island, Louisiana, for the piping plover and four additional shorebird species (Wilson's plover (Charadrius wilsonia), snowy plover (Charadrius nivosus), American oystercatcher (Haematopus palliatus), and red knot (Calidris canutus)). By fitting single-species occupancy models with geographic thin plate spline parameters, hotspot priority regions for conserving piping plovers and the multispecies shorebird assemblage were identified on the island. The occupancy environmental covariate, distance to the coastline, was weakly fitting, where the spatially explicit models were heavily dependent on the spatial spline parameter for distribution estimation. Additionally, the detectability parameters for Julian date and tide stage affected model estimations, resulting in seemingly inflated estimates compared to assuming perfect detection. The models predicted species distributions, biodiversity, high-use habitats for conservation, and multispecies conservation areas using a thin-plate spline for spatially explicit estimation without significant landscape variables, demonstrating the applicability of this modeling approach for defining areas on a landscape that are more heavily used by a species or multiple species. [ABSTRACT FROM AUTHOR]

Heat shock proteins (HSPs) play critical roles in regulating different mechanisms under high-temperature conditions. HSPs have been identified and well-studied in different plants. However, there is a lack of information about their genomic organization and roles in medicinal plants and fungi, especially in Wolfi-poria cocos (W. cocos). We identified sixteen heat shock proteins (HSPs) in W. cocos and analyzed in terms of phylogenetic analysis, gene structure, motif distribution patterns, physiochemical properties, and expression comparison in different strains. Based on phylogenetic analysis, HSPs were divided into five subgroups (WcHSP100, WcHSP90, WcHSP70, WcHSP60, and WcsHSP). Subgroups WcHSP100s, WcHSP90s, WcHSP70s, WcHSP60, and WcsHSPs were further divided into 3, 2, 3, 1, and 6 subfamilies, respectively. Moreover, the expression profiling of all HSP genes in five strains of W. cocos under different temperature extremes revealed that expression of most HSPs were induced by high temperature. However, every subfamily showed different expression suggesting distinctive role in heat stress tolerance. WcHSP70-4, WcHSP90-1, and WcHSP100-1 showed the highest response to high temperature stress. Heterologous expression of WcHSP70-4, WcHSP90-1, and WcHSP100-1 genes in Escherichia coli enhanced survival rate of E. coli during heat stress. These findings suggest the role of W. cocos heat shock genes in the high temperature stress tolerance. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the capability of the digital image correlation (DIC) technique for evaluating full-field residual stresses in wire and arc additive manufactured (WAAM) components. Investigations were carried out on WAAM steel parts (wall deposited on a substrate) with two different wall heights: 24 mm and 48 mm. Mild steel solid wire AWS ER70S-6 was used to print WAAM walls on substrates that were rigidly clamped to H-profiles. DIC was used to monitor the bending deformation of WAAM parts during unclamping from the H-profiles, and residual stresses were calculated from the strain field captured during unclamping. Residual stresses determined from the proposed DIC-based method were verified with an analytical model and validated by the results from established residual stress measurement techniques, i.e., the contour method and X-ray diffraction. [ABSTRACT FROM AUTHOR]

Effective forest conservation and management needs to consider ecological factors and the susceptibility of different tree species to anthropogenic activity. Dry deciduous forests in the tropics have been undervalued because of their low timber production compared to temperate and boreal forests. In order to quantify the current significance of Pakistan's tropical dry deciduous forests in relation to ecological variables and anthropogenic threats, a broad phytosociological survey was conducted. In the study area, a total of 140 species of woody plants belonging to 52 families were identified. The cluster analysis depicts the distribution of nine plant communities in five clusters, in which Dodonaea viscosa-Acacia modesta-Dulbergia sissoo community was the most dominant community, showing cosmopolitan distribution in the study area. The results are further authenticated by DCA and CCA analyses, which indicate that altitude, precipitation, and temperature are the most important factors influencing the distribution and composition of tropical dry deciduous forests along the Himalayan foothills. In the study area, an annual fuelwood consumption of 270.38 tons was recorded. It was discovered that high-altitude forests were subject to extensive tree harvesting, overgrazing, browsing, and high fuelwood consumption. In addition, these forests have a low regeneration rate from 4.5 to 4.8 seedlings/quadrat, due to extensive human activities. Remarkable differences in the anthropogenic pressure and disturbances were found in the protected and unprotected forests. Based on our findings, we recommend that: (i) Immediate management intervention and an in-situ conservation strategy must be implemented in areas exposed to high levels of anthropogenic threats. (ii) Unsustainable grazing and fuelwood collection must be managed, and high-threat areas must be immediately prohibited. (iii) The local populace must be made aware of the grave consequences of anthropogenic disturbances, and a collaborative management strategy must be implemented. [ABSTRACT FROM AUTHOR]

In this study, the concept of online food purchasing is explored where consumers are not required to visit markets to purchase their foods, especially during the COVID-19 pandemic. Thus, the purpose of this study is to investigate the relationship between perceived e-service quality and related customer service outcomes in online shopping context. The influence of sustainable marketing practices in terms of perceived e-service quality (ESQ), perceived usefulness of online reviews (PUO), brand self-connection (BSC), personal innovativeness (PRI), and willingness to pay for online food services (WPO) has largely been neglected in the previous studies. The present study proposes a conceptual model to fill this gap and empirically examines how ESQ affects PUO, BSC, WPO, and e-word-of-mouth (e-WOM) of food delivery service brands. An online questionnaire survey was conducted with 423 customers utilizing the PLS-SEM-based approach to determine product indicators. Empirical results reveal that ESQ significantly influence BSC and PUO. In the same vein, PUO significantly influence BSC, while BSC significantly influence e-WOM and WPO. The results further indicate that PRI moderates the relationship between ESQ and BSC. In a post-pandemic context, our analysis indicates enormous implications for food service delivery brands in emerging economies. Online food service providers should consider reviewers' opinions about the products and services they offer and encourage their customers to write positive reviews of the products and services they offer. [ABSTRACT FROM AUTHOR]

Aloe vera, Mentha arvensis (mint), Coriandrum sativum (coriander), and Cymbopogon citratus (lemongrass) leaf extracts were used to synthesize stable silver nanoparticles (Ag-NPs) by green chemistry. UV–vis spectrophotometry, X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy dispersive X-ray (EDX) spectroscopy techniques were used to characterize these biosynthesized nanoparticles. The data indicated that the silver nanoparticles were successfully synthesized, and the narrower particle size distribution was at 10–22 nm by maintaining a specific pH. As a short-term post-sowing treatment, Ag-NP solutions of different sizes (10 and 50 ppm) were introduced to mung bean seedlings, and the overall increase in plant growth was found to be more pronounced at 50 ppm concentration. The antibacterial activity of Ag-NPs was also investigated by disc diffusion test, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) test. The zones of inhibition (ZOI) were shown by Escherichia coli (E. coli) (1.9, 2.1, 1.7, and 2 mm), followed by Staphylococcus aureus (S. aureus) (1.8, 1.7, 1.6, and 1.9 mm), against coriander, mint, Aloe vera, and lemongrass, respectively. MIC and MBC values of E. coli, and S. aureus ranged from 7 to 8 µg/mL. Overall, this study demonstrates that Ag-NPs exhibit a strong antimicrobial activity and thus might be developed as a new type of antimicrobial agent for the treatment of bacterial infection. [ABSTRACT FROM AUTHOR]

In the areas of tidal and tsunami waves in oceans, rivers, ion and magneto-sound waves in plasmas, electromagnetic waves in transmission lines, homogeneous and stationary media, etc., the Riemann wave equations are attractive nonlinear equations. The modified exp (− Φ (η)) -function method is used in this article to show how well it can be applied to extract travelling and solitary wave solutions from higher-order nonlinear evolution equations (NLEEs) using the equations mentioned above. Trigonometric, hyperbolic, and exponential functions solitary wave solutions can be extracted using the above-mentioned technique. By changing specific values of the embedded parameters, we can obtain bell-form soliton, consolidated bell-shape soliton, compacton, singular kink soliton, flat kink shape soliton, smooth singular soliton, and other sorts of soliton solutions. The solutions are graphically illustrated in 3D and 2D for the accuracy of the outcome by using the Wolfram Mathematica 10. The verification of numerical solvers on the stability analysis of the solution is substantially aided by the analytic solutions. [ABSTRACT FROM AUTHOR]

Host inflammatory responses are key to protection against injury; however, persistent inflammation is detrimental and contributes to morbidity and mortality. Herein, we demonstrated the anti-inflammatory role of Arteannuin-B (1) and its new spirocyclic-2-isoxazoline derivative JR-9 and their side effects in acute inflammatory condition in vivo using LPS-induced cytokines assay, carrageenan-induced paw edema, acetic acid-induced writhing and tail immersion. The results show that the spirocyclic-2-isoxazoline derivative is a potent anti-inflammatory agent with minimal cell toxicity as compared to Arteannuin-B. In addition, the efficacies of these compounds were also validated by flow cytometric, computational, and histopathological analysis. Our results show that the anti-inflammatory response of JR-9 significantly reduces the ability of mouse macrophages to produce NO, TNF-α, and IL-6 following LPS stimulation. Therefore, JR-9 is a prospective candidate for the development of anti-inflammatory drugs and its molecular mechanism is likely related to the regulation of NF-κB and MAPK signaling pathway. [ABSTRACT FROM AUTHOR]

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique. [ABSTRACT FROM AUTHOR]

The radiated flow of magnetized viscous fluid subject to the viscous dissipation phenomenon is numerically studied. The radiative phenomenon is addressed with nonlinear relations. Further, analysis is performed by using the slip effects and convective thermal flow constraints. The transformed problem is numerically evaluated using the Keller Box method. The physical parameter effects, such as the magnetic parameter for the velocity profile, Prandtl number, Brownian motion parameter and Biot number for the energy profile and Lewis number, and the thermophoresis parameter for the concentration profile are discussed. The obtained results suggest applications in enhancing the heat transfer phenomenon, thermal system, energy generation, heat transmission devices, power generation, chemical reactions, etc. [ABSTRACT FROM AUTHOR]

Prior to the physical build of the industrial automation system, some challenges arise, such as processes' cycle times calculations, ergonomics and safety evaluation, and the integration of separate machines to the complete production shops. This, in turn, requires reconfiguring the processes and component parameters. As a result, the lifecycle of the system development is prolonged, and the potential for erroneous performance increases. In modern digital manufacturing environments, virtual engineering (VE) and virtual commissioning (VC) serve as effective tools to tackle the aforementioned problems and their consequences. The virtual models developed for VE and VC not only assist system developers in the physical build stage but also in the following stages of the system lifecycle by providing a common virtual model, a digital twin (DT), of the manufacturing processes and the product. This developed model should possess the ability to simulate the system behaviour, e.g., the mechanics, kinematics, speed and acceleration profiles. Three stakeholders are involved in the development process: the machine builder, system integrator and end user. The current work focuses on the virtual engineering approach to support the entire lifecycle of a manufacturing system from the machine builder, system integrator and end user perspectives. For this purpose, it puts forward a systematic methodology of implementing VC and VE using a toolset developed by the Automation Systems Group at the University of Warwick within an industrial project. The suggested methodology is illustrated in a case study where a digital twin of a physical station was modelled, developed and tested in parallel with the physical machine development and build. Finally, the benefits and limitations are highlighted based on the gained outcomes and the implemented activities. [ABSTRACT FROM AUTHOR]

The recent outbreak of COVID-19 around the world has caused a global health catastrophe along with economic consequences. As per the World Health Organization (WHO), this devastating crisis can be minimized and controlled if humans wear facemasks in public; however, the prevention of spreading COVID-19 can only be possible only if they are worn properly, covering both the nose and mouth. Nonetheless, in public places or in chaos, a manual check of persons wearing the masks properly or not is a hectic job and can cause panic. For such conditions, an automatic mask-wearing system is desired. Therefore, this study analyzed several deep learning pre-trained networks and classical machine learning algorithms that can automatically detect whether the person wears the facemask or not. For this, 40,000 images are utilized to train and test 9 different models, namely, InceptionV3, EfficientNetB0, EfficientNetB2, DenseNet201, ResNet152, VGG19, convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), to recognize facemasks in images. Besides just detecting the mask, the trained models also detect whether the person is wearing the mask properly (covering nose and mouth), partially (mouth only), or wearing it inappropriately (not covering nose and mouth). Experimental work reveals that InceptionV3 and EfficientNetB2 outperformed all other methods by attaining an overall accuracy of around 98.40% and a precision, recall, and F1-score of 98.30%. [ABSTRACT FROM AUTHOR]

In this study, the significance of the Coriolis force on the dynamics of Carreau—Yasuda flow toward a continuously stretched surface subject to the Darcy–Forchheimer law is investigated. The nanoparticles are incorporated due to their unusual characteristics (e.g., extraordinary thermal conductivity), which are significant in heat exchangers and advanced nanotechnology. To avoid possible sedimentation of tiny particles, the gyrotactic microorganisms must be incorporated. The goal of this research was to find out the dynamics of three-dimensional rotational flow for nanofluids under the influence of Darcy–Forchheimer with the thermophoresis effect and motile microorganisms. The equations governing mass, momentum, and energy equations are formalized using partial derivatives, which may subsequently be transformed into dimensionless differential shapes using the personifications of apposite similarity transformations. The MATLAB application bvp4c was used in conjunction with a shooting technique to solve a nonlinear mathematical model based on ordinary differential equations. It was observed that the base fluid velocities decreased against higher input of rotation and porosity parameters; moreover, the Brownian motion and thermophoresis increased the temperature profile. [ABSTRACT FROM AUTHOR]

Stoichiometric ratios of carbon (C), nitrogen (N), and phosphorus (P) are considered indicators of nutrient status and ultimate ecosystem health. A detailed investigation of these elements in the leaves, branches, forest layer vegetation and soil, depending on stand age, was carried out. We investigated the effects of stand age (9-, 18-, 28-, and 48-year) on the aboveground plant parts (leaf, branch, herb, shrub, plant litter) and belowground pools (soil, roots) of P. massoniana plantations. The CNP stoichiometry of trees was affected by stand age. Mean N content in the aboveground parts in the nine-yr stand was greater than the other stands (18-, 28-, 48-yr), which decreased with increasing stand age. As stands aged, the nutrient demands of the plantations increased as well as their N:P ratios in soil. C content in the soil ranged from 30 to 105, the total N was 0.06 to 1.6, and the total P content ranged from 3.3–6.4 g kg−1. Soil C, N and P contents were greatly influenced by both stand age and soil depth, because surface soil sequester C and N more actively compared to deeper horizons, and more nutrients are released to the topsoil by the plant litter layer. Similarly, the ratios of other layers had a similar pattern as CNP because more nutrients were taken up by the plantations, decreasing nutrient supply in the deeper soil horizons. The green leaves N:P ratios (16) indicate limited growth of P. massoniana, as the range for global nutrient limitation for woody plants oscillated between 14–16, indicating N and P limitation. Young stands were observed to have greater P content and P resorption efficiency (56.9%–67.3%), with lower C:P and N:P ratios (704.4; 14.8). We conclude that with stand development, the nutrient demands of the plantations also increase, and soil N:P stoichiometry shows that these improve soil quality. [ABSTRACT FROM AUTHOR]

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts. [ABSTRACT FROM AUTHOR]

Wire + arc additive manufacturing (WAAM) is a modern manufacturing process that has opened new possibilities for rapid builds and reductions in material wastage. This paper explores residual stress in WAAM Ti-6Al-4V walls built using three different deposition strategies: single bead, parallel path, and oscillation path. The effect of interlayer hammer peening and interlayer temperature was investigated for the single bead walls. We also examined the residual stress in compact-tension (C(T)) coupons extracted from large builds (walls) with crack orientation either parallel with or perpendicular to the build direction. This type of sample is often used for the measurement of the fatigue crack growth rate. The contour method was used for experimental determinations of residual stress. In addtion, residual stress in the C(T) coupons was estimated by finite element (FE) analysis. A good agreement was achieved between the contour method and FE analysis. The oscillation-path wall had the lowest residual stress values. For the single bead walls built with various process conditions, residual stress was significantly reduced after removing the substrate. A interlayer temperature of 110 °C resulted in much higher residual stress values in the wall (both tensile and compressive) compared to the continuous build, with much higher interlayer temperature. [ABSTRACT FROM AUTHOR]

The snow leopard (Panthera uncia) is a cryptic and rare big cat inhabiting Asia's remote and harsh elevated areas. Its population has decreased across the globe for various reasons, including human–snow leopard conflicts (HSCs). Understanding the snow leopard's distribution range and habitat interactions with human/livestock is essential for understanding the ecological context in which HSCs occur and thus gives insights into how to mitigate HSCs. In this study, a MaxEnt model predicted the snow leopard's potential distribution and analyzed the land use/cover to determine the habitat interactions of snow leopards with human/livestock in Karakoram–Pamir, northern Pakistan. The results indicated an excellent model performance for predicting the species' potential distribution. The variables with higher contributions to the model were the mean diurnal temperature range (51.7%), annual temperature range (18.5%), aspect (14.2%), and land cover (6.9%). The model predicted approximately 10% of the study area as a highly suitable habitat for snow leopards. Appropriate areas included those at an altitude ranging from 2721 to 4825 m, with a mean elevation of 3796.9 ± 432 m, overlapping between suitable snow leopard habitats and human presence. The human encroachment (human settlements and agriculture) in suitable snow leopard habitat increased by 115% between 2008 and 2018. Increasing encroachment and a clear overlap between snow leopard suitable habitat and human activities, signs of growing competition between wildlife and human/livestock for limited rangeland resources, may have contributed to increasing HSCs. A sound land use plan is needed to minimize overlaps between suitable snow leopard habitat and human presence to mitigate HSCs in the long run. [ABSTRACT FROM AUTHOR]

Deep learning has gained immense attention from researchers in medicine, especially in medical imaging. The main bottleneck is the unavailability of sufficiently large medical datasets required for the good performance of deep learning models. This paper proposes a new framework consisting of one variational autoencoder (VAE), two generative adversarial networks, and one auxiliary classifier to artificially generate realistic-looking skin lesion images and improve classification performance. We first train the encoder-decoder network to obtain the latent noise vector with the image manifold's information and let the generative adversarial network sample the input from this informative noise vector in order to generate the skin lesion images. The use of informative noise allows the GAN to avoid mode collapse and creates faster convergence. To improve the diversity in the generated images, we use another GAN with an auxiliary classifier, which samples the noise vector from a heavy-tailed student t-distribution instead of a random noise Gaussian distribution. The proposed framework was named TED-GAN, with T from the t-distribution and ED from the encoder-decoder network which is part of the solution. The proposed framework could be used in a broad range of areas in medical imaging. We used it here to generate skin lesion images and have obtained an improved classification performance on the skin lesion classification task, rising from 66% average accuracy to 92.5%. The results show that TED-GAN has a better impact on the classification task because of its diverse range of generated images due to the use of a heavy-tailed t-distribution. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), rapidly spreading around the world, poses a major threat to the global public health. Herein, we demonstrated the binding mechanism of PF-07321332, α-ketoamide, lopinavir, and ritonavir to the coronavirus 3-chymotrypsin-like-protease (3CLpro) by means of docking and molecular dynamic (MD) simulations. The analysis of MD trajectories of 3CLpro with PF-07321332, α-ketoamide, lopinavir, and ritonavir revealed that 3CLpro–PF-07321332 and 3CLpro–α-ketoamide complexes remained stable compared with 3CLpro–ritonavir and 3CLpro–lopinavir. Investigating the dynamic behavior of ligand–protein interaction, ligands PF-07321332 and α-ketoamide showed stronger bonding via making interactions with catalytic dyad residues His41–Cys145 of 3CLpro. Lopinavir and ritonavir were unable to disrupt the catalytic dyad, as illustrated by increased bond length during the MD simulation. To decipher the ligand binding mode and affinity, ligand interactions with SARS-CoV-2 proteases and binding energy were calculated. The binding energy of the bespoke antiviral PF-07321332 clinical candidate was two times higher than that of α-ketoamide and three times than that of lopinavir and ritonavir. Our study elucidated in detail the binding mechanism of the potent PF-07321332 to 3CLpro along with the low potency of lopinavir and ritonavir due to weak binding affinity demonstrated by the binding energy data. This study will be helpful for the development and optimization of more specific compounds to combat coronavirus disease. [ABSTRACT FROM AUTHOR]

Drawing on the motivation, opportunity and ability (MOA) framework, we investigate the influence of charismatic leadership on salespeople's service and sales activities—termed service-sales ambidextrous (SSA) behavior, which subsequently turn into service recovery performance outcomes. The primary aim of this research is to strengthen salespeople's service quality in parallel to their selling activities while recovering a service failure. We validate the model using a sample of 344 business-to-business salespeople using partial least square structural equation modeling (PLS-SEM) technique. Study results show that charismatic leadership is positively and significantly related to service-sales ambidexterity. Likewise, service-sales ambidexterity has a positive and significant relationship with service recovery performance and adaptive selling behavior. Moreover, we found a significant relationship between adaptive selling behavior and service recovery performance. The results further specify that salesperson motivation, opportunity and ability to engage in SSA significantly moderate the relationship between charismatic leadership and service-sales ambidexterity. The results suggest the need for training programs that provide the salesperson with opportunities to understand the simultaneous implementation of selling strategies while also providing customer services. [ABSTRACT FROM AUTHOR]

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with the MOEA/D framework in order to make the QPSO be able to solve MOPs effectively, with the advantage of the QPSO being fully used. We also employ a diversity controlling mechanism to avoid the premature convergence especially at the later stage of the search process, and thus further improve the performance of our proposed algorithm. In addition, we introduce a number of nondominated solutions to generate the global best for guiding other particles in the swarm. Experiments are conducted to compare the proposed algorithm, DMO-QPSO, with four multi-objective particle swarm optimization algorithms and one multi-objective evolutionary algorithm on 15 test functions, including both bi-objective and tri-objective problems. The results show that the performance of the proposed DMO-QPSO is better than other five algorithms in solving most of these test problems. Moreover, we further study the impact of two different decomposition approaches, i.e., the penalty-based boundary intersection (PBI) and Tchebycheff (TCH) approaches, as well as the polynomial mutation operator on the algorithmic performance of DMO-QPSO. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy. [ABSTRACT FROM AUTHOR]

Toll-like receptor (TLR) signaling plays a critical role in the induction and progression of autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematous, experimental autoimmune encephalitis, type 1 diabetes mellitus and neurodegenerative diseases. Deciphering antigen recognition by antibodies provides insights and defines the mechanism of action into the progression of immune responses. Multiple strategies, including phage display and hybridoma technologies, have been used to enhance the affinity of antibodies for their respective epitopes. Here, we investigate the TLR4 antibody-binding epitope by computational-driven approach. We demonstrate that three important residues, i.e., Y328, N329, and K349 of TLR4 antibody binding epitope identified upon in silico mutagenesis, affect not only the interaction and binding affinity of antibody but also influence the structural integrity of TLR4. Furthermore, we predict a novel epitope at the TLR4-MD2 interface which can be targeted and explored for therapeutic antibodies and small molecules. This technique provides an in-depth insight into antibody–antigen interactions at the resolution and will be beneficial for the development of new monoclonal antibodies. Computational techniques, if coupled with experimental methods, will shorten the duration of rational design and development of antibody therapeutics. [ABSTRACT FROM AUTHOR]

APETALA2/ETHYLENE RESPONSIVE FACTOR (AP2/ERF) transcription factors play important roles in plant development and stress response. Although AP2/ERF genes have been extensively investigated in model plants such as Arabidopsis thaliana, little is known about their role in biotic stress response in perennial fruit tree crops such as apple (Malus × domestica). Here, we investigated the role of MdERF100 in powdery mildew resistance in apple. MdERF100 localized to the nucleus but showed no transcriptional activation activity. The heterologous expression of MdERF100 in Arabidopsis not only enhanced powdery mildew resistance but also increased reactive oxygen species (ROS) accumulation and cell death. Furthermore, MdERF100-overexpressing Arabidopsis plants exhibited differential expressions of genes involved in jasmonic acid (JA) and salicylic acid (SA) signaling when infected with the powdery mildew pathogen. Additionally, yeast two-hybrid and bimolecular fluorescence complementation assays confirmed that MdERF100 physically interacts with the basic helix–loop–helix (bHLH) protein MdbHLH92. These results suggest that MdERF100 mediates powdery mildew resistance by regulating the JA and SA signaling pathways, and MdbHLH92 is involved in plant defense against powdery mildew. Overall, this study enhances our understanding of the role of MdERF genes in disease resistance, and provides novel insights into the molecular mechanisms of powdery mildew resistance in apple. [ABSTRACT FROM AUTHOR]

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly. [ABSTRACT FROM AUTHOR]

The necrotrophic fungus Botrytis cinerea causes devastating pre- and post-harvest yield losses in grapevine (Vitis vinifera L.). Although B. cinerea has been well-studied in different plant species, there is limited information related to the resistance and susceptibility mechanisms of Vitis genotypes against B. cinerea infection. In the present study, leaves and berries of twenty four grape genotypes were evaluated against B. cinerea infection. According to the results, one genotype (Ju mei gui) was highly resistant (HR), one genotype (Kyoho) was resistant (R), eight genotypes were susceptible (S), and fourteen genotypes were highly susceptible (HS) against infection of B. cinerea in leaves. Whereas in the case of B.cinerea infection in grape berry, three genotypes were found to be highly resistant, three resistant, eleven genotypes susceptible, and seven were highly susceptible. To further explore the mechanism of disease resistance in grapevine, we evaluated "Ju mei gui" and "Summer black" in terms of B. cinerea progression, reactive oxygen species reactions, jasmonic acid contents, and the activities of antioxidant enzymes in leaf and fruit. We surmise that the resistance of "Ju mei gui" is due to seized fungal growth, minor reactive oxygen species (ROS) production, elevated antioxidant enzyme activity, and more jasmonic acid (JA) contents. This study provides insights into the resistance and susceptibility mechanism of Vitis genotypes against B. cinerea. This will help for the selection of appropriate germplasm to explore the molecular basis of disease resistance mechanisms in grapevine. [ABSTRACT FROM AUTHOR]

Members of the plant-specific GASA (gibberellic acid-stimulated Arabidopsis) gene family have multiple potential roles in plant growth and development, particularly in flower induction and seed development. However, limited information is available about the functions of these genes in fruit plants, particularly in grapes. We identified 14 GASA genes in grapevine (Vitis vinifera L.) and performed comprehensive bioinformatics and expression analyses. In the bioinformatics analysis, the locations of genes on chromosomes, physiochemical properties of proteins, protein structure, and subcellular positions were described. We evaluated GASA proteins in terms of domain structure, exon-intron distribution, motif arrangements, promoter analysis, phylogenetic, and evolutionary history. According to the results, the GASA domain is conserved in all proteins and the proteins are divided into three well-conserved subgroups. Synteny analysis proposed that segmental and tandem duplication have played a role in the expansion of the GASA gene family in grapes, and duplicated gene pairs have negative selection pressure. Most of the proteins were predicted to be in the extracellular region, chloroplasts, and the vacuole. In silico promoter analysis suggested that the GASA genes may influence different hormone signaling pathways and stress-related mechanisms. Additionally, we performed a comparison of the expression between seedless (Thompson seedless) and seeded (Red globe) cultivars in different plant parts, including the ovule during different stages of development. Furthermore, some genes were differentially expressed in different tissues, signifying their role in grapevine growth and development. Several genes (VvGASA2 and 7) showed different expression levels in later phases of seed development in Red globe and Thompson seedless, suggesting their involvement in seed development. Our study presents the first genome-wide identification and expression profiling of grapevine GASA genes and provides the basis for functional characterization of GASA genes in grapes. We surmise that this information may provide new potential resources for the molecular breeding of grapes. [ABSTRACT FROM AUTHOR]

Members of the plant-specific B3-domain transcription factor family have important and varied functions, especially with respect to vegetative and reproductive growth. Although B3 genes have been studied in many other plants, there is limited information on the genomic organization and expression of B3 genes in grapevine (Vitis vinifera L.). In this study, we identified 50 B3 genes in the grapevine genome and analyzed these genes in terms of chromosomal location and syntenic relationships, intron–exon organization, and promoter cis-element content. We also analyzed the presumed proteins in terms of domain structure and phylogenetic relationships. Based on the results, we classified these genes into five subfamilies. The syntenic relationships suggest that approximately half of the genes resulted from genome duplication, contributing to the expansion of the B3 family in grapevine. The analysis of cis-element composition suggested that most of these genes may function in response to hormones, light, and stress. We also analyzed expression of members of the B3 family in various structures of grapevine plants, including the seed during seed development. Many B3 genes were expressed preferentially in one or more structures of the developed plant, suggesting specific roles in growth and development. Furthermore, several of the genes were expressed differentially in early developing seeds from representative seeded and seedless cultivars, suggesting a role in seed development or abortion. The results of this study provide a foundation for functional analysis of B3 genes and new resources for future molecular breeding of grapevine. [ABSTRACT FROM AUTHOR]

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the "big data" generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery. [ABSTRACT FROM AUTHOR]

Marine flora is taxonomically diverse, biologically active, and chemically unique. It is an excellent resource, which offers great opportunities for the discovery of new biopharmaceuticals such as immunomodulators and drugs targeting cancerous, inflammatory, microbial, and fungal diseases. The ability of some marine molecules to mediate specific inhibitory activities has been demonstrated in a range of cellular processes, including apoptosis, angiogenesis, and cell migration and adhesion. Immunomodulators have been shown to have significant therapeutic effects on immune-mediated diseases, but the search for safe and effective immunotherapies for other diseases such as sinusitis, atopic dermatitis, rheumatoid arthritis, asthma and allergies is ongoing. This review focuses on the marine-originated bioactive molecules with immunomodulatory potential, with a particular focus on the molecular mechanisms of specific agents with respect to their targets. It also addresses the commercial utilization of these compounds for possible drug improvement using metabolic engineering and genomics. [ABSTRACT FROM AUTHOR]

Botrytis cinerea is a necrotrophic fungal phytopathogen with devastating effects on many Vitis genotypes. Here, a screening of 81 Vitis genotypes for leaf resistance to B. cinerea revealed two highly resistant (HR), twelve resistant (R), twenty-five susceptible (S) and forty-two highly susceptible (HS) genotypes. We focused on the HR genotype, 'Zi Qiu' (Vitis davidii), and the HS genotype 'Riesling' (V. vinifera), to elucidate mechanisms of host resistance and susceptibility against B. cinerea, using detached leaf assays. These involved a comparison of fungal growth, reactive oxygen species (ROS) responses, jasmonic acid (JA) levels, and changes in the anti-oxidative system between the two genotypes after inoculation with B. cinerea. Our results indicated that the high-level resistance of 'Zi Qiu' can be attributed to insignificant fungal development, low ROS production, timely elevation of anti-oxidative functions, and high JA levels. Moreover, severe fungal infection of 'Riesling' and sustained ROS production coincided with relatively unchanged anti-oxidative activity, as well as low JA levels. This study provides insights into B. cinerea infection in grape, which can be valuable for breeders by providing information for selecting suitable germplasm with enhanced disease resistance. [ABSTRACT FROM AUTHOR]

Jasmonate ZIM-domain (JAZ) family proteins comprise a class of transcriptional repressors that silence jasmonate-inducible genes. Although a considerable amount of research has been carried out on this gene family, there is still very little information available on the role of specific JAZ gene members in multiple pathogen resistance, especially in non-model species. In this study, we investigated the potential resistance function of the VqJAZ7 gene from a disease-resistant wild grapevine, Vitis quinquangularis cv. "Shang-24", through heterologous expression in Arabidopsis thaliana. VqJAZ7-expressing transgenic Arabidopsis were challenged with three pathogens: the biotrophic fungus Golovinomyces cichoracearum, necrotrophic fungus Botrytis cinerea, and semi-biotrophic bacteria Pseudomonas syringae pv. tomato DC3000. We found that plants expressing VqJAZ7 showed greatly reduced disease symptoms for G. cichoracearum, but not for B. cinerea or P. syringae. In response to G cichoracearum infection, VqJAZ7-expressing transgenic lines exhibited markedly higher levels of cell death, superoxide anions (O2¯, and H2O2 accumulation, relative to nontransgenic control plants. Moreover, we also tested the relative expression of defense-related genes to comprehend the possible induced pathways. Taken together, our results suggest that VqJAZ7 in grapevine participates in molecular pathways of resistance to G. cichoracearum, but not to B. cinerea or P. syringae. [ABSTRACT FROM AUTHOR]

Botrytis cinerea is a necrotrophic fungal phytopathogen with devastating effects on many Vitis genotypes. Here, a screening of 81 Vitis genotypes for leaf resistance to B . cinerea revealed two highly resistant (HR), twelve resistant (R), twenty-five susceptible (S) and forty-two highly susceptible (HS) genotypes. We focused on the HR genotype, 'Zi Qiu' ( Vitis davidii ), and the HS genotype 'Riesling' ( V . vinifera ), to elucidate mechanisms of host resistance and susceptibility against B . cinerea , using detached leaf assays. These involved a comparison of fungal growth, reactive oxygen species (ROS) responses, jasmonic acid (JA) levels, and changes in the anti-oxidative system between the two genotypes after inoculation with B . cinerea . Our results indicated that the high-level resistance of 'Zi Qiu' can be attributed to insignificant fungal development, low ROS production, timely elevation of anti-oxidative functions, and high JA levels. Moreover, severe fungal infection of 'Riesling' and sustained ROS production coincided with relatively unchanged anti-oxidative activity, as well as low JA levels. This study provides insights into B . cinerea infection in grape, which can be valuable for breeders by providing information for selecting suitable germplasm with enhanced disease resistance.