The 100 most-cited articles in Scientific Reports, 2023

Molecular docking is a computational technique that predicts the binding affinity of ligands to receptor proteins. Although it has potential uses in nutraceutical research, it has developed into a formidable tool for drug development. Bioactive substances called nutraceuticals are present in food sources and can be used in the management of diseases. Finding their molecular targets can help in the creation of disease-specific new therapies. The purpose of this review was to explore molecular docking's application to the study of dietary supplements and disease management. First, an overview of the fundamentals of molecular docking and the various software tools available for docking was presented. The limitations and difficulties of using molecular docking in nutraceutical research are also covered, including the reliability of scoring functions and the requirement for experimental validation. Additionally, there was a focus on the identification of molecular targets for nutraceuticals in numerous disease models, including those for sickle cell disease, cancer, cardiovascular, gut,…

Abstract Cesium tin chloride (CsSnCl 3 ) is a potential and competitive absorber material for lead-free perovskite solar cells (PSCs). The full potential of CsSnCl 3 not yet been realized owing to the possible challenges of defect-free device fabrication, non-optimized alignment of the electron transport layer (ETL), hole transport layer (HTL), and the favorable device configuration. In this work, we proposed several CsSnCl 3 -based solar cell (SC) configurations using one dimensional solar cell capacitance simulator (SCAPS-1D) with different competent ETLs like indium–gallium–zinc–oxide (IGZO), tin-dioxide (SnO 2 ), tungsten disulfide (WS 2 ), ceric dioxide (CeO 2 ), titanium dioxide (TiO 2 ), zinc oxide (ZnO), C 60 , PCBM, and HTLs of cuprous oxide (Cu 2 O), cupric oxide (CuO), nickel oxide (NiO), vanadium oxide (V 2 O 5 ), copper iodide (CuI), CuSCN, CuSbS 2 , Spiro MeOTAD, CBTS, CFTS, P3HT, PEDOT:PSS. Simulation results revealed that ZnO, TiO 2 , IGZO, WS 2 , PCBM, and C 60 ETLs-based halide perovskites with ITO/ETLs/CsSnCl 3 /CBTS/Au heterostructure exhibited outstanding…

In many dilemmas, decisions are determined not by a single factor, but by multiple ones, including memory, reputation, reward and punishment. In recent years, how to design a mechanism to promote cooperation has become a research hot-spot. However, most of the previous studies mainly consider the historical benefits of the game, and pay less attention to the stability of the strategy (the frequency of strategy changes in the length of memory) and the proportion of memory in decision-making. The decision-making process of group evolution involves the influence of memory information on cooperative evolution in multi round games. It makes up for the lack of stability factors and weights in previous studies. Based on the above factors, a new strategy update rule is proposed to study the influence of the stability of historical strategy information on the evolution of cooperation in prisoner's dilemma game, and the influence of memory weight on cooperation is considered. The stability of the current strategy is measured by the strategy in historical memory (the number of times the…

Although chatbots such as ChatGPT can facilitate cost-effective text generation and editing, factually incorrect responses (hallucinations) limit their utility. This study evaluates one particular type of hallucination: fabricated bibliographic citations that do not represent actual scholarly works. We used ChatGPT-3.5 and ChatGPT-4 to produce short literature reviews on 42 multidisciplinary topics, compiling data on the 636 bibliographic citations (references) found in the 84 papers. We then searched multiple databases and websites to determine the prevalence of fabricated citations, to identify errors in the citations to non-fabricated papers, and to evaluate adherence to APA citation format. Within this set of documents, 55% of the GPT-3.5 citations but just 18% of the GPT-4 citations are fabricated. Likewise, 43% of the real (non-fabricated) GPT-3.5 citations but just 24% of the real GPT-4 citations include substantive citation errors. Although GPT-4 is a major improvement over GPT-3.5, problems remain.

Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding "realistic image appearance", "anatomical correctness", and "consistency between slices". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data]).

ChatGPT and similar generative AI models have attracted hundreds of millions of users and have become part of the public discourse. Many believe that such models will disrupt society and lead to significant changes in the education system and information generation. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models-both lack scientific rigor. We systematically assess the quality of AI-generated content through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays. We use essays that were rated by a large number of human experts (teachers). We augment the analysis by considering a set of linguistic characteristics of the generated essays. Our results demonstrate that ChatGPT generates essays that are rated higher regarding quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays. Since the technology is readily available, we believe that educators must act immediately. We must…

Abstract Methylene blue (MB) is abundantly found in textile industrial effluent which can cause severe health problems for public and environmental ecology. Therefore, this study aimed to remove MB from textile wastewater using the activated carbon developed from Rumex abyssinicus . The adsorbent was activated using chemical and thermal methods, and then it was characterized by SEM, FTIR, BET, XRD, and pH zero-point charge (pHpzc). The adsorption isotherm and kinetics were also investigated. The experimental design was composed of four factors at three levels (pH (3, 6, and 9), initial MB concentration (100, 150, and 200 mg/L), adsorbent dosage (20, 40, and 60 mg/100 mL), and contact time (20, 40, and 60 min)). The adsorption interaction was evaluated using response surface methodology. The characterization of a Rumex abyssinicus activated carbon was found to have multiple functional groups (FTIR), an amorphous structure (XRD), crack with ups and down morphology (SEM), pHpzc of 5.03 and a high BET-specific surface area of 2522 m 2 /g. The optimization of MB dye removal was carried…

The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models' consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT's 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov-Smirnov, Wilcoxon, and Kruskal-Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and…

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the…

Abstract In recent ages, green nanotechnology has gained attraction in the synthesis of metallic nanoparticles due to their cost-effectiveness, simple preparation steps, and environmentally-friendly. In the present study, copper oxide nanoparticles (CuO NPs) were prepared using Parthenium hysterophorus whole plant aqueous extract as a reducing, stabilizing, and capping agent. The CuO NPs were characterized via UV–Vis Spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), powder X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Dynamic Light Scattering (DLS). The UV–Vis spectra of CuO NPs showed a surface plasmonic resonance band to occur at 340 nm. FTIR analysis revealed the presence of secondary metabolites on the surface of CuO NPs, with a characteristic Cu–O stretching band being identified at 522 cm −1 . Scanning electron micrographs and transmission electron micrographs showed that CuO NPs were nearly spherical, with an average particle of 59.99 nm obtained from the SEM micrograph. The monoclinic crystalline…

Wearable health sensors could monitor the wearer's health and surrounding environment in real-time. With the development of sensor and operating system hardware technology, the functions of wearable devices have been gradually enriched with more diversified forms and more accurate physiological indicators. These sensors are moving towards high precision, continuity, and comfort, making great contributions to improving personalized health care. At the same time, in the context of the rapid development of the Internet of Things, the ubiquitous regulatory capabilities have been released. Some sensor chips are equipped with data readout and signal conditioning circuits, and a wireless communication module for transmitting data to computer equipment. At the same time, for data analysis of wearable health sensors, most companies use artificial neural networks (ANN). In addition, artificial neural networks could help users effectively get relevant health feedback. Through the physiological response of the human body, various sensors worn could effectively transmit data to the control unit,…

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We…

Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising diffusion probabilistic models, which have demonstrated superiority in natural image synthesis. In this study, we introduce Medfusion, a conditional latent DDPM designed for medical image generation, and evaluate its performance against GANs, which currently represent the state-of-the-art. Medfusion was trained and compared with StyleGAN-3 using fundoscopy images from the AIROGS dataset, radiographs from the CheXpert dataset, and histopathology images from the CRCDX dataset. Based on previous studies, Progressively Growing GAN (ProGAN) and Conditional GAN (cGAN) were used as additional baselines on the CheXpert and CRCDX datasets, respectively. Medfusion exceeded GANs in terms of diversity (recall), achieving better scores of 0.40 compared to 0.19 in the AIROGS dataset, 0.41 compared to 0.02 (cGAN) and 0.24 (StyleGAN-3) in the CRMDX dataset, and 0.32 compared to 0.17 (ProGAN) and 0.08 (StyleGAN-3) in the CheXpert dataset.…

Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and…

Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions ("smart replies"), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve…

Abstract Moringa oleifera Lam . is a common edible plant, famous for several nutritional and therapeutic benefits. This study investigates the salt -induced modulations in plant growth, physio-biochemical responses, and antioxidant performance of M. oleifera grown under 0, 50, and 100 mM NaCl concentrations. Results showed that the plant effectively managed moderate salinity (50 mM NaCl) by maintaining succulence, weight ratios, and biomass allocation patterns of both shoot and root with minimal reduction in dry biomass. However, high salinity (100 mM NaCl) remarkably declined all growth parameters. The plant accumulated more Na + and Cl − , while less K + under salinity as compared to the control. Consequently, osmotic potentials of both root and leaf decreased under salinity, which was corroborated by the high amount of proline and soluble sugars. Increased level of H 2 O 2 with significantly unchanged membrane fluidity indicating its role in perceiving and managing stress at moderate salinity. In addition, increased activities of superoxide dismutase, and catalase, with increased…

Creativity has traditionally been considered an ability exclusive to human beings. However, the rapid development of artificial intelligence (AI) has resulted in generative AI chatbots that can produce high-quality artworks, raising questions about the differences between human and machine creativity. In this study, we compared the creativity of humans (n = 256) with that of three current AI chatbots using the alternate uses task (AUT), which is the most used divergent thinking task. Participants were asked to generate uncommon and creative uses for everyday objects. On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceed those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to fully replicate or surpass with AI technology. The study provides insights into the relationship…

The release and rapid diffusion of ChatGPT have caught the attention of educators worldwide. Some educators are enthusiastic about its potential to support learning. Others are concerned about how it might circumvent learning opportunities or contribute to misinformation. To better understand reactions about ChatGPT concerning education, we analyzed Twitter data (16,830,997 tweets from 5,541,457 users). Based on topic modeling and sentiment analysis, we provide an overview of global perceptions and reactions to ChatGPT regarding education. ChatGPT triggered a massive response on Twitter, with education being the most tweeted content topic. Topics ranged from specific (e.g., cheating) to broad (e.g., opportunities), which were discussed with mixed sentiment. We traced that authority decisions may influence public opinions. We discussed that the average reaction on Twitter (e.g., using ChatGPT to cheat in exams) differs from discussions in which education and teaching-learning researchers are likely to be more interested (e.g., ChatGPT as an intelligent learning partner). This study…

Abstract In this paper, we aimed to investigate the linear and nonlinear optical properties of GO-ZnO and RGO-ZnO nanocomposites in comparison with pure GO and reduced graphene oxide (RGO). For this purpose, GO, RGO, GO-ZnO, and RGO-ZnO were synthesized and characterized by Fourier transform infrared (FT-IR), Ultraviolet–Visible (UV–Vis) absorption, X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX). XRD and EDX analysis indicated the reduction of GO as well as the successful synthesis of GO-ZnO and RGO-ZnO nanocomposites. The FT-IR spectroscopy showed that absorption bands were at 3340 cm−1, 1630 cm−1, 1730 cm−1 and 480 cm−1 related to OH, C=C, C=O, and Zn–O stretching vibrations, respectively. The direct band gaps of GO, RGO, GO-ZnO and RGO-ZnO from UV–Vis spectra were at 3.36, 3.18, 3.63 and 3.25 eV, sequentially. Moreover, the third-order nonlinear optical properties were investigated using a z-scan technique with Nd: YAG laser (532 nm, 70 mW). It can be seen that the nonlinear absorption coefficient value $$(\upbeta )$$ ( β ) increased from 5.3 × 10–4 (GO)…

A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images…

Microglia play a vital role maintaining brain homeostasis but can also cause persistent neuroinflammation. Short-chain fatty acids (SCFAs) produced by the intestinal microbiota have been suggested to regulate microglia inflammation indirectly by signaling through the gut-brain axis or directly by reaching the brain. The present work evaluated the anti-inflammatory effects of SCFAs on lipopolysaccharide (LPS)-stimulated microglia from mice fed inulin, a soluble fiber that is fermented by intestinal microbiota to produce SCFAs in vivo, and SCFAs applied to primary microglia in vitro. Feeding mice inulin increased SCFAs in the cecum and in plasma collected from the hepatic portal vein. Microglia isolated from mice fed inulin and stimulated with LPS in vitro secreted less tumor necrosis factor α (TNF-α) compared to microglia from mice not given inulin. Additionally, when mice were fed inulin and injected i.p with LPS, the ex vivo secretion of TNF-α by isolated microglia was lower than that secreted by microglia from mice not fed inulin and injected with LPS. Similarly, in vitro…

Abstract Deliberate control of the breath (breathwork) has recently received an unprecedented surge in public interest and breathing techniques have therapeutic potential to improve mental health. Our meta-analysis primarily aimed to evaluate the efficacy of breathwork through examining whether, and to what extent, breathwork interventions were associated with lower levels of self-reported/subjective stress compared to non-breathwork controls. We searched PsycInfo, PubMed, ProQuest, Scopus, Web of Science, ClinicalTrials.gov and ISRCTN up to February 2022, initially identifying 1325 results. The primary outcome self-reported/subjective stress included 12 randomised-controlled trials ( k = 12) with a total of 785 adult participants. Most studies were deemed as being at moderate risk of bias. The random-effects analysis yielded a significant small-to-medium mean effect size, g = − 0.35 [95% CI − 0.55, − 0.14], z = 3.32, p = 0.0009, showing breathwork was associated with lower levels of stress than control conditions. Heterogeneity was intermediate and approaching significance, χ 2 11…

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been…

Abstract In this research work, carbon dots (CDs) were synthesized from the renewable leaves of an indigenous medicinal plant by the one-pot sand bath method, Azadirachta indica . The synthesized CDs were characterized for its optical properties using UV–Vis, Fluorescence and Fourier transform infrared (FT-IR) spectrophotometry and for structural properties using dynamic light scattering (DLS), X-ray Diffraction (XRD) and high resolution Transmission electron microscopy (HR-TEM). The synthesized CDs exhibited concentration dependent biocompatibility when tested in mouse fibroblast L929 cell line. The EC 50 values of biomedical studies, free radical scavenging activity (13.87 μgmL −1 ), and total antioxidant capacity (38 μgmL −1 ) proved CDs were exceptionally good. These CDs showed an appreciable zone of inhibition when examined on four bacterial (two gram-positive and gram-negative) and two fungal strains at minimum concentrations. Cellular internalisation studies performed on human breast cancer cells (MCF 7- bioimaging) revealed the applicability of CDs in bioimaging, wherein the…

Sustainable agriculture requires cooperative and coordinated action across multiple sectors and policy domains. However, farmer-stakeholder behaviors and action remain pivotal to sustainable food system management in many rural development contexts. We assess farmer pro-environmental behavioral intention through the development and application of a novel integrated approach combining two dominant psychological theories of behavior change: the Norm Activation Model (NAM) and Theory of Planned Behavior (TPB). We apply this framework to targeted research with potato growers of Kerman Province in southeastern Iran, using survey data (sample n = 381) analyzed through structural equation modeling (SEM). The integrated NAM-TPB model provides insight into both pro-social and self-interested motivations for farmer pro-environmental behavioral intention, with the model explaining 77% of total variance. We found that three variables, Awareness of Consequence (AC), Perceived Behavioral Control (PBC), and Subjective Norms (SN) were the strongest indicators of pro-environmental behavioral…

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to…

Abstract Oxidative stress has been shown to induce cell death in a wide range of human diseases including cardiac ischemia/reperfusion injury, drug induced cardiotoxicity, and heart failure. However, the mechanism of cell death induced by oxidative stress remains incompletely understood. Here we provide new evidence that oxidative stress primarily induces ferroptosis, but not apoptosis, necroptosis, or mitochondria-mediated necrosis, in cardiomyocytes. Intriguingly, oxidative stress induced by organic oxidants such as tert-butyl hydroperoxide (tBHP) and cumene hydroperoxide (CHP), but not hydrogen peroxide (H 2 O 2 ), promoted glutathione depletion and glutathione peroxidase 4 (GPX4) degradation in cardiomyocytes, leading to increased lipid peroxidation. Moreover, elevated oxidative stress is also linked to labile iron overload through downregulation of the transcription suppressor BTB and CNC homology 1 (Bach1), upregulation of heme oxygenase 1 (HO-1) expression, and enhanced iron release via heme degradation. Strikingly, oxidative stress also promoted HO-1 translocation to…

In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared…

The impact of chemical reaction and activation energy plays a vital role in the analysis of fluid dynamics and its thermal properties. The application of the flow of fluid is significantly considered in nuclear reactors, automobiles, manufacturing setups, electronic appliances etc. This study explores the impacts of activation energy and chemical reaction on the magnetohydrodynamic Darcy-Forchheimer squeezed Casson fluid flow through a porous material across the horizontal channel where the two parallel plates are assumed to be in motion. By using similarity variables, partial differential equations are converted to ordinary differential equations. Numerical method is applied using MATLAB to solve the problems and acquire the data for velocity field, thermal distribution, and concentration distribution. The graphs indicate that fluid velocity and temperature increases as the plates are brought closer. In addition, there was a correlation between a rise in the Hartmann number and a decrease in the fluid's velocity because of the existence of strong Lorentz forces. The temperature and…

Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda-Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision…

This paper presents a novel supervised convolutional neural network architecture, "DUCK-Net", capable of effectively learning and generalizing from small amounts of medical images to perform accurate segmentation tasks. Our model utilizes an encoder-decoder structure with a residual downsampling mechanism and a custom convolutional block to capture and process image information at multiple resolutions in the encoder segment. We employ data augmentation techniques to enrich the training set, thus increasing our model's performance. While our architecture is versatile and applicable to various segmentation tasks, in this study, we demonstrate its capabilities specifically for polyp segmentation in colonoscopy images. We evaluate the performance of our method on several popular benchmark datasets for polyp segmentation, Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LARIBPOLYPDB showing that it achieves state-of-the-art results in terms of mean Dice coefficient, Jaccard index, Precision, Recall, and Accuracy. Our approach demonstrates strong generalization capabilities, achieving…

Ukraine has been embroiled in an increasing war since February 2022. In addition to Ukrainians, the Russo-Ukraine war has affected Poles due to the refugee crisis and the Taiwanese, who are facing a potential crisis with China. We examined the mental health status and associated factors in Ukraine, Poland, and Taiwan. The data will be used for future reference as the war is still ongoing. From March 8 to April 26, 2022, we conducted an online survey using snowball sampling techniques in Ukraine, Poland, and Taiwan. Depression, anxiety, and stress were measured using the Depression, Anxiety, and Stress (DASS)-21 item scale; post-traumatic stress symptoms by the Impact of Event Scale-Revised (IES-R) and coping strategies by the Coping Orientation to Problems Experienced Inventory (Brief-COPE). We used multivariate linear regression to identify factors significantly associated with DASS-21 and IES-R scores. There were 1626 participants (Poland: 1053; Ukraine: 385; Taiwan: 188) in this study. Ukrainian participants reported significantly higher DASS-21 (p < 0.001) and IES-R (p < 0.01)…

Rapid urbanization, population growth, agricultural practices, and industrial activities have led to widespread groundwater contamination. This study evaluated heavy metal contamination in residential drinking water in Shiraz, Iran (2021). The analysis involved 80 groundwater samples collected across wet and dry seasons. Water quality was comprehensively assessed using several indices, including the heavy metals evaluation index (HEI), heavy metal pollution index (HPI), contamination degree (CD), and metal index (MI). Carcinogenic and non-carcinogenic risk assessments were conducted using deterministic and probabilistic approaches for exposed populations. In the non-carcinogenic risk assessment, the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) are employed. The precision of risk assessment was bolstered through the utilization of Monte Carlo simulation, executed using the R software platform. Based on the results, in both wet and dry seasons, Zinc (Zn) consistently demonstrates the highest mean concentration, followed by Manganese (Mn) and Chromium (Cr).…

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean…

ChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it can improve the moral judgment and decisions of users. Unfortunately, ChatGPT's advice is not consistent. Nonetheless, it does influence users' moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT corrupts rather than improves its users' moral judgment. While these findings call for better design of ChatGPT and similar bots, we also propose training to improve users' digital literacy as a remedy. Transparency, however, is not sufficient to enable the responsible use of AI.

Although extreme weather events recur periodically everywhere, the impacts of their simultaneous occurrence on crop yields are globally unknown. In this study, we estimate the impacts of combined hot and dry extremes as well as cold and wet extremes on maize, rice, soybean, and wheat yields using gridded weather data and reported crop yield data at the global scale for 1980-2009. Our results show that co-occurring extremely hot and dry events have globally consistent negative effects on the yields of all inspected crop types. Extremely cold and wet conditions were observed to reduce crop yields globally too, although to a lesser extent and the impacts being more uncertain and inconsistent. Critically, we found that over the study period, the probability of co-occurring extreme hot and dry events during the growing season increased across all inspected crop types; wheat showing the largest, up to a six-fold, increase. Hence, our study highlights the potentially detrimental impacts that increasing climate variability can have on global food production.

Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs…

Abstract Bacterial infections are one of the leading causes of death worldwide. In the case of topical bacterial infections such as wound infections, silver (Ag) has historically been one of the most widely used antibacterials. However, scientific publications have demonstrated the adverse effects of silver on human cells, ecotoxicity and insufficient antibacterial effect for the complete elimination of bacterial infections. The use of Ag in the form of nanoparticles (NPs, 1–100 nm) allows to control the release of antibacterial Ag ions but is still not sufficient to eliminate infection and avoid cytotoxicity. In this study, we tested the potency of differently functionalized copper oxide (CuO) NPs to enhance the antibacterial properties of Ag NPs. The antibacterial effect of the mixture of CuO NPs (CuO, CuO–NH 2 and CuO–COOH NPs) with Ag NPs (uncoated and coated) was studied. CuO and Ag NP combinations were more efficient than Cu or Ag (NPs) alone against a wide range of bacteria, including antibiotic-resistant strains such as gram-negative Escherichia coli and Pseudomonas…

The rising interest in carbon dots (c-dots) is driven by their remarkable potential in the field of biomedical applications. This is due to their distinctive and adjustable photoluminescence characteristics, outstanding physicochemical properties, excellent photostability, and biocompatibility. Herein, carbon dots were successfully produced via the heat synthesis method and characterization for physical and chemical properties using UV-Vis spectrophotometer, photoluminescence spectroscopy, Fourier Transform Infrared and Raman spectroscopy, Energy-dispersive X-ray analysis, and quantum yield. The resulting carbon dots exhibited a distinct blue fluorescence upon exposure to ultraviolet radiation with a 366 nm wavelength. The photoluminescence spectrum of carbon dots displayed a fluorescence peak around 470 nm when excited with a 325 nm wavelength. The synthesized carbon dots demonstrated thermal stability and maintained photoluminescence intensity under different pH conditions, including neutral and alkaline mediums, and good salt resistance ability. Raman spectroscopy confirmed the…

Nanotechnology is one of the fastest-growing markets, but developing eco-friendly products, their maximum production, stability, and higher yield is a challenge. In this study, silver nanoparticles were synthesized using an easily available resource, leaves extract of the Neem (Azadirachta indica) plant, as a reducing and capping agent, determined their effect on germination and growth of tomato plants. The maximum production of silver nanoparticles was noted at 70 °C after 3 h of reaction time while treating the 10 ml leaf extract of Neem plant with 10 ml of 1 mM silver nitrate. The impact of the extract preparation method and solvent type on the plant mediated fabrication of silver nanoparticles was also investigated. The UV-spectrophotometric analysis confirmed the synthesis of silver nanoparticles and showed an absorption spectrum within Δ420-440 nm range. The size of the fabricated silver nanoparticles was 22-30 nm. The functional groups such as ethylene, amide, carbonyl, methoxy, alcohol, and phenol attached to stabilize the nanoparticles were observed using the FTIR…

Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the lesions can be challenging and time-consuming, especially in resource-limited settings where laboratory tests may not be available. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown great potential in image recognition and classification tasks. To this end, this study proposes an approach using CNNs to classify monkeypox skin lesions. Additionally, the study optimized the CNN model using the Grey Wolf Optimizer (GWO) algorithm, resulting in a significant improvement in accuracy, precision, recall, F1-score, and AUC compared to the non-optimized model. The GWO optimization strategy can enhance the performance of CNN models on similar tasks. The optimized model achieved an impressive accuracy of 95.3%, indicating that the GWO optimizer has improved the model's ability to discriminate between positive and negative classes. The proposed approach…

Abstract The green methodologies of nanoparticles with plant extracts have received an increase of interest. Copper oxide nanoparticles (CuO NPs) have been utilized in a many of applications in the last few decades. The current study presents the synthesis of CuO NPs with aqueous extract of Morinda citrifolia as a stabilizing agent. The leaf extract of Morinda citrifolia was mixed with a solution of copper sulphate (CuSO 4 ·5H 2 O) and sodium hydroxide as a catalyst. UV–visible spectroscopy, FTIR, XRD, SEM, TEM, and EDAX analysis were performed to study the synthesized CuO NPs. Particle size distribution of the synthesized CuO NPs have been measured with dynamic light scattering. The CuO NPs synthesized were highly stable, sphere-like, and have size of particles from 20 to 50 nm. Furthermore, as-formed CuO NPs shown strong antibacterial activity against the Gram-positive bacteria ( Bacillus subtilis, and Staphylococcus aureus ), and Gram-negative bacteria ( Escherichia coli ). CuO NPs revealed a similar trend was analysed for antifungal activity. The zone of inhibition for the fungi…

Abstract In this study, three samples of commercial titanium dioxide nanoparticles (TiO 2 ) in different sizes were used to investigate their effect on the formulation of sunscreen creams. The aim was to evaluate their role in the performance of sunscreens (i.e. SPF, UVAPF, and critical wavelength). Then the particle size of these samples was determined by photon correlation spectroscopy methods. As a result, the size of primary particles was reduced by using milling and homogenization methods at different times. The results showed that the particle size of samples TA, TB, and TC in the ultrasonic homogenizer decreased from 966.4, 2745.8, and 2471.6 nm to 142.6, 254.8, and 262.8 nm, respectively. These particles were used in the pristine formulation. Then the functional characteristics of each formulation were determined by standard methods. TA had the best dispersion in cream compared to other samples due to its smaller size (i.e. 142.6 nm). For each formulation, two important parameters, including pH and TiO 2 dosage, were investigated in different states. The results showed that…

Abstract Clove and green Coffee (g-Coffee) extracts were used to synthesize green iron oxide nanoparticles, which were then used to sorb Cd 2+ and Ni 2+ ions out of an aqueous solution. Investigations with x-ray diffraction, Fourier-transform infrared spectroscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, nitrogen adsorption and desorption (BET), Zeta potential, and scanning electron microscopy were performed to know and understand more about the chemical structure and surface morphology of the produced iron oxide nanoparticles. The characterization revealed that the main component of iron nanoparticles was magnetite when the Clove extract was used as a reducing agent for Fe 3+ , but both magnetite and hematite were included when the g-Coffee extract was used. Sorption capacity for metal ions was studied as a function of sorbent dosage, metal ion concentration, and sorption period. The maximum Cd 2+ adsorption capacity was 78 and 74 mg/g, while that of Ni 2+ was 64.8 and 80 mg/g for iron nanoparticles prepared using Clove and g-Coffee, respectively.…

Characterization of surface wettability plays an integral role in physical, chemical, and biological processes. However, the conventional fitting algorithms are not suitable for accurate estimation of wetting properties, especially on hydrophilic surfaces, due to optical distortions triggered by changes in the focal length of the moving drops. Therefore, here we present an original setup coupled with Convolutional Neural Networks (CNN) for estimation of Contact Angle (CA). The developed algorithm is trained on 3375 ground truth images (at different front-lit illuminations), less sensitive to the edges of the drops, and retains its stability for images that are synthetically blurred with higher Gaussian Blurring (GB) values (GB: 0-22) if compared to existing goniometers (GB: 0-12). Besides, the proposed technique can precisely analyze drops of various colors and chemistries on different surfaces. Finally, our automated orthogonal camera goniometer has a significantly lower average standard deviation (6.7° vs. 14.6°) and coefficient of variation (14.9 vs. 29.2%) than the existing…

We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11-19, 2019 and March 01-06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio,…

Cyclooxygenase (COX) and Lipoxygenase (LOX) are essential enzymes for arachidonic acid (AA) to eicosanoids conversion. These AA-derived eicosanoids are essential for initiating immunological responses, causing inflammation, and resolving inflammation. Dual COX/5-LOX inhibitors are believed to be promising novel anti-inflammatory agents. They inhibit the synthesis of prostaglandins (PGs) and leukotrienes (LTs), but have no effect on lipoxin formation. This mechanism of combined inhibition circumvents certain limitations for selective COX-2 inhibitors and spares the gastrointestinal mucosa. Natural products, i.e. spice chemicals and herbs, offer an excellent opportunity for drug discovery. They have proven anti-inflammatory properties. However, the potential of a molecule to be a lead/ drug candidate can be much more enhanced if it has the property of inhibition in a dual mechanism. Synergistic activity is always a better option than the molecule's normal biological activity. Herein, we have explored the dual COX/5-LOX inhibition property of the three major potent phytoconsituents…

A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer's disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis. The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual's risk for the disease. Using the largest genome-wide association study of gut microbiota genera from the MiBioGen consortium, we used polygenic risk score (PRS) analyses with the "best-fit" model implemented in PRSice-2 and determined the genetic correlation between 119 genera and AD in a discovery sample (ADc12 case/control: 1278/1293). To confirm the results from the discovery sample, we next repeated the PRS analysis in a…

In this study we analyzed the flow, heat and mass transfer behavior of Casson nanofluid past an exponentially stretching surface under the impact of activation energy, Hall current, thermal radiation, heat source/sink, Brownian motion and thermophoresis. Transverse magnetic field with the assumption of small Reynolds number is implemented vertically. The governing partial nonlinear differential equations of the flow, heat and mass transfer are transformed into ordinary differential equations by using similarity transformation and solved numerically by using Matlab bvp4c package. The impact of each of the Hall current parameter, thermal radiation parameter, heat source/sink parameter, Brownian motion parameter, Prandtl number, thermophoresis parameter and magnetic parameter on velocity, concentration and temperature, is discussed through graphs. The skin friction coefficient along the x-and z-directions, the local Nusselt number and the Sherwood number are calculated numerically to look into the inside behavior of the emerging parameters. It is witnessed that the flow velocity is a…

Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acoustic data. With the advent of deep learning models, classification of important signals from these datasets has markedly improved. These models power critical data analyses for research and decision-making in biodiversity monitoring, animal behaviour studies, and natural resource management. However, deep learning models are often data-hungry and require a significant amount of labeled training data to perform well. While sufficient training data is available for certain taxonomic groups (e.g., common bird species), many classes (such as rare and endangered species, many non-bird taxa, and call-type) lack enough data to train a robust model from scratch. This study investigates the utility of feature embeddings extracted from audio classification models to identify bioacoustic classes other than the ones these models were originally trained on. We evaluate models on…

Abstract Industrial chemical pollutants such as methylene blue (MB) dye are released into the water body and potentially cause harm to the human and aquatic biosphere. Therefore, this study aims to synthesize eco-friendly nanocatalysts, i.e., reduced graphene oxide (rGO), zinc oxide (ZnO), and reduced graphene oxide-zinc oxide (rGO@ZnO) nanocomposites, for efficient photocatalytic degradation of MB dye. A graphite rod was obtained from waste dry cell batteries for the electrochemical exfoliation synthesis of graphene oxide (GO) and rGO. For the eco-friendly synthesis of ZnO and rGO@ZnO nanocatalysts, Croton macrostachyus leaf extract was used as a reducing and capping agent. The synthesized nanocatalysts were characterized using a UV–Vis spectrophotometer, Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy with energy-dispersive X-ray. The eco-friendly synthesized rGO, ZnO, and rGO@ZnO nanocatalysts were applied for the photocatalytic degradation of MB dye using direct sunlight irradiation. At optimum parameters, photocatalytic degradation…

The relationship between body mass index (BMI) and body fatness could differ according to age, sex, and race-ethnicity. We aimed to evaluate in which contexts BMI could be a good measure for body fatness compared to dual-energy X-ray absorptiometry (DXA) derived measures. The study population included 18,061 participants (9141 men and 8920 women) aged 18 and older who tested DXA from the National Health and Nutrition Examination Survey (NHANES) database from 1999 to 2006, and 8107 men and 10,754 women with DXA data from Korea NHANES from 2008 to 2011 to represent the Asian population. We calculated Pearson correlation coefficients between BMI and DXA derived fat mass index (FMI) and percentage body fat (PBF) depending on age, sex, and race-ethnicity. The correlation between BMI, FMI and PBF and obesity-related biomarkers was also estimated among the subgroup with both DXA and information on each biomarker. BMI was strongly correlated with FMI (r = 0.944 in men and 0.976 in women), PBF (r = 0.735 in men and 0.799 in women), and truncal fat mass (r = 0.914 in men and 0.941 in women)…

Attention often disengages from primary tasks in favor of secondary tasks (i.e., multitasking) and task-unrelated thoughts (i.e., mind wandering). We assessed whether attentional disengagement, in the context of a cognitive task, can substantially differ between samples from commonly used online participant recruitment platforms, Prolific and Mechanical Turk (MTurk). Initially, eighty participants were recruited through Prolific to perform an attention task in which the risk of losing points for errors was varied (high risk = 80% chance of loss, low risk = 20% chance of loss). Attentional disengagement was measured via task performance along with self-reported mind wandering and multitasking. On Prolific, we observed surprisingly low levels of disengagement. We then conducted the same experiment on MTurk. Strikingly, MTurk participants exhibited more disengagement than Prolific participants. There was also an interaction between risk and platform, with the high-risk group exhibiting less disengagement, in terms of better task performance, than the low-risk group, but only on MTurk.…

Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm-extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The…

Abstract In slowly progressive type 1 diabetes mellitus (SPIDDM), the pancreas shows sustained islet inflammation, pancreatitis, pancreatic acinar cell metaplasia/dysplasia (ADM), and intraepithelial neoplasia (PanIN), a precancerous lesion. The mechanisms underlying these changes remain unclear. The presence of enterovirus (EV) encoded-capsid protein 1 (VP1) and -2A protease (2A pro ) and the innate immune responses of the pancreas were studied using immunohistochemistry and in situ hybridization in 12 SPIDDM and 19 non-diabetic control pancreases. VP1, 2A pro , and EV-RNA were detected in islets and the exocrine pancreas in all SPIDDM pancreases. Innate immune receptor, melanoma differentiation-associated gene 5 (MDA5), and interferon (IFN)-beta1 were intensified in the islets of SPIDDM patients with short disease duration. However, expressions of MDA5 and IFN-beta1were suppressed in those with longer disease duration. CD3 + T cell infiltration was observed in the VP1- and insulin-positive islets (insulitis) and exocrine acinar cells. CD11c + dendritic cells (DCs) in islets were…

Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively "replaying" real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP-a tool for explaining black-box models'…

Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.

Fetal bovine serum (FBS) is a natural medium used in cell cultures containing the large amount of nutrients necessary for cell growth and is often used for in vitro cultures of animal cells. Although FBS plays a vital role in cell cultures, there are small molecules contained within FBS that remain unidentified, and their effects on cultured cells is poorly understood. Here, we report that different brands of FBS have varying influences on the background expression of IL-8, not TNFα and IL1β in epithelial cells. The endogenous small molecules in FBS and ERK pathways may contribute to these effects. In addition, FBS form the IL-8 stimulation and IL-8 non-responsive groups have different metabolome profiles. Overall, our study suggests that metabolites in FBS should be included in the quantitative considerations when conducting cell experiments, especially immune-related experiments, to improve the repeatability of experimental results in scientific papers; IL-8 could thus be an important factor in selecting FBS.

Abstract Introduction The rapid progress in artificial intelligence, machine learning, and natural language processing has led to the emergence of increasingly sophisticated large language models (LLMs) enabling their use in various applications, including medicine and healthcare. Objectives The study aimed to evaluate the performance of two LLMs: ChatGPT (based on GPT-3.5) and GPT-4, on the Medical Final Examination (MFE). Methods The models were tested on three editions of the MFE from: Spring 2022, Autumn 2022, and Spring 2023 in two language versions – English and Polish. The accuracies of both models were compared and the relationships between the correctness of answers with the index of difficulty and discrimination power index were investigated. Results The study demonstrated that GPT-4 outperformed GPT-3.5 in all three examinations regardless of the language used. GPT-4 achieved mean accuracies of 80.7% for Polish and 79.6% for English, passing all MFE versions. GPT-3.5 had mean accuracies of 56.6% for Polish and 58.3% for English, passing 2 of 3 Polish versions and all 3…

Drug development is a time-consuming and expensive process, given the low success rate of clinical trials. Now, anticancer drug developments have shifted to three-dimensional (3D) models which are more likely to mimic tumor behavior compared to traditional two-dimensional (2D) cultures. A comparative study among different aspects was conducted between 2D and 3D cultures using colorectal cancer (CRC) cell lines, in addition, Formalin-Fixed Paraffin-Embedded (FFPE) block samples of patients with CRC were used for evaluation. Compared to the 2D culture, cells grown in 3D displayed significant (p < 0.01) differences in the pattern of cell proliferation over time, cell death phase profile, expression of tumorgenicity-related genes, and responsiveness to 5-fluorouracil, cisplatin, and doxorubicin. Epigenetically, 3D cultures and FFPE shared the same methylation pattern and microRNA expression, while 2D cells showed elevation in methylation rate and altered microRNA expression. Lastly, transcriptomic study depending on RNA sequencing and thorough bioinformatic analyses showed significant…

Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of a stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. The 'unknown' is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has the potential…

Abstract In today’s race to find ways to produce cheap and green hydrogen, the natural hydrogen wells in Bourakebougou offer a promising solution and are a good example of how H 2 can be produced in the natural environment. Not only has one well been successfully exploited to generate electricity for the local village, but twenty-four other exploratory boreholes have also demonstrated the presence of natural H 2 in the surrounding area. The Bourakebougou H 2 field offers a unique opportunity for geoscientists to determine the key characteristics of natural hydrogen reservoirs. This paper presents the coring, logging, and geochemistry studies that were performed to better characterize the nature of the Bourakebougou H 2 -bearing reservoirs. The shallowest main reservoir, in which there is the highest content of H 2 , is made of dolomitic carbonate (Neoproterozoic cap carbonate). These carbonates are largely karstified and show a high degree of heterogeneity in porosity (0.21–14.32%). Based on the analysis of the drilling imagery of the carbonated reservoirs, the accumulation of…

As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to identify the nonlinearities and interactions emergent from such complexity, especially across broad swaths of parameter space. This limits understanding of the ecological mechanisms underlying model behavior. Machine learning approaches are a potential answer to this issue, given their predictive ability when applied to complex large datasets. While perceptions that machine learning is a "black box" linger, we seek to illuminate its interpretive potential in ecological modeling. To do so, we detail our process of applying random forests to complex model dynamics to produce both high predictive accuracy and elucidate the ecological mechanisms driving our predictions. Specifically, we employ an empirically rooted ontogenetically stage-structured consumer-resource simulation model. Using simulation parameters as feature inputs and simulation output as dependent variables in our random forests, we extended feature analyses into a simple…

There has been an increasing interest in the long-term impact of long COVID. However, only a few studies have investigated the clinical manifestations of long COVID after 24 months of acute infection. In this study, prospective online surveys were conducted in adults previously diagnosed with coronavirus disease 2019 (COVID-19) in South Korea between February 13 and March 13, 2020, at 6, 12, and 24 months after COVID-19. We investigated self-reported symptoms and the EuroQol-5-dimension index. Among 900 individuals enrolled initially, 150 completed all 3 surveys. After excluding the cases of COVID-19 reinfection, 132 individuals were included in the final analysis. Among the 132 participants, 94 (71.2%) experienced symptoms of long COVID. The most frequently reported symptoms were fatigue (34.8%), amnesia (30.3%), concentration difficulties (24.2%), insomnia (20.5%), and depression (19.7%). Notably, no significant differences were noted in the incidence of long COVID at 24 months in terms of the number of vaccinations received. Although the neuropsychiatric quality of life improved…

Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. These systems cannot generalize well to unseen data. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. The…

Understanding the effects of climate change and anthropogenic activities on the hydrogeomorpholgical parameters in wetlands ecosystems is vital for designing effective environmental protection and control protocols for these natural capitals. This study develops methodological approach to model the streamflow and sediment inputs to wetlands under the combined effects of climate and land use / land cover (LULC) changes using the Soil and Water Assessment Tool (SWAT). The precipitation and temperature data from General Circulation Models (GCMs) for different Shared Socio-economic Pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downscaled and bias-corrected with Euclidean distance method and quantile delta mapping (QDM) for the case of the Anzali wetland watershed (AWW) in Iran. The Land Change Modeler (LCM) is adopted to project the future LULC at the AWW. The results indicate that the precipitation and air temperature across the AWW will decrease and increase, respectively, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Streamflow and sediment loads will…

Abstract For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization.

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to…

The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random Forest were applied to identify the best models for heart disease prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, and PRC were measured to compare feature selection methods' effect on prediction algorithms. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods (e.g., j48), whereas it led to a decrease in model performance in other models (e.g. MLP, RF). SVM-based filtering methods have a best-fit accuracy of 85.5. In fact, in a best-case scenario, filtering methods result in + 2.3 model accuracy. SVM-CFS/information gain/Symmetrical uncertainty methods have the…

Alcohol Use Disorder (AUD) contributes significantly to global mortality. GLP-1 (Glucagon-like peptide-1) and GLP-1/GIP (Glucose-dependent Insulinotropic Polypeptide) agonists, FDA-approved for managing type 2 diabetes and obesity, where the former has shown to effectively reduce the consumption of alcohol in animal models but no reports exist on the latter. In this report, we conducted two studies. In the first study, we conducted an analysis of abundant social media texts. Specifically, a machine-learning based attribution mapping of ~ 68,250 posts related to GLP-1 or GLP-1/GIP agonists on the Reddit platform. Secondly, we recruited participants (n = 153; current alcohol drinkers; BMI ≥ 30) who self-reported either taking Semaglutide (GLP-1 agonist), Tirzepatide (the GLP-1/GIP combination) for ≥ 30 days or, as a control group; no medication to manage diabetes or weight loss for a within and between subject remote study. In the social media study, we report 8 major themes including effects of medications (30%); diabetes (21%); and Weight loss and obesity (19%). Among the…

Artificial intelligence has been successfully applied in various fields, one of which is computer vision. In this study, a deep neural network (DNN) was adopted for Facial emotion recognition (FER). One of the objectives in this study is to identify the critical facial features on which the DNN model focuses for FER. In particular, we utilized a convolutional neural network (CNN), the combination of squeeze-and-excitation network and the residual neural network, for the task of FER. We utilized AffectNet and the Real-World Affective Faces Database (RAF-DB) as the facial expression databases that provide learning samples for the CNN. The feature maps were extracted from the residual blocks for further analysis. Our analysis shows that the features around the nose and mouth are critical facial landmarks for the neural networks. Cross-database validations were conducted between the databases. The network model trained on AffectNet achieved 77.37% accuracy when validated on the RAF-DB, while the network model pretrained on AffectNet and then transfer learned on the RAF-DB results in…

In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh's total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2.8 million ha) of the cropland was affected out of the 4.9 million ha of agricultural area in Sindh.…

Silicon nanoparticles (Si-NPs) have shown their potential for use in farming under water-deficient conditions. Thus, the experiment was accomplished to explore the impacts of seed priming of Si-NPs on wheat (Triticum aestivum L.) growth and yield under different drought levels. The plants were grown in pots under natural ecological environmental conditions and were harvested on 25th of April, 2020. The results revealed that seed priming of Si-NPs (0, 300, 600, and 900 mg/L) suggestively improved, the spike length, grains per spike, 1000 grains weight, plant height, grain yield, and biological yield by 12-42%, 14-54%, 5-49%, 5-41%, 17-62%, and 21-64%, respectively, relative to the control. The Si-NPs improved the leaf gas trade ascribes and chlorophyll a and b concentrations, though decreased the oxidative pressure in leaves which was demonstrated by the diminished electrolyte leakage and upgrade in superoxide dismutase and peroxidase activities in leaf under Si-NPs remedies over the control. The outcomes proposed that Si-NPs could improve the yield of wheat under a dry spell. In…

Cancer-related fatigue (CRF) affects therapeutic compliance and clinical outcomes including recurrence and mortality. This study aimed to comprehensively and comparatively assess the severity-based prevalence of CRF. From two public databases (PubMed and Cochrane Library), we extracted data containing information on both prevalence and severity of fatigue in cancer patients through December 2021. We conducted a meta-analysis to produce point estimates using random effects models. Subgroup analyses were used to assess the prevalence and severity by the organ/system tumor development, treatment phase, therapeutic type, sex and assessment method. A total of 151 data (57 studies, 34,310 participants, 11,805 males and 22,505 females) were selected, which indicated 43.0% (95% CI 39.2-47.2) of fatigue prevalence. The total CRF prevalence including 'mild' level of fatigue was 70.7% (95% CI 60.6-83.3 from 37 data). The prevalence of 'severe' fatigue significantly varied by organ/system types of cancer origin (highest in brain tumors 39.7% vs. lowest in gynecologic tumors 3.9%) and treatment…

Abstract High-performance, room temperature-based novel sensing materials are one of the frontier research topics in the gas sensing field, and MXenes, a family of emerging 2D layered materials, has gained widespread attention due to their distinctive properties. In this work, we propose a chemiresistive gas sensor made from V 2 CT x MXene-derived, urchin-like V 2 O 5 hybrid materials (V 2 C/V 2 O 5 MXene) for gas sensing applications at room temperature. The as-prepared sensor exhibited high performance when used as the sensing material for acetone detection at room temperature. Furthermore, the V 2 C/V 2 O 5 MXene-based sensor exhibited a higher response (S% = 11.9%) toward 15 ppm acetone than pristine multilayer V 2 CT x MXenes (S% = 4.6%). Additionally, the composite sensor demonstrated a low detection level at ppb levels (250 ppb) at room temperature, as well as high selectivity among different interfering gases, fast response-recovery time, good repeatability with minimal amplitude fluctuation, and excellent long-term stability. These improved sensing properties can be…

Diabetes is a growing epidemic and a major threat to most of the households in India. Yet, there is little evidence on the extent of awareness, treatment, and control (ATC) among adults in the country. In this study, we estimate the prevalence and ATC of diabetes among adults across various sociodemographic groups and states of India. We used data on 2,078,315 individuals aged 15 years and over from the recent fifth round, the most recent one, of the National Family Health Survey (NFHS-5), 2019-2021, that was carried out across all the states of India. Diabetic individuals were identified as those who had random blood glucose above 140 mg/dL or were taking diabetes medication or has doctor-diagnosed diabetes. Diabetic individuals who reported diagnosis were labelled as aware, those who reported taking medication for controlling blood glucose levels were labelled as treated and those whose blood glucose levels were < 140 mg/dL were labelled as controlled. The estimates of prevalence of diabetes, and ATC were age-sex adjusted and disaggregated by household wealth quintile, education,…

Abstract The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM 10 , in the latter sets does not improve overall performance, potentially due to multicollinearity between PM 10 and PM 2.5 variables. The proposed methodology provides valuable insights…

Abstract Copper Zinc Tin Sulphide (CZTS) is a propitious semiconductor for active absorber material in thin-film solar cells (SCs). Here, SC architecture comprising FTO/ZnS/CZTS/variable HTLs/Au is discussed. Fluorine-doped tin oxide (FTO) and gold (Au) are used as front and back contacts, respectively. Zinc sulphide (ZnS) is used as an active electron transport layer (ETL), while different Cu-based materials (Cu2O, CuO, CuI, and CuSCN) are used as hole transport layers (HTL). A one-dimensional solar cell capacitance simulator (SCAPS-1D) is utilized to simulate the SC structure. Among different Cu-based HTLs, Cu2O is preferred as a potential candidate for high cell performance of CZTS-based SC. The effects of various layer parameters such as thickness, doping density, and carrier concentrations, electron affinity of HTL and absorber, respectively, are also discussed. After optimization of the device, variation of operating temperature and the effect of series and shunt resistance are also taken into consideration. The optimized results of thickness and acceptor concentration (NA) of…

Abstract This study aimed to assess the UV-shielding features of the PMMA-based thin film coatings with the addition of TiO 2 and ZnO nanoparticles as nanofillers considering different contents. Furthermore, the effect of TiO 2 /ZnO nanohybrids at different ratios and concentrations was examined. The XRD, FTIR, SEM, and EDX analyses characterized the prepared films' functional groups, structure, and morphology. Meanwhile, the coatings' optical properties and UV-protecting capability were investigated by ultraviolet–visible (UV–Vis) spectroscopy. The UV–Vis spectroscopic study revealed that as the concentration of nanoparticles increased in the hybrid-coated PMMA, the absorption in the UVA region increased. Overall, it can be concluded that the optimal coatings for PMMA were 0.1 wt% TiO 2 , 0.1 wt% ZnO, and 0.025:0.025 wt% TiO 2 : ZnO nanohybrid. Considering the acquired FT-IR of PMMA with different content of nanoparticles before and after exposure to the UV irradiation, for some films, it was confirmed that the polymer-based thin films degraded after 720 h, with either decreasing…

Microplastics (MPs) are now a global issue due to increased plastic production and use. Recently, various studies have been performed in response to the human health risk assessment. However, these studies have focused on spherical MPs, which have smooth edges and a spherical shape and account for less than 1% of MPs in nature. Unfortunately, studies on fragment-type MPs are very limited and remain in the initial stages. In this study, we studied the effect that 16.4 µm fragment type polypropylene (PP) MPs, which have an irregular shape and sharp edges and form naturally in the environment, had on breast cancer. The detrimental effects of PPMPs on breast cancer metastasis were examined. Here, 1.6 mg/ml of PPMP, which does not induce cytotoxicity in MDA-MB-231, was used, and at this concentration, PPMP did not induce morphological changes or cellular migrating in the MDA-MB-231 and MCF-7 cells. However, PPMP incubation for 24 hours in the MDA-MB-231 cells significantly altered the level of cell cycle-related transcripts in an RNA-seq analysis. When confirmed by qRT-PCR, the gene…

Moran's index is an important spatial statistical measure used to determine the presence or absence of spatial autocorrelation, thereby determining the selection orientation of spatial statistical methods. However, Moran's index is chiefly a statistical measurement rather than a mathematical model. This paper is devoted to establishing spatial autocorrelation models by means of linear regression analysis. Using standardized vector as independent variable, and spatial weighted vector as dependent variable, we can obtain a set of normalized linear autocorrelation equations based on quadratic form and vector inner product. The inherent structure of the models' parameters are revealed by mathematical derivation. The slope of the equation gives Moran's index, while the intercept indicates the average value of standardized spatial weight variable. The square of the intercept is negatively correlated with the square of Moran's index, but omitting the intercept does not affect the estimation of the slope value. The datasets of a real urban system are taken as an example to verify the…

This article's innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between a mother and her children. The real inspiration of MOA is to simulate the mother's care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The findings of optimizing unimodal functions indicate MOA's high ability in local search and exploitation. The findings of optimization of high-dimensional multimodal functions indicate the high ability of MOA in global search and exploration. The findings of optimization of fixed-dimension multi-model functions and the CEC 2017 test suite show that MOA with a high ability to balance exploration and exploitation effectively supports the search process and can generate appropriate…

Determining whether SARS-CoV-2 exhibits seasonality like other respiratory viruses is critical for public health planning. We evaluated whether COVID-19 rates follow a seasonal pattern using time series models. We used time series decomposition to extract the annual seasonal component of COVID-19 case, hospitalization, and mortality rates from March 2020 through December 2022 for the United States and Europe. Models were adjusted for a country-specific stringency index to account for confounding by various interventions. Despite year-round disease activity, we identified seasonal spikes in COVID-19 from approximately November through April for all outcomes and in all countries. Our results support employing annual preventative measures against SARS-CoV-2, such as administering seasonal booster vaccines in a similar timeframe as those in place for influenza. Whether certain high-risk individuals may need more than one COVID-19 vaccine booster dose each year will depend on factors like vaccine durability against severe illness and levels of year-round disease activity.

Drought stress during the reproductive stage and declining soybean yield potential raise concerns about yield loss and economic return. In this study, ten cultivars were characterized for 20 traits to identify reproductive stage (R1-R6) drought-tolerant soybean. Drought stress resulted in a marked reduction (17%) in pollen germination. The reduced stomatal conductance coupled with high canopy temperature resulted in reduced seed number (45%) and seed weight (35%). Drought stress followed by rehydration increased the hundred seed weight at the compensation of seed number. Further, soybean oil decreased, protein increased, and cultivars responded differently under drought compared to control. In general, cultivars with high tolerance scores for yield displayed lower tolerance scores for quality content and vice versa. Among ten cultivars, LS5009XS and G4620RX showed maximum stress tolerance scores for seed number and seed weight. The observed variability in leaf reflectance properties and their relationship with physiological or yield components suggested that leaf-level sensing…

Robot assisted surgery (RAS) provides medical practitioners with valuable tools, decreasing strain during surgery and leading to better patient outcomes. While the loss of haptic sensation is a commonly cited disadvantage of RAS, new systems aim to address this problem by providing artificial haptic feedback. N = 56 papers that compared robotic surgery systems with and without haptic feedback were analyzed to quantify the performance benefits of restoring the haptic modality. Additionally, this study identifies factors moderating the effect of restoring haptic sensation. Overall results showed haptic feedback was effective in reducing average forces (Hedges' g = 0.83) and peak forces (Hedges' g = 0.69) applied during surgery, as well as reducing the completion time (Hedges' g = 0.83). Haptic feedback has also been found to lead to higher accuracy (Hedges' g = 1.50) and success rates (Hedges' g = 0.80) during surgical tasks. Effect sizes on several measures varied between tasks, the type of provided feedback, and the subjects' levels of surgical expertise, with higher levels of…

In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called "Brain-Diffuser". In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate…

The prescription of psychotropic drugs has been rising in Europe over the last decade. This study provides a comprehensive profile of prepandemic consumption patterns of antidepressant, antipsychotic, and anxiolytic drugs in Portugal considering full nationwide psychotropic drug prescription and dispensing records (2016-2019) against several criteria, including active ingredient, sociodemographics, medical specialty, and incurred costs. An increase of 29.6% and 34.7% in the consumption of antipsychotics and antidepressants between 2016 and 2019 is highlighted, accompanied by an increase of 37M Eur in total expenditure (> 20M Eur in public copay) for these classes of drugs. Disparities in sociodemographic and geographical incidence are identified. Amongst other pivotal results, 64% of psychotropic drug prescriptions are undertaken by general practitioners, while only 21% undertaken by neurological and psychiatric specialties. Nationwide patterns of psychotropic drug prescription further reveal notable trends and determinants, establishing a reference point for cross-regional studies…

Vibrio vulnificus is an opportunistic bacterial pathogen, occurring in warm low-salinity waters. V. vulnificus wound infections due to seawater exposure are infrequent but mortality rates are high (~ 18%). Seawater bacterial concentrations are increasing but changing disease pattern assessments or climate change projections are rare. Here, using a 30-year database of V. vulnificus cases for the Eastern USA, changing disease distribution was assessed. An ecological niche model was developed, trained and validated to identify links to oceanographic and climate data. This model was used to predict future disease distribution using data simulated by seven Global Climate Models (GCMs) which belong to the newest Coupled Model Intercomparison Project (CMIP6). Risk was estimated by calculating the total population within 200 km of the disease distribution. Predictions were generated for different "pathways" of global socioeconomic development which incorporate projections of greenhouse gas emissions and demographic change. In Eastern USA between 1988 and 2018, V. vulnificus wound infections…

Abstract The band gap of rutile TiO 2 has been narrowed, via the formation of oxygen vacancies (OVs) during heat treatment in carbon powder (cHT) with embedding TiO 2 coatings. The narrowed band gap efficiently improves the visible light response of TiO 2 coatings, to further enhance the visible-light-driven photocatalytic activity. The change in OVs during cHT has been studied by manipulation of cHT temperature and time. The effect of OVs on the band structure of nonstoichiometric TiO 2- x has been further calculated by first-principles calculations. With raising the temperature, SEM images show that the nano-size fiber-like structure forms on the surface of TiO 2 coatings, and the amount of the fiber-like structure significantly increases and their size changes from nano to micro under 800 °C, contributing to cause an increase in accessible surface area. The UV–Vis results reveal that the band gap of TiO 2 has been narrowed during cHT, due to the formed oxygen vacancies. The XPS results further confirm that the formation of surface defects including OVs, and the XPS depth profile…

In the digital economy, the relationship between digital transformation and a company's total factor productivity has profound implications for high-quality business development. Heavy polluters are given more environmental responsibility because of their high pollution and emission characteristics. This paper analyses the theoretical framework for the impact of digital transformation on the total factor productivity of heavily polluting firms. Using a sample of Shanghai and Shenzhen A-share heavy polluters from 2010 to 2020, we explore how the digital transformation of heavy polluters affects the total factor productivity of firms. The study found that the digital transformation of heavily polluting companies can effectively improve total factor productivity, internally by increasing their level of green technology innovation and externally by increasing their willingness and capacity for corporate social responsibility. At the same time, digital transformation can improve total factor productivity by reducing cost stickiness, revealing the "black box" in which digital…

Abstract The potential for malachite green dye saturated effluent to severely affect the environment and human health has prompted the search for effective treatment technologies. Thus, this study was conducted with the goal of developing activated carbon from Rumex abyssinicus for the adsorptive removal of malachite green dye from an aqueous solution. Unit operations such as drying, size reduction, impregnation with H3PO4, and thermal activation were used during the preparation of the activated carbon. An experiment was designed considering four main variables at their respective three levels: initial dye concentration (50, 100, and 150 mg/L), pH (3, 6, and 9), contact period (20, 40, and 60 min), and adsorbent dosage (0.05, 0.01, and 0.15 g/100 mL). Optimization of the batch adsorption process was carried out using the Response Surface methodology's Box Behnken approach. The characterization of the activated carbon was described by SEM for surface morphology with cracks and highly porous morphology, FTIR for multi-functional groups O–H at 3506.74 cm−1 and 3290.70 cm−1, carbonyl…

Hydrolysis and aminolysis are two main commonly used chemical methods for surface modification of hydrophobic tissue engineering scaffolds. The type of chemical reagents along with the concentration and treatment time are main factors that determine the effects of these methods on biomaterials. In the present study, electrospun poly (ℇ-caprolactone) (PCL) nanofibers were modified through hydrolysis and aminolysis. The applied chemical solutions for hydrolysis and aminolysis were NaOH (0.5-2 M) and hexamethylenediamine/isopropanol (HMD/IPA, 0.5-2 M) correspondingly. Three distinct incubation time points were predetermined for the hydrolysis and aminolysis treatments. According to the scanning electron microscopy results, morphological changes emerged only in the higher concentrations of hydrolysis solution (1 M and 2 M) and prolonged treatment duration (6 and 12 h). In contrast, aminolysis treatments induced slight changes in the morphological features of the electrospun PCL nanofibers. Even though surface hydrophilicity of PCL nanofibers was noticeably improved through the both…

Given the limited availability and accessibility of onsite cognitive behavioral therapy for insomnia (CBT-I), other CBT-I settings, such as internet-delivered CBT-I (iCBT-I), have been proposed. The primary aim of the study was to compare the efficacy of available CBT-I settings on insomnia severity. A systematic review and frequentist network meta-analysis of available CBT-I settings was performed. PsycINFO, PsycARTICLES, MEDLINE, PubMed, and CINAHL were searched for randomized controlled trials (RCTs) investigating any CBT-I settings in adults with insomnia disorder. The systematic literature search (3851 references) resulted in 52 RCTs. For the primary outcome insomnia severity, all examined CBT-I settings except smartphone-delivered CBT-I yielded significant effects when compared to WL. Large standardized mean differences were found for individual onsite CBT-I (- 1.27;95%CI - 1.70, - 0.84), group-delivered CBT-I (- 1.00;95%CI - 1.42. - 0.59), telehealth (- 1.28;95%CI - 2.06, - 0.50), and guided bibliotherapy (- 0.99;95%CI - 1.67, - 0.32). Both guided iCBT-I (- 0.71;95%CI - 1.18,…

Abstract A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest operational frequency, with multiple absorption peaks at 1.89, 4.15, 5.32, 5.84, 7.04, 8.02, and 8.13 THz. The impedance matching theory and electric field distribution are investigated to understand the physical mechanism of hepta-band absorption. The sensing functionality is evaluated using a surrounding medium with a refractive index between 1 and 1.1, resulting in good Quality factor (Q) value of 117. The proposed sensor has the highest sensitivity of 4.72 THz/RIU for glucose detection. Extreme randomized tree (ERT) model is utilized to predict absorptivities for intermediate frequencies with unit cell dimensions, substrate thickness, angle variation, and refractive index values to reduce simulation time. The effectiveness of the ERT model in predicting absorption values is evaluated using the Adjusted R2 score, which is…

Sustainable Development Goal (SDG) 13 refers to "Climate Action". It is one of the 17 goals established by the United Nations in their 2030 Agenda for Sustainable Development. The primary objective of SDG13 is to take urgent action to combat climate change and its impacts. It recognises that climate change is a global challenge that requires immediate attention and concerted efforts from governments, businesses, communities, and individuals worldwide. SDG13 permeates a number of SDGs and also influences them in a significant way. Based on the need to contextualise SDG13 and considering its role as one of the central SDGs, this article outlines the links between SDG13 and the other SDGs. It also reports on a survey involving experts from 61 countries. The findings suggest that even though climate change impacts, particularly extreme weather events, are known to disproportionally affect poorer and minoritized communities, the synergies among related goals and climate justice seem to receive less attention. The article concludes by describing some of the means via which synergies…

This study evaluated the long-term myopia control effect and safety in children wearing Defocus Incorporated Multiple Segments (DIMS) spectacle lenses. Participants who completed the 2-year RCT were followed for a total of 6 years; their cycloplegic refractions and axial length were measured. Group 1 (n = 36) wore DIMS spectacles for 6 years; Group 2 (n = 14) wore DIMS lens for the first 3.5 years and SV spectacles afterwards; Group 3 (n = 22) wore SV spectacles in the first 2 years and switched to DIMS; Group 4 (n = 18) wore SV spectacles in the first 2 years, switched to DIMS for 1.5 years and then SV spectacles again. Group 1 showed no significant differences in myopia progression (- 0.52 ± 0.66 vs. - 0.40 ± 0.72D) and axial elongation (0.32 ± 0.26 vs. 0.28 ± 0.28 mm, both p > 0.05) between the first and the later 3 years. In the last 2.5 years, DIMS lens groups (Groups 1 and 3) had less myopia progression and axial elongation than the single vision groups (Groups 2 and 4). There was no evidence of rebound after stopping the treatment. Post-wear visual functions in all groups…

Abstract Formation of electrogenic microbial biofilm on the electrode is critical for harvesting electrical power from wastewater in microbial biofuel cells (MFCs). Although the knowledge of bacterial community structures in the biofilm is vital for the rational design of MFC electrodes, an in-depth study on the subject is still awaiting. Herein, we attempt to address this issue by creating electrogenic biofilm on modified graphite anodes assembled in an air–cathode MFC. The modification was performed with reduced graphene oxide (rGO), polyaniline (PANI), and carbon nanotube (CNTs) separately. To accelerate the growth of the biofilm, soybean-potato composite (plant) powder was blended with these conductive materials during the fabrication of the anodes. The MFC fabricated with PANI-based anode delivered the current density of 324.2 mA cm −2 , followed by CNTs (248.75 mA cm −2 ), rGO (193 mA cm −2 ), and blank (without coating) (151 mA cm −2 ) graphite electrodes. Likewise, the PANI-based anode supported a robust biofilm growth containing maximum bacterial cell densities with diverse…

The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26…

Abstract Interleukin (IL)-33 is a broad-acting alarmin cytokine that can drive inflammatory responses following tissue damage or infection and is a promising target for treatment of inflammatory disease. Here, we describe the identification of tozorakimab (MEDI3506), a potent, human anti-IL-33 monoclonal antibody, which can inhibit reduced IL-33 (IL-33 red ) and oxidized IL-33 (IL-33 ox ) activities through distinct serum-stimulated 2 (ST2) and receptor for advanced glycation end products/epidermal growth factor receptor (RAGE/EGFR complex) signalling pathways. We hypothesized that a therapeutic antibody would require an affinity higher than that of ST2 for IL-33, with an association rate greater than 10 7 M −1 s −1 , to effectively neutralize IL-33 following rapid release from damaged tissue. An innovative antibody generation campaign identified tozorakimab, an antibody with a femtomolar affinity for IL-33 red and a fast association rate (8.5 × 10 7 M −1 s −1 ), which was comparable to soluble ST2. Tozorakimab potently inhibited ST2-dependent inflammatory responses driven by IL-33…