The 100 most-cited articles in Scientific Reports, 2022

Abstract The work mainly focused on a validation of the method for determining the content of salicylic acid and individual unknown impurities in new pharmaceutical product—tablets containing: 75, 100 or 150 mg of acetylsalicylic acid and glycine in the amount of 40 mg for each dosage. The separation of the components was carried out by means of HPLC, using a Waters Symmetry C18 column (4.6 × 250 mm, 5 μm) as the stationary phase. The mobile phase consisted of a mixture of 85% orthophosphoric acid, acetonitrile and purified water (2:400:600 V/V/V). Detection was carried out at a wavelength of 237 nm, with a constant flow rate of 1.0 ml min −1 . In order to verify the method, linearity, precision (repeatability and reproducibility), accuracy, specificity, range, robustness, system precision, stability of the test and standard solution, limit of quantification and forced degradation were determined. Validation tests were performed in accordance with ICH (International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use) guidelines.…

Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

Abstract Zinc oxide nanoparticles (ZnO NPs) are one of the most widely used nanoparticulate materials due to their antimicrobial properties, but their main mechanism of action (MOA) has not been fully elucidated. The study characterized ZnO NPs using X-ray diffraction, FT-IR spectroscopy and scanning electron microscopy. Antimicrobial activity of clinically bacteria Escherichia coli, Staphylococcus aureus, Bacillus subtilis and Pseudomonas aeruginosa was evaluated by REMA after exposure to the ZnO NP at concentrations from 0.2 to 1.4 mM. Sensitivity was achieved at 0.6 mM for the Gram-negatives and 1.0 mM for Gram-positives cells. The effect of ZnO NPs on the membrane integrity and in the interference of cell division was investigated by its effect on the divisional ring, through fluorescence microscopy assays using B. subtilis (amy::pspac-ftsZ-gfpmut1) expressing FtsZ-GFP. Results showed that ZnO NPs did not interfere with the assembly of the divisional Z-ring. However, 70% of the cells showed damage in the cytoplasmic membrane after 15 min of exposure to the ZnO NPs. Electrostatic…

Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge. The k-nearest neighbour (KNN) algorithm is the most frequently used among the wide range of machine learning algorithms. This paper presents a study on different KNN variants (Classic one, Adaptive, Locally adaptive, k-means clustering, Fuzzy, Mutual, Ensemble, Hassanat and Generalised mean distance) and their performance comparison for disease prediction. This study analysed these variants in-depth through implementations and experimentations using eight machine learning benchmark datasets obtained from Kaggle, UCI Machine learning repository and OpenML. The datasets were related to different disease contexts. We considered the performance measures of accuracy, precision and recall for comparative analysis. The average accuracy values of these variants ranged from 64.22% to 83.62%. The Hassanaat KNN showed the highest average accuracy (83.62%), followed by the ensemble approach KNN (82.34%). A relative performance index is also…

There is increased interest in the terrestrial silicon cycle in the last decades as its different compounds and species have large implications for ecosystem performance in terms of soil nutrient and water availability, ecosystem productivity as well as ecological aspects such as plant-microbe and plant-animal feedbacks. The currently existing analytical methods are limited. Fourier-transform infrared spectroscopy (FTIR) analysis is suggested being a promising tool to differentiate between the different Si species. We report here on the differentiation of varying Si-species/Si-binding (in synthetic material) using FTIR-analyses. Therefore, we collected FTIR-spectra of five different amorphous silica, Ca-silicate, sodium silicate (all particulate), a water-soluble fraction of amorphous silica and soil affected by volcanic activity and compared their spectra with existing data. A decrease of the internal order of the materials analyzed was indicated by peak broadening of the Si-O-Si absorption band. Peak shifts at this absorption band were induced by larger ions incorporated in the…

Abstract The global agri-food system relies on synthetic nitrogen (N) fertilisation to increase crop yields, yet the use of synthetic N fertiliser is unsustainable. In this study we estimate global greenhouse (GHG) emissions due to synthetic N fertiliser manufacture, transportation, and field use in agricultural systems. By developing the largest field-level dataset available on N 2 O soil emissions we estimate national, regional and global N 2 O direct emission factors (EFs), while we retrieve from the literature the EFs for indirect N 2 O soil emissions, and for N fertiliser manufacturing and transportation. We find that the synthetic N fertiliser supply chain was responsible for estimated emissions of 1.13 GtCO 2 e in 2018, representing 10.6% of agricultural emissions and 2.1% of global GHG emissions. Synthetic N fertiliser production accounted for 38.8% of total synthetic N fertiliser-associated emissions, while field emissions accounted for 58.6% and transportation accounted for the remaining 2.6%. The top four emitters together, China, India, USA and EU28 accounted for 62% of…

ABSTRACT The objective of this systematic review and meta-analyses is to estimate the prevalence of long-COVID in children and adolescents and to present the full spectrum of symptoms present after acute COVID-19. We have used PubMed and Embase to identify observational studies published before February 10th, 2022 that included a minimum of 30 patients with ages ranging from 0 to 18 years that met the National Institute for Healthcare Excellence (NICE) definition of long-COVID, which consists of both ongoing (4 to 12 weeks) and post-COVID-19 (≥12 weeks) symptoms. Random-effects meta-analyses were performed using the MetaXL software to estimate the pooled prevalence with a 95% confidence interval (CI). Heterogeneity was assessed using I 2 statistics. The Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) reporting guideline was followed (registration PROSPERO CRD42021275408). The literature search yielded 8,373 publications, of which 21 studies met the inclusion criteria, and a total of 80,071 children and adolescents were included. The prevalence of…

The aim of our work was the synthesis of ZnO nano- and microparticles and to study the effect of shapes and sizes on cytotoxicity towards normal and cancer cells and antibacterial activity toward two kinds of bacteria. We fabricated ZnO nano- and microparticles through facile chemical and physical routes. The crystal structure, morphology, textural properties, and photoluminescent properties were characterized by powder X-ray diffraction, electron microscopies, nitrogen adsorption/desorption measurements, and photoluminescence spectroscopy. The obtained ZnO structures were highly crystalline and monodispersed with intensive green emission. ZnO NPs and NRs showed the strongest antibacterial activity against Escherichia coli and Staphylococcus aureus compared to microparticles due to their high specific surface area. However, the ZnO HSs at higher concentrations also strongly inhibited bacterial growth. S. aureus strain was more sensitive to ZnO particles than the E. coli. ZnO NPs and NRs were more harmful to cancer cell lines than to normal ones at the same concentration.

The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We…

Respiratory syncytial virus (RSV) is a leading cause of childhood morbidity, however there is no systematic testing in children hospitalised with respiratory symptoms. Therefore, current RSV incidence likely underestimates the true burden. We used probabilistically linked perinatal, hospital, and laboratory records of 321,825 children born in Western Australia (WA), 2000-2012. We generated a predictive model for RSV positivity in hospitalised children aged < 5 years. We applied the model to all hospitalisations in our population-based cohort to determine the true RSV incidence, and under-ascertainment fraction. The model's predictive performance was determined using cross-validated area under the receiver operating characteristic (AUROC) curve. From 321,825 hospitalisations, 37,784 were tested for RSV (22.8% positive). Predictors of RSV positivity included younger admission age, male sex, non-Aboriginal ethnicity, a diagnosis of bronchiolitis and longer hospital stay. Our model showed good predictive accuracy (AUROC: 0.87). The respective sensitivity, specificity, positive…

Highly pathogenic avian influenza (HPAI) viruses of the A/Goose/Guangdong/1/1996 lineage (GsGd), which threaten the health of poultry, wildlife and humans, are spreading across Asia, Europe, Africa and North America but are currently absent from South America and Oceania. In December 2021, H5N1 HPAI viruses were detected in poultry and a free-living gull in St. John's, Newfoundland and Labrador, Canada. Our phylogenetic analysis showed that these viruses were most closely related to HPAI GsGd viruses circulating in northwestern Europe in spring 2021. Our analysis of wild bird migration suggested that these viruses may have been carried across the Atlantic via Iceland, Greenland/Arctic or pelagic routes. The here documented incursion of HPAI GsGd viruses into North America raises concern for further virus spread across the Americas by wild bird migration.

The Congo Red dye was removed from a simulated textile wastewater solution using fly ash from a local power plant. The characterisation of fly ash was studied in detail by SEM, EDX, XRD, FTIR, BET surface area and TGA techniques. The influence of four parameters (contact time, initial concentration, adsorbent dose, and temperature) was analysed, the results showing that the adsorption capacity depends on these parameters. Thermodynamic and regeneration investigations as well are presented. The fit to pseudo-second-order kinetics models suggests that the removal process is a chemical adsorption. The Langmuir model fitted the experimental data, with a maximum adsorption capacity of 22.12 mg/g. The research is a preliminary case study that highlights that fly ash posed a very good potential as a material for Congo Red dye removal.

Growing antimicrobial resistance has prompted researchers to identify new natural molecules with antimicrobial potential. In this perspective, attention has been focused on biopolymers that could also be functional in the medical field. Chitin is the second most abundant biopolymer on Earth and with its deacetylated derivative, chitosan, has several applications in biomedical and pharmaceutical fields. Currently, the main source of chitin is the crustacean exoskeleton, but the growing demand for these polymers on the market has led to search for alternative sources. Among these, insects, and in particular the bioconverter Hermetia illucens, is one of the most bred. Chitin can be extracted from larvae, pupal exuviae and dead adults of H. illucens, by applying chemical methods, and converted into chitosan. Fourier-transformed infrared spectroscopy confirmed the identity of the chitosan produced from H. illucens and its structural similarity to commercial polymer. Recently, studies showed that chitosan has intrinsic antimicrobial activity. This is the first research that investigated…

Due to their properties and applications, the growing demand for chitin and chitosan has stimulated the market to find more sustainable alternatives to the current commercial source (crustaceans). Bioconverter insects, such as Hermetia illucens, are the appropriate candidates, as chitin is a side stream of insect farms for feed applications. This is the first report on production and characterization of chitin and chitosan from different biomasses derived from H. illucens, valorizing the overproduced larvae in feed applications, the pupal exuviae and the dead adults. Pupal exuviae are the best biomass, both for chitin and chitosan yields and for their abundance and easy supply from insect farms. Fourier-transform infrared spectroscopy, X-ray diffraction and scanning electron microscope analysis revealed the similarity of insect-derived polymers to commercial ones in terms of purity and structural morphology, and therefore their suitability for industrial and biomedical applications. Its fibrillary nature makes H. illucens chitin suitable for producing fibrous manufacts after…

Abstract In the present study, a green, sustainable, simple and low-cost method was adopted for the synthesis of ZnO NPs, for the first time, using the aqueous extract of sea lavender, Limonium pruinosum (L.) Chaz., as a reducing, capping, and stabilizing agent. The obtained ZnO NPs were characterized using ultraviolet–visible spectroscopy (UV–VIS), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The UV–Vis spectra of the green synthesized ZnO NPs showed a strong absorption peak at about 370 nm. Both electron microscopy and XRD confirmed the hexagonal/cubic crystalline structure of ZnO NPs with an average size ~ 41 nm. It is worth noting that the cytotoxic effect of the ZnO NPs on the investigated cancer cells is dose-dependent. The IC 50 of skin cancer was obtained at 409.7 µg/ml ZnO NPs. Also, the phyto-synthesized nanoparticles exhibited potent antibacterial and antifungal activity particularly against…

Microplastics (MPs) are a widely recognized global problem due to their prevalence in natural environments and the food chain. However, the impact of microplastics on human microbiota and their possible biotransformation in the gastrointestinal tract have not been well reported. To evaluate the potential risks of microplastics at the digestive level, completely passing a single dose of polyethylene terephthalate (PET) through the gastrointestinal tract was simulated by combining a harmonized static model and the dynamic gastrointestinal simgi model, which recreates the different regions of the digestive tract in physiological conditions. PET MPs started several biotransformations in the gastrointestinal tract and, at the colon, appeared to be structurally different from the original particles. We report that the feeding with microplastics alters human microbial colonic community composition and hypothesize that some members of the colonic microbiota could adhere to MPs surface promoting the formation of biofilms. The work presented here indicates that microplastics are indeed…

Plant disease can diminish a considerable portion of the agricultural products on each farm. The main goal of this work is to provide visual information for the farmers to enable them to take the necessary preventive measures. A lightweight deep learning approach is proposed based on the Vision Transformer (ViT) for real-time automated plant disease classification. In addition to the ViT, the classical convolutional neural network (CNN) methods and the combination of CNN and ViT have been implemented for the plant disease classification. The models have been trained and evaluated on multiple datasets. Based on the comparison between the obtained results, it is concluded that although attention blocks increase the accuracy, they decelerate the prediction. Combining attention blocks with CNN blocks can compensate for the speed.

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community's research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all…

Mucin-degrading microbes are known to harbor glycosyl hydrolases (GHs) which cleave specific glycan linkages. Although several microbial species have been identified as mucin degraders, there are likely many other members of the healthy gut community with the capacity to degrade mucins. The aim of the present study was to systematically examine the CAZyme mucin-degrading profiles of the human gut microbiota. Within the Verrucomicrobia phylum, all Akkermansia glycaniphila and muciniphila genomes harbored multiple gene copies of mucin-degrading GHs. The only representative of the Lentisphaerae phylum, Victivallales, harbored a GH profile that closely mirrored Akkermansia. In the Actinobacteria phylum, we found several Actinomadura, Actinomyces, Bifidobacterium, Streptacidiphilus and Streptomyces species with mucin-degrading GHs. Within the Bacteroidetes phylum, Alistipes, Alloprevotella, Bacteroides, Fermenitomonas Parabacteroides, Prevotella and Phocaeicola species had mucin degrading GHs. Firmicutes contained Abiotrophia, Blautia, Enterococcus, Paenibacillus, Ruminococcus,…

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may…

Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented…

Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved…

Carbon quantum dots (CQDs) derived from biomass, a suggested green approach for nanomaterial synthesis, often possess poor optical properties and have low photoluminescence quantum yield (PLQY). This study employed an environmentally friendly, cost-effective, continuous hydrothermal flow synthesis (CHFS) process to synthesise efficient nitrogen-doped carbon quantum dots (N-CQDs) from biomass precursors (glucose in the presence of ammonia). The concentrations of ammonia, as nitrogen dopant precursor, were varied to optimise the optical properties of CQDs. Optimised N-CQDs showed significant enhancement in fluorescence emission properties with a PLQY of 9.6% compared to pure glucose derived-CQDs (g-CQDs) without nitrogen doping which have PLQY of less than 1%. With stability over a pH range of pH 2 to pH 11, the N-CQDs showed excellent sensitivity as a nano-sensor for the highly toxic highly-pollutant chromium (VI), where efficient photoluminescence (PL) quenching was observed. The optimised nitrogen-doping process demonstrated effective and efficient tuning of the overall electronic…

The current study examined various types of misinformation related to the COVID-19 vaccines and their relationships to vaccine hesitancy and refusal. Study 1 asked a sample of full-time working professionals in the US (n = 505) about possible misinformation they were exposed to related to the COVID-19 vaccines. Study 2 utilized an online survey to examine U.S. college students' (n = 441) knowledge about COVID-19 vaccines, and its associations with vaccine hesitancy and behavioral intention to get a COVID-19 vaccine. Analysis of open-ended responses in Study 1 revealed that 57.6% reported being exposed to conspiratorial misinformation such as COVID-19 vaccines are harmful and dangerous. The results of a structural equation modeling analysis for Study 2 supported our hypotheses predicting a negative association between the knowledge level and vaccine hesitancy and between vaccine hesitancy and behavioral intention. Vaccine hesitancy mediated the relationship between the vaccine knowledge and behavioral intention. Findings across these studies suggest exposure to misinformation and…

In countries with a high incidence of tuberculosis, the typical clinical features of Crohn's disease (CD) may be covered up after tuberculosis infection, and the identification of atypical Crohn's disease and intestinal tuberculosis (ITB) is still a dilemma for clinicians. Least absolute shrinkage and selection operator (LASSO) regression has been applied to select variables in disease diagnosis. However, its value in discriminating ITB and atypical Crohn's disease remains unknown. A total of 400 patients were enrolled from January 2014 to January 2019 in second Xiangya hospital Central South University.Among them, 57 indicators including clinical manifestations, laboratory results, endoscopic findings, computed tomography enterography features were collected for further analysis. R software version 3.6.1 (glmnet package) was used to perform the LASSO logistic regression analysis. SPSS 20.0 was used to perform Pearson chi-square test and binary logistic regression analysis. In the variable selection step, LASSO regression and Pearson chi-square test were applied to select the most…

Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest…

With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets,…

Abstract The present study aimed to analyse the FT-IR vibrational characteristics and concentration of phenolic compounds in 25 herbal plants using Fourier transform infrared (FT-IR) spectroscopy and Ultra-High Performance Liquid Chromatography (UHPLC) techniques, respectively, and to evaluate its in vitro potential to inhibit enzymes related type 2 diabetes and obesity. The vibrational spectra regions—3400–3200 cm −1 —indicated hydroxyl group (O–H) and H-bonded stretching, which is characteristic of polyphenolic compounds. A wide range in phenolic compounds was found among the samples. Caffeic acid is the predominant phenolic compounds in the samples. Total phenolic content ranged from 5.02 mg GAE/g DW to 102.39 mg GAE/g DW. A moderate correlation ( R 2 ) between antioxidant activity and α-amylase inhibition was 0.46, ( p < 0.05) while that ( R 2 ) of p -coumaric acid and α-glucosidase inhibition was 0.54, ( p < 0.05). Moreover, the herbal infusions showed potential to inhibit digestive enzymes, the highest being on the infusion based on a cup-serving basis.

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them-a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each…

Abstract Microplastics (MPs) have become an emerging global pollutant due to their widespread dispersion and potential threats to marine ecosystems. However, studies on MPs in estuarine and coastal ecosystems of Bangladesh are very limited. Here, we conducted the first study on abundance, distribution, characteristics, and risk assessment of microplastics in the sediment of Karnaphuli River estuary, Bangladesh. Microplastic particles were extracted from sediments of 30 stations along the estuary by density separation and then enumerated and characterized using a stereomicroscope and Fourier Transform Infrared (FT-IR) spectroscopy. In the collected sediment of the Karnaphuli River estuary, the number of MPs varied from 22.29 to 59.5 items kg−1 of dry weight. The mean abundance was higher in the downstream and left banks of the estuary, whereas the predominant shape, colour, and size of MPs were films (35%), and white (19%), and 1–5 mm (30.38%), respectively. Major polymer types were polyethylene terephthalate, polystyrene, polyethylene, cellulose, and nylon. MPs were found to pose…

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large…

Proteins are the essential biological macromolecules required to perform nearly all biological processes, and cellular functions. Proteins rarely carry out their tasks in isolation but interact with other proteins (known as protein-protein interaction) present in their surroundings to complete biological activities. The knowledge of protein-protein interactions (PPIs) unravels the cellular behavior and its functionality. The computational methods automate the prediction of PPI and are less expensive than experimental methods in terms of resources and time. So far, most of the works on PPI have mainly focused on sequence information. Here, we use graph convolutional network (GCN) and graph attention network (GAT) to predict the interaction between proteins by utilizing protein's structural information and sequence features. We build the graphs of proteins from their PDB files, which contain 3D coordinates of atoms. The protein graph represents the amino acid network, also known as residue contact network, where each node is a residue. Two nodes are connected if they have a pair of…

In recent decades, surface air temperature (SAT) data from Global reanalyses points to maximum warming over the northern Barents area. However, a scarcity of observations hampers the confidence of reanalyses in this Arctic hotspot region. Here, we study the warming over the past 20-40 years based on new available SAT observations and a quality controlled comprehensive SAT dataset from the northern archipelagos in the Barents Sea. We identify a statistically significant record-high annual warming of up to 2.7 °C per decade, with a maximum in autumn of up to 4.0 °C per decade. Our results are compared with the most recent global and Arctic regional reanalysis data sets, as well as remote sensing data records of sea ice concentration (SIC), sea surface temperature (SST) and high-resolution ice charts. The warming pattern is primarily consistent with reductions in sea ice cover and confirms the general spatial and temporal patterns represented by reanalyses. However, our findings suggest even a stronger rate of warming and SIC-SAT relation than was known in this region until now.

The green synthesis of silver nanoparticles (AgNPs) and their applications have attracted many researchers as the AgNPs are used effectively in targeting specific tissues and pathogenic microorganisms. The purpose of this study is to synthesize and characterize silver nanoparticles from fully expanded leaves of Eugenia roxburghii DC., as well as to test their effectiveness in inhibiting biofilm production. In this study, at 0.1 mM concentration of silver nitrate (AgNO3), stable AgNPs were synthesized and authenticated by monitoring the color change of the solution from yellow to brown, which was confirmed with spectrophotometric detection of optical density. The crystalline nature of these AgNPs was detected through an X-Ray Diffraction (XRD) pattern. AgNPs were characterized through a high-resolution transmission electron microscope (HR-TEM) to study the morphology and size of the nanoparticles (NPs). A new biological approach was undertaken through the Congo Red Agar (CRA) plate assay by using the synthesized AgNPs against biofilm production. The AgNPs effectively inhibit biofilm…

Abstract Photocatalytic degradation is essential for the successful removal of organic contaminants from wastewater, which is important for ecological and environmental safety. The advanced oxidation process of photocatalysis has become a hot topic in recent years for the remediation of water. Cadmium sulphide (CdS) nanostructures doped with Titanium oxide (CdS/TiO 2 ) nanocomposites has manufactured under ambient conditions using a simple and modified Chemical Precipitation technique. The nanocomposites crystal structure, thermal stability, recombination of photo-generated charge carriers, bandgap, surface morphology, particle size, molar ratio, and charge transfer properties are determined. The production of nanocomposites (CdS-TiO 2 ) and their efficient photocatalytic capabilities are observed. The goal of the experiment is to improve the photocatalytic efficiency of TiO 2 in the visible region by doping CdS nanocomposites. The results showed that as-prepared CdS-TiO 2 nanocomposites has exhibited the highest photocatalytic activity in the process of photocatalytic degradation…

Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system.…

Artificial Intelligence (AI) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between MDs and AI enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human-AI collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an AI support system. Endoscopists were influenced by AI ([Formula: see text]), but not erratically: they followed the AI advice more when it was correct ([Formula: see text]) than incorrect ([Formula: see text]). Endoscopists achieved this outcome through a weighted integration of their and the AI opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human-AI hybrid team to outperform both agents taken…

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization…

SomaScan is a high-throughput, aptamer-based proteomics assay designed for the simultaneous measurement of thousands of proteins with a broad range of endogenous concentrations. In its most current version, the 7k SomaScan assay v4.1 is capable of measuring 7288 human proteins. In this work, we present an extensive technical assessment of this platform based on a study of 2050 samples across 22 plates. Included in the study design were inter-plate technical duplicates from 102 human subjects, which allowed us to characterize different normalization procedures, evaluate assay variability by multiple analytical approaches, present signal-over-background metrics, and discuss potential specificity issues. By providing detailed performance assessments on this wide range of technical aspects, we aim for this work to serve as a valuable resource for the growing community of SomaScan users.

In recent years, the problem of addressing fairness in machine learning (ML) and automatic decision making has attracted a lot of attention in the scientific communities dealing with artificial intelligence. A plethora of different definitions of fairness in ML have been proposed, that consider different notions of what is a "fair decision" in situations impacting individuals in the population. The precise differences, implications and "orthogonality" between these notions have not yet been fully analyzed in the literature. In this work, we try to make some order out of this zoo of definitions.

Effectively preventing and controlling zoonotic diseases requires a One Health approach that involves collaboration across sectors responsible for human health, animal health (both domestic and wildlife), and the environment, as well as other partners. Here we describe the Generalizable One Health Framework (GOHF), a five-step framework that provides structure for using a One Health approach in zoonotic disease programs being implemented at the local, sub-national, national, regional, or international level. Part of the framework is a toolkit that compiles existing resources and presents them following a stepwise schematic, allowing users to identify relevant resources as they are required. Coupled with recommendations for implementing a One Health approach for zoonotic disease prevention and control in technical domains including laboratory, surveillance, preparedness and response, this framework can mobilize One Health and thereby enhance and guide capacity building to combat zoonotic disease threats at the human-animal-environment interface.

Abstract The resources of clean water worldwide are very limited, and climate change is already affecting the available supplies. Therefore, developing a low-cost, highly efficient, and recyclable adsorbent to upgrade water quality has become an essential task. Herein, we report the fabrication of activated carbon (AC) adsorbents derived from lignocellulosic wastes. Both physical and chemical activation were investigated to modify the surface texture properties. The results indicated that increasing the activation temperature, whether physically or chemically, increases the specific surface area (S BET ). On the contrary, increasing the amount of the chemical activating agent significantly decreases the S BET values. The S BET of 1771, 2120, and 2490 m 2 g −1 were obtained for water vapor, K 2 CO 3 and KOH, at activation temperatures of 950 °C, 800 °C, and 800 °C, respectively. Methylene blue (MB) and phenol were used as adsorbates for the adsorption experiment. Adsorption of methylene blue dye revealed the ability of the water activated carbon to remove more than 95% of the dye…

Soil-related problems have grown up to be a major threat to human society. Scientific evaluation is helpful to understand the status of soil pollution and provide reference to further work. In this situation, Liaoning Province, a typical industrial and agricultural province in Northeast China, was selected as a case study region. It reviewed 200 studies published between 2010 and 2020 and recorded related data of soil heavy metal. It used model method and index method to evaluate the agricultural region. The comprehensive assessment score of Liaoning pollution level was 0.8998. Dalian was 0.9536, ranking first among the 14 cities. Huludao and Jinzhou were 0.7594 respectively, ranked the last. Heavy metal accumulation in different cities stemmed from different sources, including weathering of parent materials, industrial wastes, sewage irrigation, and mining activities. In general, the pollution level of heavy metal in Liaoning was at low risk level, but it still needs to pay attention to the health risk of heavy metal and the input of heavy metal into the soil, especially cadmium…

Childhood obesity is a risk factor for numerous health conditions. A critical factor in the etiology of obesity appears to be the gut microbiota, which is the microbial community that resides in the human gut. The ratio of the phyla Firmicutes and Bacteroidetes (F/B) and gut bacterial genera that produce short-chain fatty acids (SCFA) have been suggested to contribute to obesity. The current study investigated (1) whether differences in F/B ratio can be observed in infancy and childhood in relation to zBMI in healthy children, and (2) whether an innovative proxy measure adds evidence to a relationship between SCFA producers and the etiology of obesity. Stool samples were collected at five time points, and zBMI was assessed at eight time points throughout the first 12 years of life. Our confirmatory analyses with Bayesian multilevel models showed no relationship between the F/B ratio and zBMI. Also, a proxy measure constructed from known SCFA producers was unrelated to zBMI throughout the first 12 years of life. Exploratory analyses using multilevel and random forest models suggest…

Abstract Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R 2 ) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R 2…

Increased intracellular toxicity due to an imbalance in copper homeostasis caused by copper ion accumulation could regulate the rate of cancer cell growth and proliferation. The goal of this study was to create a novel Cuproptosis-related lncRNA signature that may be utilized to predict survival and immunotherapy in HCC patients. Cuproptosis-associated lncRNAs and differentially expressed lncRNAs between HCC tumor tissue and normal tissue were discovered first. By LASSO-Cox analysis, the overlapping lncRNAs were then utilized to build a Cuproptosis-associated lncRNA signature, which might be used to predict patient prognosis and responsiveness to immune checkpoint blockade (ICB) therapy. Differences in the infiltration of immune cell subpopulations between high and low-risk score subgroups were also analyzed. Moreover, a nomogram based on the Cuproptosis-associated lncRNA signature and clinical features was developed and demonstrated to have good predictive potential. Finally, qRT-PCR was performed in HerpG2 and MHCC-97H cell lines to explore whether these lncRNAs were indeed…

Breast cancer survival rates have markedly improved. Consequently, survivorship issues have received increased attention. One common sequel of treatment is chemotherapy-induced cognitive impairment (CICI). CICI causes a range of impairments that can have a significant negative impact on quality of life. Knowledge of the prevalence of this condition is required to inform survivorship plans, and ensure adequate resource allocation and support is available for sufferers, hence a systematic review of prevalence data was performed. Medline, Scopus, CINAHL and PSYCHInfo were searched for eligible studies which included prevalence data on CICI, as ascertained though the use of self-report, or neuropsychological tests. Methodological quality of included studies was assessed. Findings were synthesised narratively, with meta-analyses being used to calculate pooled prevalence when impairment was assessed by neuropsychological tests. The review included 52 studies. Time-points considered ranged from the chemotherapy treatment period to greater than 10 years after treatment cessation. Summary…

Geological porous reservoirs provide the volume capacity needed for large scale underground hydrogen storage (UHS). To effectively exploit these reservoirs for UHS, it is crucial to characterize the hydrogen transport properties inside porous rocks. In this work, for the first time in the community, we have performed [Formula: see text]/water multiphase flow experiments at core scale under medical X-ray CT scanner. This has allowed us to directly image the complex transport properties of [Formula: see text] when it is injected or retracted from the porous rock. The important effective functions of capillary pressure and relative permeability are also measured, for both drainage and imbibition. The capillary pressure measurements are combined with MICP data to derive a receding contact angle for the [Formula: see text]/water/sandstone rock system. The rock core sample is a heterogeneous Berea sandstone (17 cm long and 3.8 cm diameter). Our investigation reveals the interplay between gravitational, capillary, and viscous forces. More specifically, it illustrates complex displacement…

Transcutaneous auricular vagus nerve stimulation (taVNS) has been investigated as a novel neuromodulation tool. Although taVNS is generally considered safe with only mild and transient adverse effects (AEs), those specifically caused by taVNS have not yet been investigated. This systematic review and meta-analysis on taVNS aimed to (1) systematically analyze study characteristics and AE assessment, (2) characterize and analyze possible AEs and their incidence, (3) search for predictable risk factors, (4) analyze the severity of AE, and (5) suggest an evidence-based taVNS adverse events questionnaire for safety monitoring. The articles searched were published through April 7, 2022, in Medline, Embase, Web of Science, Cochrane, and Lilacs databases. In general, we evaluated 177 studies that assessed 6322 subjects. From these, 55.37% of studies did not mention the presence or absence of any AEs; only 24.86% of the studies described that at least one adverse event occurred. In the 35 studies reporting the number of subjects with at least one adverse event, a meta-analytic approach to…

Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The model can capture complex nonlinear phenomena, from plasticity to buckling instability, seemingly learning physical relationships between the predicted physical fields. Owing to its flexibility, this graph-based framework aims at connecting materials' microstructure, base materials' properties,…

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and…

Abstract In this work, a simple and green synthesis procedure for phytofabrication Zinc oxide-silver supported biochar nanocomposite (Ag/ZnO@BC) via Persicaria salicifolia biomass is investigated for the first time to uphold numerous green chemistry such as less hazardous chemical syntheses. XRD technique showed the crystal structure of the phytosynthesized Ag/ZnO@BC, whereas UV–visible spectroscopy, FT-IR, SEM, EDX, TEM, and XPS analyses indicated the successful biosynthesis of the nanocomposite. Testing the photocatalytic potential of this novel nanocomposite in the removal of TC under different conditions unraveled its powerful photodegradation efficiency that reached 70.3% under the optimum reaction conditions: TC concentration; 50 ppm, pH; 6, a dose of Ag/ZnO@BC; 0.01 g, temperature; 25 °C, and H 2 O 2 concentration; 100 mM. The reusability of Ag/ZnO@BC was evident as it reached 53% after six cycles of regeneration. Ag/ZnO@BC was also shown to be a potent antimicrobial agent against Klebsiella pneumonia as well as a promising antioxidant material. Therefore, the current work…

Abstract Older adults are especially susceptible to fake news online, possibly because they are less digitally literate compared to younger individuals. Interventions for older adults have emerged to improve digital literacy, although there has been little evaluation of their effectiveness in improving older adults’ resilience to fake news. We report the results of a digital literacy intervention for older adults administered during the 2020 U.S. election. The intervention was a 1-hour, self-directed series of interactive modules designed to teach concepts and skills for identifying misinformation online. Consistent with our pre-registered hypothesis, older adults ( M age = 67) in the treatment condition (N = 143) significantly improved their likelihood of accurately discerning fake from true news from 64% pre-intervention to 85% post-intervention. In contrast, older adults in the control condition (N = 238) did not significantly improve (from 55% to 57%). The treated older adults were also more likely to employ strategies for identifying misinformation online compared to…

We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

Bone defects stand out as one of the greatest challenges of reconstructive surgery. Fused deposition modelling (FDM) allows for the printing of 3D scaffolds tailored to the morphology and size of bone damage in a patient-specific and high-precision manner. However, FDM still suffers from the lack of materials capable of efficiently supporting osteogenesis. In this study, we developed 3D-printed porous scaffolds composed of polylactic acid/hydroxyapatite (PLA/HA) composites with high ceramic contents (above 20%, w/w) by FDM. The mechanical properties of the PLA/HA scaffolds were compatible with those of trabecular bone. In vitro degradation tests revealed that HA can neutralize the acidification effect caused by PLA degradation, while simultaneously releasing calcium and phosphate ions. Importantly, 3D-printed PLA/HA did not induce the upregulation of activation markers nor the expression of inflammatory cytokines in dendritic cells thus exhibiting no immune-stimulatory properties in vitro. Evaluations using human mesenchymal stem cells (MSC) showed that pure PLA scaffolds exerted an…

Sea level rise (SLR) will increase adaptation needs along low-lying coasts worldwide. Despite centuries of experience with coastal risk, knowledge about the effectiveness and feasibility of societal adaptation on the scale required in a warmer world remains limited. This paper contrasts end-century SLR risks under two warming and two adaptation scenarios, for four coastal settlement archetypes (Urban Atoll Islands, Arctic Communities, Large Tropical Agricultural Deltas, Resource-Rich Cities). We show that adaptation will be substantially beneficial to the continued habitability of most low-lying settlements over this century, at least until the RCP8.5 median SLR level is reached. However, diverse locations worldwide will experience adaptation limits over the course of this century, indicating situations where even ambitious adaptation cannot sufficiently offset a failure to effectively mitigate greenhouse-gas emissions.

Road damage detection is an important task to ensure road safety and realize the timely repair of road damage. The previous manual detection methods are low in efficiency and high in cost. To solve this problem, an improved YOLOv5 road damage detection algorithm, MN-YOLOv5, was proposed. We optimized the YOLOv5s model and chose a new backbone feature extraction network MobileNetV3 to replace the basic network of YOLOv5, which greatly reduced the number of parameters and GFLOPs of the model, and reduced the size of the model. At the same time, the coordinate attention lightweight attention module is introduced to help the network locate the target more accurately and improve the target detection accuracy. The KMeans clustering algorithm is used to filter the prior frame to make it more suitable for the dataset and to improve the detection accuracy. To improve the generalization ability of the model, a label smoothing algorithm is introduced. In addition, the structure reparameterization method is used to accelerate model reasoning. The experimental results show that the improved…

Increasing number of Janus kinase (JAK) inhibitors have been approved for chronic haematopoietic neoplasms and inflammatory/autoimmune diseases. We aimed to assess safety of the first three approved JAK inhibitors: ruxolitinib, tofacitinib and baricitinib. In this retrospective observational study, pharmacovigilance data were extracted from the World Health Organization database. Adverse events are classified according to Medical Dictionary for Regulatory Activities hierarchy. Until February 28, 2021, all Individual Case Safety Reports [ICSRs] with the suspected drug ruxolitinib, tofacitinib or baricitinib were included. Disproportionality analysis was performed and the information component (IC) was estimated. Adverse events were considered a significant signal if the lower end of the 95% credibility interval of the IC (IC025) was positive. We identified 126,815 ICSRs involving JAK inhibitors. Ruxolitinib, tofacitinib and baricitinib were associated with infectious adverse events (IC025 1.7, especially with viral [herpes and influenza], fungal, and mycobacterial infectious…

Fabrication based on additive manufacturing (AM) process from a three-dimensional (3D) model has received significant attention in the past few years. Although 3D printing was introduced for production of prototypes, it has been currently used for fabrication of end-use products. Therefore, the mechanical behavior and strength of additively manufactured parts has become of significant importance. 3D printing has been affected by different parameters during preparation, printing, and post-printing processes, which have influence on quality and behavior of the additively manufactured components. This paper discusses the effects of two printing parameters on the mechanical behavior of additively manufactured components. In detail, polylactic acid material was used to print test coupons based on fused deposition modeling process. The specimens with five different raster orientations were printed with different printing speeds. Later, a series of tensile tests was performed under static loading conditions. Based on the results, strength and stiffness of the examined specimens have been…

Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median:…

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the…

Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and…

Abstract Mining activities have increased the accumulation of heavy metals in farmland soil and in food crops. To identify the key soil properties influencing heavy metal bioavailability and accumulation in food crops, 81 crop samples and 81 corresponding agricultural soil samples were collected from rape, wheat, and paddy fields. Heavy metal (copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), iron (Fe), and manganese (Mn)) concentrations in soils and rape, wheat, rice grains were determined using inductively coupled plasma atomic emission spectroscopy, and soil physicochemical properties (pH, organic matter, total nitrogen, total phosphorus, available phosphorus, and available potassium (AK)) were analyzed. Soil extractable metals were extracted using various single extractants (DTPA, EDTA, NH 4 OAc, NH 4 NO 3 , and HCl). The average concentrations of Cu, Zn, Pb, Cd, and Mn in the soil samples all exceeded the local geochemical background value (background values of Cu, Zn, Pb, Cd, and Mn are 43.0, 81.0, 28.5, 0.196, and 616 mg/kg, respectively), and Cd over-standard rate was the…

Mutations in superoxide dismutase 1 gene (SOD1) are linked to amyotrophic lateral sclerosis (ALS), a neurodegenerative disorder predominantly affecting upper and lower motor neurons. The clinical phenotype of ALS shows inter- and intrafamilial heterogeneity. The aim of the study was to analyze the relations between individual SOD1 mutations and the clinical presentation using in silico methods to assess the SOD1 mutations severity. We identified SOD1 causative variants in a group of 915 prospectively tested consecutive Polish ALS patients from a neuromuscular clinical center, performed molecular modeling of mutated SOD1 proteins and in silico analysis of mutation impact on clinical phenotype and survival analysis of associations between mutations and hazard of clinical end-points. Fifteen SOD1 mutations were identified in 21.1% familial and 2.3% sporadic ALS cases. Their effects on SOD1 protein structure and functioning inferred from molecular modeling and in silico analyses correlate well with the clinical data. Molecular modeling results support the hypothesis that folding…

Many infectious diseases, including COVID-19, are transmitted by airborne pathogens. There is a need for effective environmental control measures which, ideally, are not reliant on human behaviour. One potential solution is Krypton Chloride (KrCl) excimer lamps (often referred to as Far-UVC), which can efficiently inactivate pathogens, such as coronaviruses and influenza, in air. Research demonstrates that when KrCl lamps are filtered to remove longer-wavelength ultraviolet emissions they do not induce acute reactions in the skin or eyes, nor delayed effects such as skin cancer. While there is laboratory evidence for Far-UVC efficacy, there is limited evidence in full-sized rooms. For the first time, we show that Far-UVC deployed in a room-sized chamber effectively inactivates aerosolised Staphylococcus aureus. At a room ventilation rate of 3 air-changes-per-hour (ACH), with 5 filtered-sources the steady-state pathogen load was reduced by 98.4% providing an additional 184 equivalent air changes (eACH). This reduction was achieved using Far-UVC irradiances consistent with current…

Abstract Biosynthesis of silver nanoparticles (AgNPs) is emerging as a simple and eco-friendly alternative to conventional chemical synthesis methods. The role of AgNPs is expanding as antimicrobial and anticancer agents, sensors, nanoelectronic devices, and imaging contrast agents. In this study, biogenic AgNPs were synthesized using extracts of different marine algae species, including Ulva rigida (green alga), Cystoseira myrica (brown alga), and Gracilaria foliifera (red alga), as reducing and capping agents. The Physiochemical properties, cytotoxicity, anticancer and antimicrobial activities of the biosynthesized AgNPs were assessed. Surface plasmonic bands of the biosynthesized AgNPs capped with U. rigida, C. myrica, and G. foliifera extracts were visually observed to determine a colour change, and their peaks were observed at 424 nm, 409 nm, and 415 nm, respectively, by UV–Vis spectroscopy; transmission electron microscopy (TEM) indicated an almost spherical shape of AgNPs with nanoscale sizes of 12 nm, 17 nm, and 24 nm, respectively. Fourier transform-infrared (FTIR)…

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer…

Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big…

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of interpretability as well as dataset representation bias and prediction fairness of deep learning models for in-hospital mortality prediction. First, we analyze the interpretability of deep learning mortality prediction models and observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models as well as recognizing new important features that domain knowledge does not consider; (2) prediction models rely on demographic…

Postpartum depression is common; however, little is known about its relationship to social support and postpartum depression. This study examined the association between them among South Korean women within one year of childbirth. This study was based on the 2016 Korean Study of Women's Health-Related Issues (K-Stori), a cross-sectional survey employing nationally-representative random sampling. Participants were 1,654 postpartum women within a year of giving birth. Chi-square test and logistic regression analysis were conducted to analyze the associations between social support (and other covariates) and postpartum depression. Among participants, 266 (16.1%) had postpartum depression. Depending on the level of social support, 6.0%, 53.9%, and 40.1% of them had low, moderate, and high social support, respectively. Women with moderate or low social support were more likely to have postpartum depression (OR = 1.78, 95% CI = 1.26-2.53; OR = 2.76, 95% CI = 1.56-4.89). This trend was observed in participants with multiparity, pregnancy loss, obese body image, and employed women. Social…

As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. In this study, we present the first analysis of AI "aging": the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle. Using datasets from four different industries (healthcare operations, transportation, finance, and weather) and four standard machine learning models, we identify and describe the main temporal degradation patterns. We also demonstrate the principal differences between temporal model degradation and related concepts that have been explored previously, such as data concept drift and continuous learning. Finally, we indicate potential causes of temporal degradation, and suggest approaches to detecting aging and reducing its impact.

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15%…

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest…

Abstract Fibroblasts are the main cells of connective tissue and have pivotal roles in the proliferative and maturation phases of wound healing. These cells can secrete various cytokines, growth factors, and collagen. Vascular endothelial growth factor (VEGF) is a unique factor in the migration process of fibroblast cells through induces wound healing cascade components such as angiogenesis, collagen deposition, and epithelialization. This study aimed to create VEGF165 overexpressing fibroblast cells to evaluate angiogenesis function in wound healing. In vitro, a novel recombinant expression vector, pcDNA3.1(-)-VEGF, was produced and transfected into the fibroblast cells. Following selecting fibroblast cells with hygromycin, recombinant cells were investigated in terms of VEGF expression by quantifying and qualifying methods. Mechanical, physical, and survival properties of polyurethane-cellulose acetate (PU-CA) scaffold were investigated. Finally, in vivo, the angiogenic potential was evaluated in four groups containing control, PU-CA, PU-CA with fibroblast cells, and…

Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on wearables are epoch-based physical activity counts using accelerometer data. Even though activity counts have been the backbone of thousands of clinical and epidemiological studies, there are large variations of the algorithms that compute counts and their associated parameters-many of which have often been kept proprietary by device providers. This lack of transparency has hindered comparability between studies using different devices and limited their broader clinical applicability. ActiGraph devices have been the most-used wearable accelerometer devices for over two decades. Recognizing the importance of data transparency, interpretability and interoperability to both research and clinical use, we here describe the detailed counts algorithms of five generations of ActiGraph devices going back to the first AM7164 model, and publish the current counts algorithm in ActiGraph's ActiLife and CentrePoint software as a…

Abstract Chitosan nanoparticles (CNPs) are promising versatile cationic polymeric nanoparticles, which have received growing interest over last few decades. The biocompatibility, biodegradability, environmental safety and non-toxicity of the chitosan nanoparticles makes it preferred for a wide range of biological applications including agriculture, medical and pharmaceutical fields. In this study, CNPs were biosynthesized by aqueous extract of Eucalyptus globulus Labill fresh leaves as bio-reductant. Box–Behnken design in 29 experimental runs was used for optimization of different factors affecting the production of CNPs. The maximum yield of CNPs was 9.91 mg/mL at pH of 4.5, chitosan concentration of 1%, incubation time of 60 min and temperature of 50 °C. The crystallinity, particle size and morphology of the biosynthesized CNPs were characterized. The CNPs possess a positively charged surface of 31.1 mV. The SEM images of the CNPs confirms the formation of spherical form with smooth surface. The TEM images show CNPs were spherical in shape and their size range was between 6.92 and…

In recent years, vibrational spectroscopic techniques based on Fourier transform infrared (FTIR) or Raman microspectroscopy have been suggested to fulfill the unmet need for microplastic particle detection and identification. Inter-system comparison of spectra from reference polymers enables assessing the reproducibility between instruments and advantages of emerging quantum cascade laser-based optical photothermal infrared (O-PTIR) spectroscopy. In our work, IR and Raman spectra of nine plastics, namely polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, polycarbonate, polystyrene, silicone, polylactide acid and polymethylmethacrylate were simultaneously acquired using an O-PTIR microscope in non-contact, reflection mode. Comprehensive band assignments were presented. We determined the agreement of O-PTIR with standalone attenuated total reflection FTIR and Raman spectrometers based on the hit quality index (HQI) and introduced a two-dimensional identification (2D-HQI) approach using both Raman- and IR-HQIs. Finally, microplastic particles were prepared as…

Globally, there is growing interest to recycle organic waste using insect larvae into high-quality frass fertilizer through circular economy approach. This paper presents the first comparative report on the nutrient concentrations, fertilizing indices, nutrient supply potentials and compost maturity of nine edible insect frass fertilizers. Our results revealed that frass fertilizers from all the insect species had adequate concentrations and contents of macronutrients [nitrogen (N), phosphorus (P), potassium (K)], secondary nutrients (calcium, magnesium, and sulphur) and micro-nutrients (manganese, copper, iron, zinc, boron, and sodium). The fertilizing indices of the frass fertilizers were above 3. However, black soldier fly (BSF) frass fertilizer had significantly higher N (20-130%) and K (17-193%) concentrations compared to others. The P concentration of Gryllus bimaculatus frass fertilizer was 3-800% higher compared to those of frass fertilizers from other insect species. The potential N and K supply capacities of BSF frass fertilizer was 19-78% and 16-190% higher, respectively.…

Abstract This study investigated for the first time a simple bio-synthesis approach for the synthesis of copper oxide nanoparticles (CuO NPs) using Annona muricata L ( A. muricata ) plant extract to test their anti-cancer effects. The presence of CuONPs was confirmed by UV–visible spectroscopy, Scanning electron microscope (SEM), and Transmission electron microscope (TEM). The antiproliferative properties of the synthesized nanoparticles were evaluated against (AMJ-13), (MCF-7) breast cancer cell lines, and the human breast epithelial cell line (HBL-100) as healthy cells. This study indicates that CuONPs reduced cell proliferation for AMJ-13 and MCF-7. HBL-100 cells were not significantly inhibited for several concentration levels or test periods. The outcomes suggest that the prepared copper oxide nanoparticles acted against the growth of specific cell lines observed in breast cancer. It was observed that cancer cells had minor colony creation after 24 h sustained CuONPs exposure using (IC 50 ) concentration for AMJ-13 was (17.04 µg mL −1 ). While for MCF-7 cells was (18.92 µg mL…

We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50-0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung…

Several large-scale drivers of both anthropogenic and natural environmental changes are interacting nonlinearly in the transition zone between eastern Amazonia and the adjacent Cerrado, considered to be another Brazilian agricultural frontier. Land-use change for agrobusiness expansion together with climate change in the transition zone between eastern Amazonia and the adjacent Cerrado may have induced a worsening of severe drought conditions over the last decade. Here we show that the largest warming and drying trends over tropical South America during the last four decades are observed to be precisely in the eastern Amazonia-Cerrado transition region, where they induce delayed wet-season and worsen severe drought conditions over the last decade. Our results evidence an increase in temperature, vapor pressure deficit, subsidence, dry-day frequency, and a decrease in precipitation, humidity, and evaporation, plus a delay in the onset of the wet season, inducing a higher risk of fire during the dry-to-wet transition season. These findings provide observational evidence of the…

Carbon fibers were successfully fabricated via the electrospinning technique, followed by stabilizing and carbonizing electrospun PAN fibers. A wide range of analytical techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Diffuse reflectance spectroscopy (DRS), photoluminescence spectroscopy (PL), vibrating sample magnetometer (VSM) techniques, and Hall effect were performed to study of the effect of carbonization temperature on the physical and chemical characterization of carbon fibers. The SEM images of the PAN precursor exhibit a smooth outer surface, after the stabilization and carbonization process, along with a broken fiber at higher carbonization temperature about 1400 °C. Morphological characterization based on the recorded TEM images of carbonized fibers at 1000 °C and 1400 °C, showed that the obtained morphology can be classified as fiber structures, where their diameters ranged from 196 to 331 nm. The XRD patterns of PAN-based carbon fibers confirm the structural changes from linear structure into a…

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure…

E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification…

Abstract The necessity to resolve the issue of rapid charge carrier recombination for boosting photocatalytic performance is a vigorous and challenging research field. To address this, the construction of a binary system of step-scheme (S-scheme) CuO@TiO2 heterostructure composite has been demonstrated through a facile solid-state route. The remarkably enhanced photocatalytic performance of CuO@TiO2, compared with single TiO2, which can consequence in the more efficient separation of photoinduced charge carriers, reduced the band gap of TiO2, improved the electrical transport performance, and improved the lifetimes, thus donating it with the much more powerful oxidation and reduction capability. A photocatalytic mechanism was proposed to explain the boosted photocatalytic performance of CuO@TiO2 on a complete analysis of physicochemical, DFT calculations, and electrochemical properties. In addition, this work focused on the investigation of the stability and recyclability of CuO@TiO2 in terms of efficiency and its physical origin using XRD, BET, and XPS. It is found that the removal…

In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with…

To overcome the limitations of the conventional vacuum thermoforming manufacturing method, direct 3D printing of clear aligners has been developed. The present study investigated the thermo-mechanical and viscoelastic properties of a photocurable resin TC-85, which is a new material for the direct 3D printed clear aligners, comparing to a conventional thermoplastic material polyethylene terephthalate glycol. Dynamic mechanical analysis was performed to analyse the mechanical behaviours of the two materials at 37 °C and 80 °C, respectively. Furthermore, the shape memory property of the two materials was evaluated using a U-shape bending test, and the shape recovery ratio for 60 min at 37 °C was calculated. The results indicate that TC-85 can constantly apply a light force to the teeth when used for the 3D printed clear aligners, owing to its flexibility and viscoelastic properties. In addition, it is expected that the force decay induced by repeated insertion of the clear aligners will be reduced and a constant orthodontic force will be maintained. Furthermore, its geometric…

Developing a cheap, stable and effective photocatalyst is necessary for remediation of persistent organic pollutants. To address this challenge, we proposed a unique interfacial engineering technique and proper bandgap matching strategy to synthesize MWCNTs/ZnO/Chitosan ternary nanocomposite for effective photocatalytic application. The features of the prepared samples were determined by FESEM, TEM, EDX, elemental mapping, AFM, FT-IR, XRD, UV-Vis spectroscopy and BET surface analysis. The obtained results showed successful fabrication of synthesized nanocomposites with enhanced surface area. Degradation effect of nanostructures on methylene blue (MB) and antibacterial activity against Escherichia coli (E. coli), Staphylococcus aureus (S. aureus) and Bacillus subtilis (B. subtilis) pathogenic strains were investigated. The proposed photocatalytic mechanism illustrated the electron transfer facilitated by MWCNTs/ZnO/Chitosan structure which results in spatial separation of electron-hole pairs. Compared with ZnO and ZnO/Chitosan, the prepared MWCNTs/ZnO/Chitosan ternary nanocomposite…

The evolution of the art ecosystem is driven by largely invisible networks, defined by undocumented interactions between artists, institutions, collectors and curators. The emergence of cryptoart, and the NFT-based digital marketplace around it, offers unprecedented opportunities to examine the mechanisms that shape the evolution of networks that define artistic practice. Here we mapped the Foundation platform, identifying over 48,000 artworks through the associated NFTs listed by over 15,000 artists, allowing us to characterize the patterns that govern the networks that shape artistic success. We find that NFT adoption by both artists and collectors has undergone major changes, starting with a rapid growth that peaked in March 2021 and the emergence of a new equilibrium in June. Despite significant changes in activity, the average price of the sold art remained largely unchanged, with the price of an artist's work fluctuating in a range that determines his or her reputation. The artist invitation network offers evidence of rich and poor artist clusters, driven by homophily,…

Abstract Osteoarthritis (OA) is a common disabling disease worldwide, with no effective and safe disease-modifying drugs (DMOAD) in the market. However, studies suggest that drugs, such as liraglutide, which possess strong potential in decreasing low-grade systemic inflammation may be effective in treating OA. Therefore, the aim of this study was to examine the anti-inflammatory, analgesic, and anti-degradative effects in OA using in vitro and in vivo experiments. The results showed that intra-articular injection of liraglutide alleviated pain-related behavior in in vivo sodium monoiodoacetate OA mouse model, which was probably driven by the GLP-1R-mediated anti-inflammatory activity of liraglutide. Moreover, liraglutide treatment significantly decreased IL-6, PGE 2 and nitric oxide secretion, and the expression of inflammatory genes in vitro in chondrocytes and macrophages in a dose-dependent manner. Additionally, liraglutide shifted polarized macrophage phenotype in vitro from the pro-inflammatory M1 phenotype to the M2 anti-inflammatory phenotype. Furthermore, liraglutide exerted…

Battery electric vehicles (BEVs) have emerged as a promising alternative to traditional internal combustion engine (ICE) vehicles due to benefits in improved fuel economy, lower operating cost, and reduced emission. BEVs use electric motors rather than fossil fuels for propulsion and typically store electric energy in lithium-ion cells. With rising concerns over fossil fuel depletion and the impact of ICE vehicles on the climate, electric mobility is widely considered as the future of sustainable transportation. BEVs promise to drastically reduce greenhouse gas emissions as a result of the transportation sector. However, mass adoption of BEVs faces major barriers due to consumer worries over several important battery-related issues, such as limited range, long charging time, lack of charging stations, and high initial cost. Existing solutions to overcome these barriers, such as building more charging stations, increasing battery capacity, and stationary vehicle-to-vehicle (V2V) charging, often suffer from prohibitive investment costs, incompatibility to existing BEVs, or long travel…

Preterm birth is a major cause of neonatal morbidity and mortality worldwide. Increasing evidence links the vaginal microbiome to the risk of spontaneous preterm labour that leads to preterm birth. The aim of this systematic review and network meta-analysis was to investigate the association between the vaginal microbiome, defined as community state types (CSTs, i.e. dominance of specific lactobacilli spp, or not (low-lactobacilli)), and the risk of preterm birth. Systematic review using PubMed, Web of Science, Embase and Cochrane library was performed. Longitudinal studies using culture-independent methods categorizing the vaginal microbiome in at least three different CSTs to assess the risk of preterm birth were included. A (network) meta-analysis was conducted, presenting pooled odds ratios (OR) and 95% confidence intervals (CI); and weighted proportions and 95% CI. All 17 studies were published between 2014 and 2021 and included 38-539 pregnancies and 8-107 preterm births. Women presenting with "low-lactobacilli" vaginal microbiome were at increased risk (OR 1.69, 95% CI…

Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to…

. Due to the complexity of runoff generation and the streamflow process, the historical impact of human-induced climate change on river flooding remains uncertain. Here, we address the question of whether anthropogenic climate change has altered the probability of the extreme river flood events for the period 1951-2010 based on simulated river discharge derived from large ensemble climate experiments with and without human-induced climate change. The results indicate that human-induced climate change altered the probabilities of 20 of the 52 analyzed flood events. Fourteen of these 20 flood events, which occurred mainly in Asia and South America, were very likely to have been enhanced by human-induced climate change due to an increase in heavy precipitation. Conversely, two flood events in North/South America and two flood events in Asia and two flood events in Europe were suppressed by human-induced climate change, perhaps as a result of lower snowfall. Human-induced climate change has enhanced flooding more prominently in recent years, providing important insights into potential…

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near…

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3-100%, and 97%, CI 95%: 93.7-100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2-59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model…

Today's growing phishing websites pose significant threats due to their extremely undetectable risk. They anticipate internet users to mistake them as genuine ones in order to reveal user information and privacy, such as login ids, pass-words, credit card numbers, etc. without notice. This paper proposes a new approach to solve the anti-phishing problem. The new features of this approach can be represented by URL character sequence without phishing prior knowledge, various hyperlink information, and textual content of the webpage, which are combined and fed to train the XGBoost classifier. One of the major contributions of this paper is the selection of different new features, which are capable enough to detect 0-h attacks, and these features do not depend on any third-party services. In particular, we extract character level Term Frequency-Inverse Document Frequency (TF-IDF) features from noisy parts of HTML and plaintext of the given webpage. Moreover, our proposed hyperlink features determine the relationship between the content and the URL of a webpage. Due to the absence of…

Fabrication of well-ordered and bio-mimetic scaffolds is one of the most important research lines in tissue engineering. Different techniques have been utilized to achieve this goal, however, each method has its own disadvantages. Recently, melt electrowriting (MEW) as a technique for fabrication of well-organized scaffolds has attracted the researchers' attention due to simultaneous use of principles of additive manufacturing and electrohydrodynamic phenomena. In previous research studies, polycaprolactone (PCL) has been mostly used in MEW process. PCL is a biocompatible polymer with characteristics that make it easy to fabricate well-arranged structures using MEW device. However, the mechanical properties of PCL are not favorable for applications like bone tissue engineering. Furthermore, it is of vital importance to demonstrate the capability of MEW technique for processing a broad range of polymers. To address aforementioned problems, in this study, three ten-layered box-structured well-ordered scaffolds, including neat PLA, neat PCL, and PLA/PCL composite are fabricated using…

It is generally accepted that climate change is having a negative impact on food security. However, most of the literature variously focuses on the complex and many mechanisms linking climate stressors; the links with food production or productivity rather than food security; and future rather than current effects. In contrast, we investigate the extent to which current changes in food insecurity can be plausibly attributed to climate change. We combine food insecurity data for 83 countries from the FAO food insecurity experience scale (FIES) with reanalysed climate data from ERA5-Land, and use a panel data regression with time-varying coefficients. This framework allows us to estimate whether the relationship between food insecurity and temperature anomaly is changing over time. We also control for Human Development Index, and drought measured by six-month Standardized Precipitation Index. Our empirical findings suggest that for every 1 [Formula: see text] of temperature anomaly, severe global food insecurity has increased by 1.4% (95% CI 1.3-1.47) in 2014 but by 1.64% (95% CI…

Many critical advances in research utilize techniques that combine high-resolution with high-content characterization at the single cell level. We introduce the MICS (MACSima Imaging Cyclic Staining) technology, which enables the immunofluorescent imaging of hundreds of protein targets across a single specimen at subcellular resolution. MICS is based on cycles of staining, imaging, and erasure, using photobleaching of fluorescent labels of recombinant antibodies (REAfinity Antibodies), or release of antibodies (REAlease Antibodies) or their labels (REAdye_lease Antibodies). Multimarker analysis can identify potential targets for immune therapy against solid tumors. With MICS we analysed human glioblastoma, ovarian and pancreatic carcinoma, and 16 healthy tissues, identifying the pair EPCAM/THY1 as a potential target for chimeric antigen receptor (CAR) T cell therapy for ovarian carcinoma. Using an Adapter CAR T cell approach, we show selective killing of cells only if both markers are expressed. MICS represents a new high-content microscopy methodology widely applicable for…