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Given the high prevalence of lung cancer, an accurate diagnosis is crucial. In the diagnosis process, radiologists play an important role by examining numerous radiology exams to identify different types of nodules. To aid the clinicians' analytical efforts, computer-aided diagnosis can streamline the process of identifying pulmonary nodules. For this purpose, medical reports can serve as valuable sources for automatically retrieving image annotations. Our study focused on converting medical reports into nodule annotations, matching textual information with manually annotated data from the Lung Nodule Database (LNDb)—a comprehensive repository of lung scans and nodule annotations. As a result of this study, we have released a tabular data file containing information from 292 medical reports in the LNDb, along with files detailing nodule characteristics and corresponding matches to the manually annotated data. The objective is to enable further research studies in lung cancer by bridging the gap between existing reports and additional manual annotations that may be collected, thereby fostering discussions about the advantages and disadvantages between these two data types. [ABSTRACT FROM AUTHOR]

Helix mimicry provides probes to perturb protein-protein interactions (PPIs). Helical conformations can be stabilized by joining side chains of non-terminal residues (stapling) or via capping fragments. Nature exclusively uses capping, but synthetic helical mimics are heavily biased towards stapling. This study comprises: (i) creation of a searchable database of unique helical N-caps (ASX motifs, a protein structural motif with two intramolecular hydrogen-bonds between aspartic acid/asparagine and following residues); (ii) testing trends observed in this database using linear peptides comprising only canonical L-amino acids; and, (iii) novel synthetic N-caps for helical interface mimicry. Here we show many natural ASX motifs comprise hydrophobic triangles, validate their effect in linear peptides, and further develop a biomimetic of them, Bicyclic ASX Motif Mimics (BAMMs). BAMMs are powerful helix inducing motifs. They are synthetically accessible, and potentially useful to a broad section of the community studying disruption of PPIs using secondary structure mimics. Researchers mimic protein interface helices by stapling peptide side chains, or replacing hydrogen bonds with covalent ones, and synthetic helical mimics are heavily biased towards stapling. Here the authors describe bioinformatic discovery of hydrophobic triangles at helix N-termini, and rigid, bicyclic synthetic mimics of them. [ABSTRACT FROM AUTHOR]

Emission data collection has always been a significant burden and challenge for Chinese counties to develop a CO2 emission inventory. This paper proposed a fuel consumption-based method to develop a local-specific CO2 emission rate database for Chinese counties using only open-source big data. Localized vehicle fuel consumption data is obtained through natural language processing (NLP) algorithm and large language model (LLM). The emission rates derived by our proposed method are consistent with field test results in literature. Besides, the CO2 emission estimation results using local-specific traffic activity data indicate that our method could effectively improve the accuracy of vehicle emission assessment. Compared with conventional method, the novel approach proposed in this paper can provide a pathway for convenient, universal, and cost-saving assessment for local scale CO2 emission rates. With this method, it is possible to formulate a local-specific CO2 emission database in various Chinese counties using only open-access big data. [ABSTRACT FROM AUTHOR]

In individuals with acne vulgaris, alterations occur in serum metabolite composition, yet the exact causal link between these metabolites and acne development remains elusive. Using genome-wide association datasets, we performed bidirectional Mendelian randomization (MR) to investigate the potential causal relationship between 309 serum metabolites and acne vulgaris. We performed sensitivity analysis to evaluate the presence of heterogeneity and pleiotropy. Forward MR analysis found 14 serum metabolites significantly associated with acne vulgaris, and reverse MR analysis found no significant association between acne vulgaris and these serum metabolites. Through validation using data from the FinnGen database of acne vulgaris studies, we found a conclusive and significant correlation between stearoylcarnitine and acne vulgaris. This provides new evidence in the search for new targets for the treatment of acne vulgaris. [ABSTRACT FROM AUTHOR]

The gene products of PRS1-PRS5 in Saccharomyces cerevisiae are responsible for the production of PRPP (5-phospho-D-ribosyl-α-1-pyrophosphate). However, it has been demonstrated that they are also involved in the cell wall integrity (CWI) signalling pathway as shown by protein–protein interactions (PPIs) with, for example Slt2, the MAP kinase of the CWI pathway. The following databases: SGD, BioGRID and Hit Predict, which collate PPIs from various research papers, have been scrutinized for evidence of PPIs between Prs1-Prs5 and components of the CWI pathway. The level of certainty in PPIs was verified by interaction scores available in the Hit Predict database revealing that well-documented interactions correspond with higher interaction scores and can be graded as high confidence interactions based on a score > 0.28, an annotation score ≥ 0.5 and a method-based high confidence score level of ≥ 0.485. Each of the Prs1-Prs5 polypeptides shows some degree of interaction with the CWI pathway. However, Prs5 has a vital role in the expression of FKS2 and Rlm1, previously only documented by reporter assay studies. This report emphasizes the importance of investigating interactions using more than one approach since every method has its limitations and the use of different methods, as described herein, provides complementary experimental and statistical data, thereby corroborating PPIs. Since the experimental data described so far are consistent with a link between PRPP synthetase and the CWI pathway, our aim was to demonstrate that these data are also supported by high-throughput bioinformatic analyses promoting our hypothesis that two of the five PRS-encoding genes contain information required for the maintenance of CWI by combining data from our targeted approach with relevant, unbiased data from high-throughput analyses. [ABSTRACT FROM AUTHOR]

The northern giant hornet Vespa mandarinia (NGH) is a voracious predator of other insect species, including honey bees. NGH's native range spans subtropical and temperate regions across much of east and southeast Asia and, in 2019, exotic populations of the species were discovered in North America. Despite this broad range and invasive potential, investigation of the population genomic structure of NGH across its native and introduced ranges has thus far been limited to a small number of mitochondrial samples. Here, we present analyses of genomic data from NGH individuals collected across the species' native range and from exotic individuals collected in North America. We provide the first survey of whole-genome population variation for any hornet species, covering this species' native and invasive ranges, and in doing so confirm likely origins in Japan and South Korea for the two introductions. We additionally show that, while this introduced population exhibited strongly elevated levels of inbreeding, these signatures of inbreeding are also present in some long-standing native populations, which may indicate that inbreeding depression alone is insufficient to prevent the persistence of NGH populations. As well as highlighting the importance of ongoing monitoring and eradication efforts to limit the spread of this species outside of its natural range, our data will serve as a foundational database for future genomic studies into introduced hornet populations. [ABSTRACT FROM AUTHOR]

Humans' ability to manage their emotions has a big impact on their ability to plan and make decisions. In order to better understand people and improve human–machine interaction, researchers in affective computing and artificial intelligence are investigating the detection and recognition of emotions. However, different cultures have distinct ways of expressing emotions, and the existing emotion recognition datasets and models may not effectively capture the nuances of the Indian population. To address this gap, this study proposes custom-built lightweight Convolutional Neural Network (CNN) models that are optimized for accuracy and computational efficiency. These models are trained and evaluated on two Indian emotion datasets: The Indian Spontaneous Expression Dataset (ISED) and the Indian Semi Acted Facial Expression Database (iSAFE). The proposed CNN model with manual feature extraction provides remarkable accuracy improvement of 11.14% for ISED and 4.72% for iSAFE datasets as compared to baseline, while reducing the training time. The proposed model also surpasses the accuracy produced by pre-trained ResNet-50 model by 0.27% ISED and by 0.24% for the iSAFE dataset with significant improvement in training time of approximately 320 s for ISED and 60 s for iSAFE dataset. The suggested lightweight CNN model with manual feature extraction offers the advantage of being computationally efficient and more accurate compared to pre-trained model making it a more practical and efficient solution for emotion recognition among Indians. [ABSTRACT FROM AUTHOR]

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI. [ABSTRACT FROM AUTHOR]

Although more than half of all genes generate transcripts that differ in 3′UTR length, current analysis pipelines only quantify the amount but not the length of mRNA transcripts. 3′UTR length is determined by 3′ end cleavage sites (CS). We map CS in more than 200 primary human and mouse cell types and increase CS annotations relative to the GENCODE database by 40%. Approximately half of all CS are used in few cell types, revealing that most genes only have one or two major 3′ ends. We incorporate the CS annotations into a computational pipeline, called scUTRquant, for rapid, accurate, and simultaneous quantification of gene and 3′UTR isoform expression from single-cell RNA sequencing (scRNA-seq) data. When applying scUTRquant to data from 474 cell types and 2134 perturbations, we discover extensive 3′UTR length changes across cell types that are as widespread and coordinately regulated as gene expression changes but affect mostly different genes. Our data indicate that mRNA abundance and mRNA length are two largely independent axes of gene regulation that together determine the amount and spatial organization of protein synthesis. While gene expression analysis is commonly performed, 3′UTR length analysis is limited due to technical challenges. Here the authors provide an open-access analysis pipeline for scRNA-seq data to simultaneously quantify gene expression and 3′UTR length. [ABSTRACT FROM AUTHOR]

Transshipment is one of the most common activities occurring between carriers and fishing vessels to exchange fish, as well as fuel, crew, and gear at sea or at port. While transshipment reduces the need for the fishing vessels to visit ports to offload their catches, thus increasing their efficiency, research has shown that this activity is also one of the major facilitators of IUU fishing. Research on transshipments is limited, and the few studies that are available on the subject focus on identifying hotspots of transshipment, and networks of actors involved. No study to date has examined the role ports play in facilitating transshipment activities, nor are there any studies that examine whether ports that are affiliated with China (the country with the highest IUU Fishing Index Score and 38% of the global share of distant-water fleets) are more likely to experience disproportionately higher volumes of FOC-flagged carrier vessel visits. Therefore, using the carrier vessel portal database from Global Fishing Watch, which contains information on the origin and destination ports of the carrier vessels involved in transshipment activities between 2015 and 2022, this research aims to (a) understand hot spots of FOC-flagged carrier vessel activity in the high seas and where such vessels offload their catches around the world; and (b) empirically test the characteristics of the ports (and the countries where these ports are located) used by these vessels to offload their catches. Findings suggest that ports that experience higher volumes of fishing vessel traffic, are in close proximity to high-seas transshipment activities involving FOC-flagged carriers, are designated ports of entry for foreign vessels, are Chinese-affiliated, as well as have low monitoring, control, and surveillance capacity are significantly more likely to be visited by FOC-flagged high-risk carrier vessels. This research proposes policy recommendations deriving from the findings of this research. [ABSTRACT FROM AUTHOR]

Globally, slope failures cause severe disasters and substantial financial losses annually. Recent advancements in machine learning (ML) algorithms and dataset collection have created alternate solutions for complex slope stability problems. However, rock slope stability prediction remains a challenging problem due to factors such as inadequate data and insufficient generalization performance of rock slope prediction models. The black-box nature of AI models also causes further criticism of using such models to address issues such as slope stability. In this study, we proposed an artificial intelligence (AI) based technique for rock slope stability prediction based on evolutionary and ML algorithms. The proposed GA-XGBoost model uses XGBoost to model the relationship between the input and output parameters of rock slopes, while Genetic Algorithm (GA) optimizes the hyperparameters of XGBoost. A comprehensive rock slope database of 7525 slope cases is implemented in this study to develop and verify the model. The model attains an impressive performance score of R 2 = 0.9999 , M A E = 0.8006 , and R M S E = 1.8624 on the training dataset and R 2 = 0.9934 , M A E = 2.2793 , and R M S E = 11.1090 on the testing dataset. Furthermore, to assess the relative significance of the various influential slope parameters, the SHapley Additive exPlanations (SHAP) algorithm is implemented. This step enables the physical and quantitative interpretations of dependencies between the input and output variables. Generally, this relationship is hidden in traditional machine learning algorithms. [ABSTRACT FROM AUTHOR]

Underwater acoustic target recognition plays an essential role in sonar signal processing. Despite numerous efforts in target recognition, it remains a challenging task due to the complex nature of the underwater environment. Specifically, the overlapping acoustic feature of different target classes, coupled with the temporal-spatial variation characteristic of the marine environment, results in reduced recognition performance. This paper introduces a novel approach to address these challenges, which emphasizes the acquisition of fine-grained feature parameters and achieving high-precision classification results. Firstly, we develop a wavelet-auditory feature that comprehensively represents the underwater acoustic signal. The feature describes the time-frequency auditory information and reduces the dimensionality of the original signal. Secondly, a convolutional recurrent neural network with residual blocks module is designed, which enables the extraction of more discriminative global deep features from the wavelet-auditory feature. The network addresses the effects of time-varying and time-dependent nonuniform underwater environments. Finally, we conduct experiments on the ShipsEar database to evaluate the proposed method. Experimental results demonstrate that our method outperforms other classification methods in terms of recognition accuracy. Furthermore, the efficacity of each method component has been demonstrated via ablation studies, demonstrating that the proposed method is a significant initiative and contribution to the underwater acoustic target recognition task. [ABSTRACT FROM AUTHOR]

In this paper, we present our effort to develop an automatic speaker verification (ASV) system for low resources children's data. For the children's speakers, very limited amount of speech data is available in majority of the languages for training the ASV system. Developing an ASV system under low resource conditions is a very challenging problem. To develop the robust baseline system, we merged out of domain adults' data with children's data to train the ASV system and tested with children's speech. This kind of system leads to acoustic mismatches between training and testing data. To overcome this issue, we have proposed spectral warping based data augmentation. We modified adult speech data using spectral warping method (to simulate like children's speech) and added it to the training data to overcome data scarcity and mismatch between adults' and children's speech. The proposed data augmentation gives 20.46% and 52.52% relative improvement (in equal error rate) for Indian Punjabi and British English speech databases, respectively. We compared our proposed method with well known data augmentation methods: SpecAugment, speed perturbation (SP) and vocal tract length perturbation (VTLP), and found that the proposed method performed best. The proposed spectral warping method is publicly available at https://github.com/kathania/Speaker-Verification-spectral-warping. [ABSTRACT FROM AUTHOR]

Automatic document authentication is a complex task. The aim is to prove that the document at hand is not a fraudulent one. This can be achieved through a fingerprint that is based on the document's content. To this end, it is necessary to analyze and describe the different constituent elements of the document: graphics, text, tables, as well as the layout. In this context, this article focuses on layout description and authentication. The Delaunay layout descriptor Eskenazi et al. 2015 is a robust descriptor allowing the fast comparison and authentication of layouts based on the spatial relationships of the regions composing the document. As the page layout description needs a segmentation of the document into regions, the Delaunay layout descriptor does not allow to match an authentic copy with the original when the number of segmented regions is different for both documents. This is mainly due to the use of a global matching approach. To overcome this drawback, we present a new refined matching algorithm for the Delaunay layout descriptor, which combines global and local matching. Furthermore, we present a storage and retrieval scheme to match a Delaunay layout descriptor efficiently with a layout database. In addition to its ability of comparing layouts with a different number of segmented regions, the proposed method outperforms related work. We obtain respectively a false negative and false positive rate of 0.011 and 0.0 for a data set of printed and scanned layouts, and of 0.3978 and 0.0029 for a data set of real documents. [ABSTRACT FROM AUTHOR]

The demand for home sleep apnea testing (HSAT) devices is escalating, particularly in the context of the coronavirus 2019 (COVID-19) pandemic. The absence of standardized development and verification procedures poses a significant challenge. This study meticulously analyzed the approval process characteristics of HSAT devices by the U.S. Food and Drug Administration (FDA) from September 1, 2003, to September 1, 2023, with a primary focus on ensuring safety and clinical effectiveness. We examined 58 reports out of 1046 that underwent FDA clearance via the 510(k) and de novo pathways. A substantial surge in certifications after the 2022 pandemic was observed. Type-3 devices dominated, signifying a growing trend for both home and clinical use. Key measurement items included respiration and sleep analysis, with the apnea–hypopnea index (AHI) and sleep stage emerging as pivotal indicators. The majority of FDA-cleared HSAT devices adhered to electrical safety and biocompatibility standards. Critical considerations encompass performance and function testing, usability, and cybersecurity. This study emphasized the nearly indispensable role of clinical trials in ensuring the clinical effectiveness of HSAT devices. Future studies should propose guidances that specify stringent requirements, robust clinical trial designs, and comprehensive performance criteria to guarantee the minimum safety and clinical effectiveness of HSATs. [ABSTRACT FROM AUTHOR]

Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores. [ABSTRACT FROM AUTHOR]

Global scientific output is expanding exponentially, which in turn calls for a better understanding of the science of science and especially how the boundaries of scientific fields expand through processes of emergence. The present study proposes the application of embedded topic modeling techniques to identify new emerging science via knowledge recombination activities as evidenced through the analysis of research publication metadata. First, a dataset is constructed from metadata derived from the Web of Science Core Collection database. The dataset is then used to generate a global map representing a categorical scientific co-occurrence network. A research field is defined as interdisciplinary when multiple science categories are listed in its description. Second, the co-occurrence networks are subsequently compared between periods to determine changing patterns of influence in light of interdisciplinarity. Third, embedded topic modeling enables unsupervised association of interdisciplinary classification. We present the results of the analysis to demonstrate the emergence of global interdisciplinary sciences and further we perform qualitative validation on the results to identify what the sources of the emergent areas are. Based on these results, we discuss potential applications for identifying emergence through the merging of global interdisciplinary domains. [ABSTRACT FROM AUTHOR]

In an increasingly volatile, uncertain, complex, and ambiguous (VUCA) world, the importance of organizational resilience has grown, yet the scholarly literature on this topic remains fragmented. To address this gap, our study conducted a bibliometric analysis of 469 articles from the Scopus database using VOSViewer software to systematically review and map trends, gaps, and significant contributions in the field. Our analysis revealed key themes such as resilience, crisis management, innovation, COVID-19, dynamic capabilities, sustainability, and change management, which are crucial to understanding organizational resilience. The findings highlight that the United Kingdom and the University of Oulu are significant contributors to this research area, with notable authors including Duchek E., Martinelli E., Santoro G., Williams T.A., and Youssef C.M. playing a pivotal role in advancing this field. By providing a comprehensive overview of institutional affiliations, countries, authors, journals, publications, and keyword co-occurrences, our study not only maps the landscape of organizational resilience research but also identifies critical areas for future inquiry. This contribution enhances both theoretical and practical understandings of organizational resilience, aiding practitioners in developing robust strategies to navigate the challenges of the VUCA world. [ABSTRACT FROM AUTHOR]

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development. [ABSTRACT FROM AUTHOR]

In the case of hepatocellular carcinoma, there is a need to find novel immune biomarkers to predict cancer prognosis, which will help prolong patient survival. On the basis of these findings, we explored the role of the hub genes in hepatocellular carcinoma via computational analysis for future immunotherapy. To study this phenomenon, we selected three datasets downloaded from the GEO database (GSE25097, GSE76427 and GSE84402). The gene expression analysis platform (GEAP) online tool was used for the data analysis to identify the DEGs. Functional enrichment analysis was performed by GO and KEGG enrichment analysis. The genes associated with these genes were identified via Cytoscape software. Immune cell infiltration and correlation analysis were used to screen the hub genes. The results revealed that the PTTG1, NCAPG, RACGAP1, PBK, ASPM, AURKA, CDCA5, KIF20A, MELK and PRC1 genes were correlated with immune targets, and these hub gene biomarkers will aid in future cancer prognosis and immunotherapy targeting in hepatocellular carcinoma patients. [ABSTRACT FROM AUTHOR]

Geographic Information Systems (GIS)-based technologies are increasingly crucial in the domain of cultural heritage conservation, facilitating the construction of dynamic information management systems and serving as robust platforms for research and display. This review utilizes CiteSpace and Bibliometrix R language to perform a bibliometric analysis of academic literature sourced from the Web of Science (WoS) Core Collection, focusing on the application of GIS in cultural heritage conservation. The analysis covers a broad spectrum of academic articles, identifying research hotspots, patterns of national cooperation, interdisciplinary mobility, knowledge structure, and developmental trends. The findings reveal that this research area is experiencing a phase of steady growth. While three emerging trends have been identified, demonstrating significant theoretical and technical advancements, there remains considerable potential for enhancing in their practical implication within conservation efforts. The study advocates for the integration of digital technologies into the humanities, emphasizing the need for a heritage database equipped with standardized data exchange protocols to support display and analytical functions. This systematic research approach not only illuminates new strategies for the inheritance and innovation in the conservation of cultural heritage, but also paves the way for future explorations in this increasingly vital field. [ABSTRACT FROM AUTHOR]

The increasing prevalence of Internet of Things (IoT) systems has made them attractive targets for malicious actors. To address the evolving threats and the growing complexity of detection, there is a critical need to search for and develop new algorithms that are fast and robust in detecting and classifying dangerous network traffic. In this context, deep reinforcement learning (DRL) is gaining recognition as a prospective solution in numerous fields as it enables autonomous agents to cooperate with their environment for decision-making without relying on human experts. This article presents an innovative approach to intrusion detection in IoT systems using an adversarial reinforcement learning (RL) algorithm known for its exceptional predictive capabilities. The predictive process relies on a classifier, implemented as a streamlined and highly efficient neural network. Embedded within this classifier is a policy function meticulously trained using an innovative RL model. Importantly, this model ensures that the environment's behavior is dynamically fine-tuned simultaneously with the learning process, improving the overall effectiveness of the intrusion detection approach. The efficiency of our proposal was assessed using the Bot-IoT database, consisting of a mixture of legitimate IoT network traffic and simulated attack scenarios. Our scheme shows superior performance compared to existing ones. Therefore, our approach to IoT intrusion detection can be considered a valuable alternative to existing methods, capable of significantly improving the IoT systems' security. [ABSTRACT FROM AUTHOR]

The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose challenges for first-principles simulations and experimental techniques. In this study, we leverage machine learning and a first-principles database to analyze the distribution of surface oxygen vacancies (VO) and induced small polarons on rutile TiO2(110), effectively disentangling the interactions between polarons and defects. By combining neural-network supervised learning and simulated annealing, we elucidate the inhomogeneous VO distribution observed in scanning probe microscopy (SPM). Our approach allows us to understand and predict defective surface patterns at enhanced length scales, identifying the specific role of individual types of defects. Specifically, surface-polaron-stabilizing VO-configurations are identified, which could have consequences for surface reactivity. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle. The continually developing Tracking Atmospheric Rivers Globally as Elongated Targets (tARget) algorithm identifies AR objects at individual time steps based on thresholding integrated water vapor transport (IVT) and other requirements, and tracks each AR object in time and space. Building on previous versions of tARget, this paper discusses further refinements to the algorithm to better handle ARs in tropical and polar areas, as well as "zonal" ARs which the previous versions of the algorithm were not designed to capture. This further regionally refined algorithm is applied to the ERA5 reanalysis over 1940–2023 at 6 h intervals and a 0.25° × 0.25° horizontal resolution. The AR detection results are evaluated in terms of key AR characteristics. We anticipate this regionally refined global AR database will aid further understanding of ARs such as AR process studies, evaluation of AR simulations and predictions, and assessment of climate change impacts on ARs. [ABSTRACT FROM AUTHOR]

Objective: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. Materials and methods: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. Results: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05–329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707–0.971), 0.873 (0–0.997), and 0.894 (0.025–0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). Conclusion: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models. [ABSTRACT FROM AUTHOR]

Purpose: The detection rate of Salmonella enterica serovar 1,4,[5], 12: i: - (S. 1,4,[5], 12: i: -) has increased as the most common serotype globally. A S. 1,4,[5], 12: i: - strain named ST3606 (sequence type 34), isolated from a fecal specimen of a child with acute diarrhea hospitalized in a tertiary hospital in China, was firstly reported to be resistant to carbapenem and ceftazidime-avibactam. The aim of this study was to characterize the whole-genome sequence of S. 1,4,[5], 12: i: - isolate, ST3606, and explore its antibiotic resistance genes and their genetic environments. Methods: The genomic DNA of S. 1,4,[5], 12: i: - ST3606 was extracted and performed with single-molecule real-time sequencing. Resistance genes, plasmid replicon type, mobile elements, and multilocus sequence types (STs) of ST3606 were identified by ResFinder 3.2, PlasmidFinder, OriTfinder database, ISfinder database, and MLST 2.0, respectively. The conjugation experiment was utilized to evaluate the conjugation frequency of pST3606-2. Protein expression and enzyme kinetics experiments of CTX-M were performed to analyze hydrolytic activity of a novel CTX-M-261 enzyme toward several antibiotics. Results: Single-molecule real-time sequencing revealed the coexistence of a 109-kb IncI1-Iα plasmid pST3606-1 and a 70.5-kb IncFII plasmid pST3606-2. The isolate carried resistance genes, including blaNDM-5, sul1, qacE, aadA2, and dfrA12 in pST3606-1, blaTEM-1B, aac(3)-lld, and blaCTX-M-261, a novel blaCTX-M-1 family member, in pST3606-2, and aac(6')-Iaa in chromosome. The blaCTX-M-261 was derived from blaCTX-M-55 by a single-nucleotide mutation 751G>A leading to amino acid substitution of Val for Met at position 251 (Val251Met), which conferred CTX-M increasing resistance to ceftazidime verified by antibiotics susceptibility testing of transconjugants carrying pST3606-2 and steady-state kinetic parameters of CTX-M-261. pST3606-1 is an IncI1-α incompatibility type that shares homology with plasmids of pC-F-164_A-OXA140, pE-T654-NDM-5, p_dm760b_NDM-5, and p_dmcr749c_NDM-5. The conjugation experiment demonstrated that pST3606-2 was successfully transferred to the Escherichia coli recipient C600 with four modules of OriTfinder. Conclusion: Plasmid-mediated horizontal transfer plays an important role in blaNDM-5 and blaCTX-M-261 dissemination, which increases the threat to public health due to the resistance to most β-lactam antibiotics. This is the first report of blaCTX-M-261 and blaNDM-5 in S. 1,4,[5], 12: i: -. The work provides insights into the enzymatic function and demonstrates the ongoing evolution of CTX-M enzymes and confirms urgency to control resistance of S. 1,4,[5], 12: i: -. [ABSTRACT FROM AUTHOR]

Introduction: Spontaneous reporting of adverse events (AEs) is a mainstay of pharmacovigilance, and an ongoing challenge is how to ensure that more high-quality reports are collected for comprehensive information provision. The Med Safety App, a smartphone-based application, was launched in Nigeria in November 2020 to provide an electronic platform for users to seamlessly report AEs. There has been a paucity of evidence on the use of this application or other mobile applications for reporting adverse drug reactions/AEs following immunization in the Nigerian environment. Objective: The aim of this study was to evaluate the trends in adverse event reporting before and after the introduction of the Med Safety App in Nigeria. Methods: This was a retrospective, observational study using data from the VigiFlow database to compare adverse event reporting in Nigeria before and after the deployment of the Med Safety App. The baseline period was 1st April 2019 to 30th October 2020 and the comparison period was 1st November 2020 to 31st May 2022. We used Vigilance Hub, the back-end system for the Med Safety App, to extract data on App downloads and de-identified user statistics. Data were summarized using descriptive statistics, frequencies and proportions. Quality was assessed by assigning a completeness score to each individual case safety report. The Kruskal–Wallis test was used to test for differences in medians between groups. Results: Following deployment of the App, the Nigerian National Pharmacovigilance Centre recorded an increase in the total number of adverse event reports received in VigiFlow, from 2051 in the baseline period to 18,995 following deployment of the App, with 81.7% of those reported via the Med Safety App. There was a reduction in the proportion of paper-based reporting from 98.4 to 15.7% post-deployment, and direct reporting by consumers increased from 2.7 to 17.6%. Of the 15,526 reports submitted via the App, 15,111 (97.3%) had a completeness score above 70% and 6993 (45%) had a completeness score of 100%. The median completeness score of adverse event reports on the Med Safety App was 6 out of 7. On bivariate analysis using the Kruskal–Wallis test, there was an association between means of reporting and completeness score, and this association was significant, with a p value of 0.0001, which may reflect the validation rules that are applied within the App. Conclusion: Deployment of the Med Safety App increased both the number and quality of adverse event reports; however, more awareness and capacity building are needed to strengthen and sustain reporting on the tool by all categories of healthcare professionals and consumers/patients. [ABSTRACT FROM AUTHOR]

The present research introduces the best architecture model for predicting the unsoaked California bearing ratio (CBRu) of soil by comparing the models based on the least square support vector machine (LSSVM), long- short-term memory (LSTM), and artificial neural network (ANN) approach. The two kernel functions, linear and polynomial, have been selected to create LSSVM models. The developed LSTM models have been optimized by the Adam algorithm. In the employed ANN models, the Levenberg–Marquardt (LM), BFGS Quasi-Newton (BFG), scaled conjugate gradient (SCG), gradient descent with momentum (GDM), gradient descent (GD), and gradient descent with adaptive learning (GDA) algorithms have been used in the backpropagation process. For this purpose, three databases, such as training, testing and validation, have been compiled from the published research. A laboratory database has been developed by performing laboratory experiments for soil samples collected from and around Kota, Rajasthan, used for cross-validation of the best architecture model. The statistical tools, such as root means square error (RMSE), mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), variance accounted for (VAF), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), normalized mean bias error (NMBE), Legate and McCabe's index (LMI), root mean square error to observation's standard deviation ratio (RSR), a20-index, index of agreement (IOA) and index of scatter (IOS) have been used to measure the performance of the models. The LSTM model MD 14 has achieved higher performance and accuracy (RMSE = 0.9127%, MAE = 0.8114%, R = 0.9863%, MAPE = 9.0772%, VAF = 97.26, WMAPE = 0.0669%, NS = 0.9708, NMBE = 0.0687%, LMI = 0.1926, RSR = 0.1708, a20-index = 93.88, IOA = 0.9037 and IOS = 0.0752) in testing phase. For the performance validation, model (MD) 14 has predicted the CBRu of the validation database. Also, model MD 14 has attained higher performance (RMSE = 1.2671%, MAE = 1.0161%, R = 0.9909) in the validation phase. By comparing the performances and performing score analysis, the LSTM model MD 14 has been recognized as the best architecture model for predicting the unsoaked CBR of soil. Moreover, model MD 14 has gained over 96% (R = 0.9689) accuracy in predicting the CBRu of laboratory-tested soil samples. The present research also represents that the nonlinear approach has achieved higher performance with a high overfitting ratio. In addition, the artificial neural network requires a large database to predict the unsoaked CBR with higher performance and the least overfitting ratio. The present research also rejects the null hypothesis of normality. Sensitivity analysis illustrates that gravel content and maximum dry density of soil affect the prediction of unsoaked CBR. [ABSTRACT FROM AUTHOR]

Study Design: Systematic review and meta-analysis. Objective: There is some controversy about the effects of calcitonin (CT) on lumbar spinal stenosis (LSS). This systematic review and meta-analysis is to assess the strength of the evidence supporting the use of CT in the treatment of patients with LSS. Material and Method: We performed an electronic search depicting randomized controlled trials (RCTs) through 4 databases from the date of database creation to January 2023. 3 different researchers conducted independent literature screening, data extractions, and quality assessments. The outcome measures included visual analogue scale (VAS), walking distance, and oswestry disability index (ODI). Meta-analysis and trial sequence analysis (TSA) were carried out using RevMan 5.4, Stata 16.0, and TSA 0.9. GRADE 3.6 was used to evaluate the evidence quality. Results: We accepted 9 studies with 496 participants. The meta-analysis revealed that CT offered no significant improvement in VAS, walking distance, or ODI in patients with LSS. Conclusion: There is no evidence that CT has a benefit in patients with LSS, either alone or in combination with other treatments, or depending on the route of administration, according to the systematic review and meta-analysis of relevant RCTs. [ABSTRACT FROM AUTHOR]

Background: In 2012, the American Society of Anesthesiologists (ASA) published guidelines recommending against routine preoperative laboratory testing for low‐risk patients to reduce unnecessary medical expenditures. The aim of this study was to assess the change in routine preoperative laboratory testing in low‐risk versus higher‐risk patients before and after release of these guidelines. Methods: The ACS‐NSQIP database, 2005–2018, was separated into low‐risk versus higher‐risk patients based upon a previously published stratification. The guideline implementation date was defined as January 2013. Changes in preoperative laboratory testing over time were compared between low‐ and higher‐risk patients. A difference‐in‐differences model was applied. The primary outcome included any laboratory test obtained ≤90 days prior to surgery. Results: Of 7,507,991 patients, 972,431 (13.0%) were defined as low‐risk and 6,535,560 (87.0%) higher‐risk. Use of any preoperative laboratory test declined in low‐risk patients from 66.5% before to 59.6% after guidelines, a 6.9 percentage point reduction, versus 93.0%–91.9% in higher‐risk patients, a 1.1 percentage point reduction (p < 0.0001, comparing percentage point reductions). After risk‐adjustment, the adjusted odds ratio for having any preoperative laboratory test after versus before the guidelines was 0.77 (95% CI 0.76–0.78) in low‐risk versus 0.93 (0.92–0.94) in higher‐risk patients. In low‐risk patients, lack of any preoperative testing was not associated with worse outcomes. Conclusions: While a majority of low‐risk patients continue to receive preoperative laboratory testing not recommended by the ASA, there has been a decline after implementation of guidelines. Continued effort should be directed at the deimplementation of routine preoperative laboratory testing for low‐risk patients. [ABSTRACT FROM AUTHOR]

Background: For patients with gastric cancer, a well‐balanced treatment that considers both oncological aspects and surgical risk is demanded. This study aimed to explore the optimal extent of lymph node dissection (LND) for patients with gastric cancer according to surgical risk, stratified by the risk calculator system produced by the Japan National Clinical Database (NCD). Patients and Methods: We retrospectively evaluated 187 patients who underwent radical gastrectomy for gastric cancer. Using the median predicted anastomotic leak rate obtained by the NCD risk calculator as the cutoff value, we classified 97 and 90 patients as having high and low risks, respectively. Results: In low‐risk patients, although limited LND reduced the postoperative intraabdominal infectious complications (IAIC), multivariate analysis revealed standard LND as an independent prognostic factor that improved Relapse‐free survival (RFS). In high‐risk patients, the rates of postoperative IAIC and RFS were similar between standard and limited LND. Pancreatic fistula was not observed in the limited dissection group. Conclusion: Limited LND might be the optimal treatment strategy for patients with gastric cancer with high surgical risk. [ABSTRACT FROM AUTHOR]

Background: The introduction of laparoscopy in 1989 revolutionized surgical practices, reducing post-operative complications, and enhancing outcomes. Despite its benefits, limitations in laparoscopic tools have led to continued use of open surgery. Robotic-assisted surgery emerged to address these limitations, but its adoption trends and potential impact on open and laparoscopic surgery require analysis. Methods: A retrospective analysis used the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) databases from 2012 to 2021. The study encompassed various abdominal procedures, employing Vector Autoregressive (VAR) models to analyze the dynamic relationships between surgical techniques. The models predicted future trends in open, laparoscopic, and robotic surgery until Q2 of 2025. Results: The analysis included 360,171 patients across diverse procedures. In urology, robotic surgery dominated prostatectomies (83.1% in 2021) and nephrectomies (55.1% in 2021), while the open approach remained the predominant surgical technique for cystectomies (72.5% in 2021). In general surgery, robotic colectomies were forecasted to surpass laparoscopy, becoming the primary approach by 2024 (45.7% in 2025). Proctectomies also showed a shift towards robotic surgery, predicted to surpass laparoscopy and open surgery by 2025 (32.3%). Pancreatectomies witnessed a steady growth in robotic surgery, surpassing laparoscopy in 2021, with forecasts indicating further increase. While hepatectomies remained predominantly open (70.0% in 2025), esophagectomies saw a rise in robotic surgery, predicted to become the primary approach by 2025 (52.3%). Conclusions: The study suggests a transformative shift towards robotic-assisted surgery, poised to dominate various minimally invasive procedures. The forecasts indicate that robotic surgery may surpass laparoscopy and open surgery in colectomies, proctectomies, pancreatectomies, and esophagectomies by 2025. This anticipated change emphasizes the need for proactive adjustments in surgical training programs to align with evolving surgical practices. The findings have substantial implications for future healthcare practices, necessitating a balance between traditional laparoscopy and the burgeoning role of robotic-assisted surgery. [ABSTRACT FROM AUTHOR]

Background: Urticaria is a clinical condition characterized by the appearance of wheals (hives), angioedema, or both. Over the last several decades, a better understanding of the mechanisms at play in the immunopathogenesis of urticaria has underscored the existence of numerous urticaria subtypes. Separating the different kinds of urticaria explicitly helps find the best detection method for the management of this skin disorder. Subtypes of urticaria also include both spontaneous and physical types. The conventional ones include spontaneous urticaria, constituting both acute and chronic urticaria. Therefore, a broad and effective therapy is essential for the diagnosis and treatment of urticaria. Methods: To understand the immunopathogenesis of urticaria, various databases, including PubMed, Scopus, and Web of Science, were used to retrieve original articles and reviews related to urticaria. While information on several clinical trials were obtained from clinicaltrials.gov database. Results: This article highlights the immunopathogenesis involved in the intricate interaction between cellular infiltration, immune reactions, coagulation cascades, and autoantibodies that underlie urticaria's pathophysiology. Conclusion: The recent progress in understanding urticaria can help to understand the intricate characteristics in the immunopathogenesis of urticaria and could play a beneficial role in the management of urticaria. [ABSTRACT FROM AUTHOR]

Background: The management of uncontrolled severe asthma has greatly improved since the advent of novel biologic therapies. Up to August 2022, five biologics have been approved for the type 2 asthma phenotype: anti-IgE (omalizumab), anti-IL5 (mepolizumab, reslizumab, benralizumab), and anti-IL4 (dupilumab) monoclonal antibodies. These drugs are usually well tolerated, although long-term safety information is limited, and some adverse events have not yet been fully characterized. Spontaneous reporting systems represent the cornerstone for the detection of potential signals and evaluation of the real-world safety of all marketed drugs. Objective: The aim of this study was to provide an overview of safety data of biologics for severe asthma using VigiBase, the World Health Organization global pharmacovigilance database. Methods: We selected all de-duplicated individual case safety reports (ICSRs) attributed to five approved biologics for severe asthma in VigiBase, up to 31st August 2022 (omalizumab, mepolizumab, reslizumab, benralizumab and dupilumab). Descriptive frequency analyses of ICSRs were carried out both as a whole class and as individual products. Reporting odds ratios (ROR) with 95% confidence intervals (CIs) were used as the measure of disproportionality for suspected adverse drug reactions (ADRs) associated with the study drugs compared with either all other suspected drugs (Reference Group 1, RG1) or inhaled corticosteroids plus long-acting β-agonists (ICSs/LABAs) (Reference Group 2, RG2) or with oral corticosteroids (OCSs) (Reference Group 3, RG3). Results: Overall, 31,724,381 ICSRs were identified in VigiBase and 167,282 (0.5%) were related to study drugs; the remaining reports were considered as RG1. Stratifying all biologic-related ICSRs by therapeutic indication, around 29.4% (n = 48,440) concerned asthma use; omalizumab was mainly indicated as the suspected drug (n = 20,501), followed by dupilumab, mepolizumab, benralizumab and reslizumab. Most asthma ICSRs concerned adults (57%) and women (64.1%). Asthma biologics showed a higher frequency of serious suspected ADR reporting than RG1 (41.3% vs 32.3%). The most reported suspected ADRs included asthma, dyspnea, product use issue, drug ineffective, cough, headache, fatigue and wheezing. Asthma biologics were disproportionally associated with several unknown or less documented adverse events, such as malignancies, pulmonary embolism and deep vein thrombosis with omalizumab; alopecia and lichen planus with dupilumab; alopecia and herpes infections with mepolizumab; alopecia, herpes zoster and eosinophilic granulomatosis with polyangiitis related to benralizumab; and alopecia with reslizumab. Conclusions: The most frequently reported suspected ADRs of asthma biologics in VigiBase confirmed the presence of well-known adverse effects such as general disorders, injection-site reactions, nasopharyngitis, headache and hypersensitivity, while some others (e.g. asthma reactivation or therapeutic failure) could be ascribed to the indication of use. Moreover, the analysis of signals of disproportionate reporting suggests the presence of malignancies, effects on the cardiovascular system, alopecia and autoimmune conditions, requiring further assessment and investigation. [ABSTRACT FROM AUTHOR]

One of the major concerns affecting the public health is microbial pathogens. Commercial antibiotics have a wide range of applications, however harmful microorganisms have increased their resistance to antibiotics, making the fight against these pathogens difficult. For many years, scientists have focused on finding natural sources with strong antimicrobial activity. At this point, microalgae cells have attracted great attention due to their biological activities including, antibacterial, antifungal, and antiviral effects. In order to discover promising strains with strong antimicrobial activity and to obtain interested components efficiently, a thorough scientific approach is needed by considering all steps of the process. This process mainly consisted of strain selection, cultivation, harvesting of biomass, extraction and purification of compounds and screening their antimicrobial properties. Using microalgal compounds against microbial pathogens is still in its early days. In this context, the present review aims to contribute existing database of antimicrobial potential of microalgae. With this aim, the impact of microalgae species and their components were investigated according to their bioactivities against bacteria, fungi, and viruses. The crucial points in this regard were emphasized and important suggestions were presented for further researches. [ABSTRACT FROM AUTHOR]

The study aims to enhance ethanol production by Wickerhamomyces subpelliculosus ZE75 isolated from marine sediment. In addition, analyzing the kinetic parameters of ethanol production and optimization of the fermentation conditions was performed. The marine yeast isolate ZE75 was selected as the front runner ethanol-producer, with an ethanol yield of 89.77 gL−1. ZE75 was identified relying on the phenotypic and genotypic characteristics of W. subpelliculosus. The genotypic characterization based on the Internal Transcribed Spacer (ITS) sequence was deposited in the GenBank database with the accession number OP715873. The maximum specific ethanol production rate (vmax) was 0.482 gg−1 h−1 at 175 gL−1 glucose concentration, with a high accuracy of R2 0.95. The maximum growth specific rates (μmax) were 0.141 h−1 obtained at 150 gL−1 glucose concentration with R2 0.91. Optimization of the fermentation parameters such as pH and salinity has been achieved. The highest ethanol yield 0.5637 gg−1 was achieved in a 100% natural seawater-based medium. The maximum ethanol production of 104.04 gL−1 was achieved at pH 4.5 with a specific ethanol rate of 0.1669 gg−1 h−1. The findings of the present study recommend the possibility of ethanol production from a seawater-based medium on a large scale using W. subpelliculosus ZE75. [ABSTRACT FROM AUTHOR]

Background: Corticosteroids are recommended only for induction of remission in inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD). This study aimed to evaluate the change in pharmacologic treatment use, particularly systemic corticosteroids, over approximately 30 years, and the impact of biologics on IBD treatment since their appearance in the 2000s. Methods: This retrospective study conducted in Japan used data from the Phoenix cohort database (January 1990 to March 2021). Patients with disease onset at age ≥ 10 years who received treatment for UC or CD between January 1990 and March 2021 were included. Outcome measures were change in IBD treatments used, total cumulative corticosteroid doses, initial corticosteroid dose, duration of corticosteroid treatment, and surgery rate. Results: A total of 1066 and 579 patients with UC and CD, respectively, were included. In UC, the rate of corticosteroid use as initial treatment was relatively stable regardless of the year of disease onset; however, in CD, its rate decreased in patients who had disease onset after 2006 (before 2006: 14.3–27.8% vs. after 2006: 6.6–10.5%). Compared with patients with disease onset before biologics became available, cumulative corticosteroid doses in both UC and CD, and the surgery rate in CD only, were lower in those with disease onset after biologics became available. Conclusions: Since biologics became available, corticosteroid use appears to have decreased, with more appropriate use. Furthermore, use of biologics may reduce surgery rates, particularly in patients with CD. UMIN Clinical Trials Registry; UMIN000035384. [ABSTRACT FROM AUTHOR]

Background: The clinicopathological features and prognosis of primary small bowel adenocarcinoma (PSBA), excluding duodenal cancer, remain undetermined due to its rarity in Japan. Methods: We analyzed 354 patients with 358 PSBAs, between January 2008 and December 2017, at 44 institutions affiliated with the Japanese Society for Cancer of the Colon and Rectum. Results: The median age was 67 years (218 males, 61.6%). The average tumor size was 49.9 (7–100) mm. PSBA sites consisted of jejunum (66.2%) and ileum (30.4%). A total of 219 patients (61.9%) underwent diagnostic small bowel endoscopy, including single-balloon endoscopy, double-balloon endoscopy, and capsule endoscopy before treatment. Nineteen patients (5.4%) had Lynch syndrome, and 272 patients (76.8%) had symptoms at the initial diagnosis. The rates for stages 0, I, II, III, and IV were 5.4%, 2.5%, 27.1%, 26.0%, and 35.6%, respectively. The 5-year overall survival rates at each stage were 92.3%, 60.0%, 75.9%, 61.4%, and 25.5%, respectively, and the 5-year disease-specific survival (DSS) rates were 100%, 75.0%, 84.1%, 59.3%, and 25.6%, respectively. Patients with the PSBA located in the jejunum, with symptoms at the initial diagnosis or advanced clinical stage had a worse prognosis. However, multivariate analysis using Cox-hazard model revealed that clinical stage was the only significant predictor of DSS for patients with PSBA. Conclusions: Of the patients with PSBA, 76.8% had symptoms at the initial diagnosis, which were often detected at an advanced stage. Detection during the early stages of PSBA is important to ensure a good prognosis. [ABSTRACT FROM AUTHOR]

Purpose: The relationship between board certification, clinical guideline implementation, and quality of gastric cancer surgery remains unclear. Methods: A web-based questionnaire survey was administered to departments registered in the National Clinical Database (NCD) of Japan between October 2014 and January 2015. Quality indicators (QIs) based on the Donabedian model were evaluated. Structural QIs (e.g., affiliations with academic societies and board certifications) and process QIs (adherence to clinical practice guidelines for gastric cancer) were assessed using risk-adjusted odds ratios (AORs) for surgical mortality. Multivariable logistic regression models with a generalized estimating equation were used. Results: A total of 835 departments performing 40,992 distal gastrectomies and 806 departments performing 19,618 total gastrectomies responded. Some certified institutions and physicians showed significant associations, with lower AORs for surgical mortality. Important process QIs included pre- and postoperative abdominal CT scanning, endoscopic resection based on progression, curative resection with D2 dissection for advanced gastric cancer, laparoscopic surgery, and HER2 testing for patients with unresectable recurrent gastric cancer. Conclusions: Multiple structural and process QIs are associated with surgical mortality after gastrectomy in Japan. Measuring and visualizing QIs may enhance healthcare improvements. [ABSTRACT FROM AUTHOR]

Purpose: To clarify the influence of surgical volume on the mortality and morbidity of gastrointestinal perforation in children in Japan. Methods: We collected data on pediatric patients with gastrointestinal perforation between 2017 and 2019, from the National Clinical Database. The surgical volumes of various institutions were classified into three groups: low (average number of surgeries for gastrointestinal perforation/year < 1), medium (≥ 1, < 6), and high (≥ 6). The observed-to-expected (o/e) ratios of 30-day mortality and morbidity were calculated for each group using an existing risk model. Results: Among 1641 patients (median age, 0.0 years), the 30-day mortality and morbidity rates were 5.2% and 37.7%, respectively. The 30-day mortality rates in the low-, medium-, and high-volume institutions were 4.9%, 5.3%, and 5.1% (p = 0.94), and the 30-day morbidity rates in the three groups were 26.8%, 39.7%, and 37.7% (p < 0.01), respectively. The o/e ratios of 30-day mortality were 1.05 (95% confidence interval [CI] 0.83–1.26), 1.08 (95% CI 1.01–1.15), and 1.02 (95% CI 0.91–1.13), and those of 30-day morbidity were 1.72 (95% CI 0.93–2.51), 1.03 (95% CI 0.79–1.28), and 0.95 (95% CI 0.56–1.33), respectively. Conclusion: Surgical volume does not have significant impact on the outcomes of pediatric gastrointestinal perforation in Japan. [ABSTRACT FROM AUTHOR]

Objective: The primary objective of this study was to find the association between dietary zinc intake and the prevalence of olfactory disorders using data from the National Health and Nutrition Examination Survey (NHANES). Methods: A cross-sectional study was conducted using the 2013–2014 NHANES data. A linear regression model was constructed with dietary zinc intake as the independent variable and olfactory dysfunction as the dependent variable. Initially, in the unadjusted model, weighted logistic regression analysis was carried out for continuous variables, and stratified analysis was conducted for categorical variables. Subsequently, three models were created to perform subgroup analysis by adjusting for different confounding factors, further investigating the relationship between dietary zinc intake and olfactory dysfunction. Finally, restricted cubic spline (RCS) models adjusting for all confounding factors were utilized to study the nonlinear associations of age and dietary zinc intake with olfactory dysfunction and their relevant thresholds. Results: A total of 2958 samples were analyzed in this study. Weighted logistic regression analysis displayed a negative relationship between dietary zinc intake and the prevalence of olfactory dysfunction in the population of non-Hispanic whites and other Hispanics, as well as in individuals with body mass index (BMI) ≥ 25 kg/m2 (OR < 1, P < 0.05). The P values for the multiplicative interaction terms adjusting for all confounding factors were not significant (P for interaction > 0.05). In the three regression models adjusting for different confounding factors, dietary zinc intake was significantly negatively related to olfactory dysfunction in all populations (Crude: OR 0.63, 95% CI 0.44–0.91; Model I: OR 0.58, 95% CI 0.38–0.90; Model II: OR 0.59, 95% CI 0.35–1.00). Subgroup analysis based on BMI showed a remarkable negative relationship between dietary zinc intake and olfactory dysfunction in the group with BMI of 25–30 kg/m2 (Crude: OR 0.50, 95% CI 0.28–0.90, P = 0.012; Model I: OR 0.49, 95% CI 0.24–1.00, P = 0.021) and the group with BMI ≥ 30 kg/m2 (Crude: OR 0.55, 95% CI 0.33–0.92, P = 0.013; Model I: OR 0.51, 95% CI 0.29–0.88, P = 0.005; Model II: OR 0.51, 95% CI 0.29–0.91, P = 0.004). RCS analysis revealed a remarkable nonlinear association of age and dietary zinc intake with olfactory dysfunction (P-non-linear < 0.05). The prevalence of olfactory dysfunction was considerably higher in individuals aged 60 and above compared to those under 60 years old. Daily dietary zinc intake within the range of 9.60–17.45 mg was a protective factor for olfactory dysfunction, while intake outside this range increased the prevalence of olfactory dysfunction. Conclusion: Daily dietary zinc intake within the range of 9.60–17.45 mg has a protective effect against olfactory dysfunction. Intake outside this range increases the prevalence of olfactory dysfunction. The prevalence of olfactory dysfunction is significantly higher in individuals aged 60 and above compared to those under 60 years old. For individuals with a BMI of 25–30 kg/m2 and a BMI ≥ 30 kg/m2, dietary zinc intake is negatively correlated with olfactory dysfunction. Therefore, it is recommended that these populations increase their dietary zinc intake to develop healthier lifestyles and maintain olfactory health. [ABSTRACT FROM AUTHOR]

Cardiovascular disorders are known to be among the most severe diseases and the leading causes of mortality all over the globe. Premature ventricular contractions (PVC) are one of the most prevalent types of cardiac arrhythmia. Recording and analyzing electrocardiogram (ECG) signals is one of the most popular methods (the least intrusive and least expensive) for investigating cardiac disorders. In this study, a new supervised approach for the automated detection of PVC has been developed through a combination of handcrafted feature extraction from ECG signals and deep-learning-based feature reduction. The proposed approach utilized multiple methodologies, namely statistical and chaos analysis in the time domain and time–frequency domain, and morphological assessment, to extract numerous features from ECG signals. Then, a variational autoencoder network is developed as a deep learning-based feature reduction technique to reduce the number of extracted features and obtain the most discriminating features. Finally, a support vector machine, k-nearest neighbor, and neural network classifiers with fivefold cross-validation are utilized to classify ECG signals. The MIT-BIH database is used to evaluate the proposed approach. The numerical results show that the proposed approach performs better than the current state-of-the-art studies. [ABSTRACT FROM AUTHOR]

Since the 1940s, patch tests in healthy volunteers (Human Predictive Patch Tests, HPPTs) have been used to identify chemicals that cause skin sensitization in humans. Recently, we reported the results of a major curation effort to support the development of OECD Guideline 497 on Defined Approaches (DAs) for skin sensitization (OECD in Guideline No. 497: Defined Approaches on Skin Sensitisation, 2021a. https://doi.org/10.1787/b92879a4-en). In the course of this work, we compiled and published a database of 2277 HPPT results for 1366 unique test substances (Strickland et al. in Arch Toxicol 97:2825–2837, 2023. https://doi.org/10.1007/s00204-023-03530-3). Here we report a detailed analysis of the value of HPPT data for classification of chemicals as skin sensitizers under the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (GHS). As a result, we propose the dose per skin area (DSA) used for classification by the GHS to be replaced by or complemented with a dose descriptor that may better reflect sensitization incidence [e.g., the DSA causing induction of sensitization in one individual (DSA1+) or the DSA leading to an incidence of induction in 5% of the tested individuals (DSA05)]. We also propose standardized concepts and workflows for assessing individual HPPT results, for integrating multiple HPPT results and for using them in concert with Local Lymph Node Assay (LLNA) data in a weight of evidence (WoE) assessment. Overall, our findings show that HPPT results are often not sufficient for deriving unambiguous classifications on their own. However, where they are, the resulting classifications are reliable and reproducible and can be integrated well with those from other skin sensitization data, such as the LLNA. [ABSTRACT FROM AUTHOR]

Purpose: The detection rate of Salmonella enterica serovar 1,4,[5], 12: i: - (S. 1,4,[5], 12: i: -) has increased as the most common serotype globally. A S. 1,4,[5], 12: i: - strain named ST3606 (sequence type 34), isolated from a fecal specimen of a child with acute diarrhea hospitalized in a tertiary hospital in China, was firstly reported to be resistant to carbapenem and ceftazidime-avibactam. The aim of this study was to characterize the whole-genome sequence of S. 1,4,[5], 12: i: - isolate, ST3606, and explore its antibiotic resistance genes and their genetic environments. Methods: The genomic DNA of S. 1,4,[5], 12: i: - ST3606 was extracted and performed with single-molecule real-time sequencing. Resistance genes, plasmid replicon type, mobile elements, and multilocus sequence types (STs) of ST3606 were identified by ResFinder 3.2, PlasmidFinder, OriTfinder database, ISfinder database, and MLST 2.0, respectively. The conjugation experiment was utilized to evaluate the conjugation frequency of pST3606-2. Protein expression and enzyme kinetics experiments of CTX-M were performed to analyze hydrolytic activity of a novel CTX-M-261 enzyme toward several antibiotics. Results: Single-molecule real-time sequencing revealed the coexistence of a 109-kb IncI1-Iα plasmid pST3606-1 and a 70.5-kb IncFII plasmid pST3606-2. The isolate carried resistance genes, including blaNDM-5, sul1, qacE, aadA2, and dfrA12 in pST3606-1, blaTEM-1B, aac(3)-lld, and blaCTX-M-261, a novel blaCTX-M-1 family member, in pST3606-2, and aac(6')-Iaa in chromosome. The blaCTX-M-261 was derived from blaCTX-M-55 by a single-nucleotide mutation 751G>A leading to amino acid substitution of Val for Met at position 251 (Val251Met), which conferred CTX-M increasing resistance to ceftazidime verified by antibiotics susceptibility testing of transconjugants carrying pST3606-2 and steady-state kinetic parameters of CTX-M-261. pST3606-1 is an IncI1-α incompatibility type that shares homology with plasmids of pC-F-164_A-OXA140, pE-T654-NDM-5, p_dm760b_NDM-5, and p_dmcr749c_NDM-5. The conjugation experiment demonstrated that pST3606-2 was successfully transferred to the Escherichia coli recipient C600 with four modules of OriTfinder. Conclusion: Plasmid-mediated horizontal transfer plays an important role in blaNDM-5 and blaCTX-M-261 dissemination, which increases the threat to public health due to the resistance to most β-lactam antibiotics. This is the first report of blaCTX-M-261 and blaNDM-5 in S. 1,4,[5], 12: i: -. The work provides insights into the enzymatic function and demonstrates the ongoing evolution of CTX-M enzymes and confirms urgency to control resistance of S. 1,4,[5], 12: i: -. [ABSTRACT FROM AUTHOR]

As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors' collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies' investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies. [ABSTRACT FROM AUTHOR]

This study conducts a citation-based comprehensive systematic literature review (SLR) of the syndicated loan market by identifying and recognizing the sources of knowledge-producing leading articles, journals and authors in this area. In total, we present a citation analysis of 374 articles from the Scopus database using a comprehensive list of keyword searches. We find that the importance of research using syndicated loan data has increased after the global financial crisis (GFC). It is perhaps due to the reason that the GFC was mainly a credit crisis that sparked the interest of research in this area. We identify the Journal of Financial Economics as the leading journal in terms of citation and the Journal of Banking and Finance in terms of publication count. Victoria Ivashina comes out as the leading author in terms of the citation count and Anthony Saunders in terms of publication count. We also perform a content analysis of the top 100 most cited papers and identify data characteristics, major themes, estimation techniques and empirical approaches. Our SLR provides a macro picture of research on syndicated loan studies. Finally, we provide an overview of the direction for future research. [ABSTRACT FROM AUTHOR]

Machine learning methods have been widely used control and information systems. Robust learning is an important issue in machine learning field. In this work, we propose a novel robust regression framework. Specifically, we propose a robust similarity measure induced by correntropy, and its important properties are demonstrated theoretically such as symmetry, boundedness, nonnegativity, consistency, smoothness and approximation behaviors. Moreover, the proposed robust metric extends the traditional metrics such as the l 2 -norm and l 0 -norm as the kernel parameter approaches different values. Then with the proposed similarity metric and ϵ -insensitive pinball loss, a new robust twin support vector regression framework (RTSVR) is proposed to handle robust regression problems. The linear RTSVR model is first built, and a kernelled RTSVR version is developed to deal with nonlinear regressions. To handle the nonconvexity of the proposed RTSVR, we use DC (different of convex function) programming algorithm to iteratively solve the problem, and the resulting algorithm converges linearly. To test the proposed RTSVR, numerical experiments are implemented on two databases including a public benchmark database and a practical application database. Experiments on benchmark data with different types of noise illustrate that the proposed methods achieve better performance than the traditional methods in most cases. Experiments on the application database, the proposed RTSVR is combined with near-infrared (NIR) spectral technique to analyze the hardness of licorice seeds in low frequency,intermediate frequency and high frequency spectral regions respectively. Experiments on different spectral regions show that the performance of the RTSVR is better than that of the traditional methods in all spectral regions. [ABSTRACT FROM AUTHOR]

Real world questions are generally complex and need the user to extract information from multiple tables in a database using complex SQL queries like nested queries. Though the overall accuracy in translation of Natural Language queries to SQL queries lies close to 75%, the accuracy of complex queries is still quite less, around 60% in the current state-of-the-art models. In this vein, this study proposes to improve the current IRNet framework for translating natural language queries to nested SQL queries, one type of complex queries. Data oversampling is first used to boost the representation of nested queries in order to achieve this goal. Second, a novel loss function that computes the overall loss while accounting for the complexity of SQL, as measured by the quantity of SELECT columns and keywords in the SQL query. The proposed method exhibited a 5% improvement in prediction of hard and extra hard queries when tested on Spider's development dataset. [ABSTRACT FROM AUTHOR]

A controller is modelled and designed to optimize the power transfer in microgrid-connected hybrid renewable energy systems using an integrated DC/DC converter. To maximize the converter's output power and minimize the switching losses of the converter, a model is developed by including a simplified high conversion ratio converter, a maximal power point tracker, and an optimal controller with an effective control strategy. The proposed control system is a combination of the Artificial Transgender Longicorn Algorithm (ATLA) and the Gradient Boosting Decision Tree (GBDT) algorithm, named the ATLA-GBDT method. In the suggested technique, the ATLA is used as an assessment method to build up accurate control signals for the system and to improve the control signals database for offline use while considering the power exchange between the source and load. In addition, for training a GBDT system online, the data set received from the sensor is used to develop a control system for faster response. In addition, the goal function is defined by the system data, which is subject to equality and inequality constraints. Various constraints considered in the problem formulation are the output of renewable energy sources, power requirements, and the state of charge of storage components. The proposed control system is simulated using the MATLAB/Simulink platform, and the implementation is compared with the existing techniques. Various performance metrics like accuracy, specificity, recall and precision, RMSE, MAPE, and MBE of the proposed method and existing methods in the literature are presented. [ABSTRACT FROM AUTHOR]

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making. [ABSTRACT FROM AUTHOR]