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Utilizing a decade-long unTimely data set, supplemented by multiband data from archives, we search for young stellar objects (YSOs) with variations larger than one magnitude in W 1 band within a region of 110 square degrees in the Galactic plane, covered by VISTA Variables in the Via Lactea. A total of 641 candidate YSOs have been identified. We classified them into bursts, dips, faders, seculars, and irregulars. Within the burst category, 18 sources were identified as FUor candidates and 1 as an EXor candidate. Irregulars are the most prevalent in the sample. In both bursts and faders, the redder sources tend to show a pattern of bluer when brighter, whereas the bluer sources display the opposite trend, possibly related to the accretion structure of YSOs at different stages. Finally, we obtained the recurrence time-scale for FUor eruptions at various stages of YSO evolution. Our findings indicate that younger YSOs generally experience more frequent eruptions compared to older ones. [ABSTRACT FROM AUTHOR]

Objective: Cognitive Behavior Therapy (CBT) is an effective treatment for anxiety and depression disorders. Nonetheless, nearly 50% of all patients do not respond. Besides other factors, nonresponse may be linked to traumatic life events. This study aims to assess the relationship between trauma history, applied therapy interventions, and therapy outcomes. Methods: We analyzed data from 340 CBT outpatients diagnosed with a depression or anxiety disorder and possibly a trauma history treated under naturalistic conditions. Based on their therapy files, we collected information on trauma history, diagnoses, applied interventions, and severity of depression and anxiety symptoms at the start and end of therapy. The relationship between trauma, diagnoses, and intervention use and the development of depression and anxiety symptoms was analyzed using Linear Mixed Models. Results: Patients with a trauma history reported higher pre‐ and posttreatment symptom severity than those without trauma. No differences in applied interventions or decrease in symptom severity were found between patients with and without a trauma history. Specialized interventions were seldom applied. Conclusion: Although no differences between patients with and without a trauma history were found in therapy response, patients with a trauma history maintained higher levels of symptom severity. These results indicate a need for more personalized interventions and evidence‐based guidelines to personalize CBT for patients with a trauma history and high symptom severity. [ABSTRACT FROM AUTHOR]

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis. [ABSTRACT FROM AUTHOR]

Increasingly artificial intelligence (AI) is employed by artists for creative purposes. At the same time, AI causes significant concerns among creative professionals in terms of copyright violations and possible job loss. To understand how it may be possible to (co)create with AI this article will enter into conversation with Indian artist Harshit Agrawal who is both a designer with Adobe and an artist who works with AI for creative purposes. Introducing the concept of the sociocultural archive comprising AI imaginations as they have featured in popular culture, this article suggests that when we seek to understand how we now live, work and create with AI in the 'present', we must also interrogate how this was once envisioned in the 'past'. This will facilitate a more productive approach to AI for the 'future'. [ABSTRACT FROM AUTHOR]

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data‐driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. [ABSTRACT FROM AUTHOR]

OrganoidPortal is a user-friendly web server and single-cell transcriptome database that provides a comprehensive resource for the analysis and interpretation of organoids, stem cell-derived 3D culture systems. It includes single-cell transcriptome profiles from 28 tissues, 1749 samples, and four species, obtained from 280 published studies. The database offers functionalities such as cell clustering, cell type annotation, developmental trajectory prediction, pathway enrichment, gene regulation, and copy number variation analyses. It also provides reference atlases, comparative molecular profiling, and tools for de novo analysis. OrganoidPortal serves as a valuable resource for researchers interested in exploring the potential of organoids for translational applications. [Extracted from the article]

The ever-changing and temporary nature of Random Access Memory (RAM) presents distinct challenges and possibilities for digital forensic investigations. This research paper offers a comprehensive analysis of RAM data retrieval techniques within the realm of forensic studies. Starting with a comprehensive explanation of the importance of RAM in forensic investigations, this paper explores the different techniques used to extract volatile data from RAM. These methods involve conducting live system analysis, memory imaging, and memory dumping techniques, each with their own set of advantages and limitations. In addition, the paper delves into the complexities of data acquisition, preservation, and analysis, taking into account factors such as system state volatility, encryption, and anti-forensic measures. In addition, this paper explores the practicality and effectiveness of RAM data retrieval methods on various operating systems and hardware setups. It assesses the strengths and weaknesses of commonly used forensic tools and frameworks for RAM analysis, showcasing their performance in practical situations. In addition, the research explores the latest developments and trends in RAM forensics. This includes the use of machine learning algorithms to automate memory analysis and the creation of specialized forensic techniques for cloud environments. Finally, this paper brings together the findings to provide valuable insights into the best practices for RAM data retrieval in forensic investigations. It highlights the significance of meticulous documentation, maintaining a clear chain of custody, and adhering to legal standards. Through the synthesis of established knowledge and the exploration of emerging trends, this research makes a significant contribution to the field of RAM forensics. It offers valuable guidance for digital forensic practitioners, law enforcement agencies, and researchers, aiding in their understanding and advancement of the subject. [ABSTRACT FROM AUTHOR]

The article "African American Migration to Liberia, 1820-1906" by Leigh Gardner provides a comprehensive dataset of 16,332 individuals who migrated to Liberia under the American Colonization Society from 1820 to 1904. The dataset includes information on names, ages, places of origin, dates of departure, literacy levels, and occupations. The migration to Liberia was historically significant for African Americans seeking to leave the American South, with fluctuations in annual numbers of migrants and a decline in migration after the 1870s. The dataset allows for comparisons with other migrant flows and sheds light on the demographics and characteristics of those who chose to emigrate to Liberia. [Extracted from the article]

Primary malignancies of the central nervous system account for 2% of all cancers in adults and almost 15% in children under 15 years of age. The prognosis of brain anaplastic cancers and glioblastomas remains extremely poor, with devastating survival expectative, and new molecular markers and therapeutic targets are essential. Epigenetic changes constitute an extensive field for the development of new diagnostic and therapeutic strategies. Histone acetyl transferase-1 (HAT1) has merged as a potential prognostic marker and therapy target for different malignancies. Data repository analysis showed HAT1 mRNA overexpression in gliomas and has been described its alternative splicing in glioblastomas. Using immunohistochemical and aptahistochemical methods, we analyzed the expression of HAT1 in meningiomas, oligodendrogliomas, and astroglial cancers. We observed that HAT1 overexpression is associated with the most aggressive tumor types and the worse prognosis, as well as with a higher probability of early relapse in meningiomas. Its cytosolic localization correlates with tumor progression and prognosis. Aptamers, synthetic oligonucleotides capable to bind and inhibit a wide variety of targets, are considered as promising diagnostic and therapeutic tools. Aptahistochemistry using the aptamer apHAT610 offered superior results in comparison with the antibody used, as a good example of the potential of aptamers as diagnostic tools for histopathology. [ABSTRACT FROM AUTHOR]

Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals. Previous studies have successfully extracted speech characteristics from EEG data and, conversely, predicted EEG activity from speech features. Machine learning techniques are generally employed to construct encoding and decoding models, which necessitate a substantial quantity of data. We present SparrKULee, a Speech-evoked Auditory Repository of EEG data, measured at KU Leuven, comprising 64-channel EEG recordings from 85 young individuals with normal hearing, each of whom listened to 90–150 min of natural speech. This dataset is more extensive than any currently available dataset in terms of both the number of participants and the quantity of data per participant. It is suitable for training larger machine learning models. We evaluate the dataset using linear and state-of-the-art non-linear models in a speech encoding/decoding and match/mismatch paradigm, providing benchmark scores for future research. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study on the main parameters and performance evaluation of microjet engines, at take-off regime and at various engine working regimes, based on thermodynamic analysis of a particular engine data library, from different engine manufacturers such as JetCat and AMT Netherlands. The studied engines have the same spool design but different thrust classes ranging from 97 to 1569 N. The particular data library includes engine specifications from catalogs or data sheets as well as our own experimental data from the JetCat P80 microjet engine, obtained using the ET 796 Jet Turbine Module, a complete testing facility for gas turbine education purposes. Various ratios and differences between certain engine main parameters and performances are studied in order to calculate values through which the analyses can be performed. Even if the engines have different thrust classes, the study examines if there are close values of the ratios and differences of parameters, that can be defined as reference parameters through which the engine performance can be compared and evaluated. [ABSTRACT FROM AUTHOR]

Investors, practitioners, and stock researchers highly need data related to financial performance to predict a company's financial health condition, which is used as a basis to consider investing in it. The Indonesia Stock Exchange (IDX) website provides reports on the company's financial performance. Unfortunately, the company’s financial data found on the IDX website are in PDF format, and researchers must download them one by one, which takes a long time. This study presents a website-based application, named Indonesia Company Performance (INDCOMP), built using the R programming language and involving various R packages and frameworks to assist investors, practitioners, and stock researchers in studying the financial performance of companies. This application can help users quickly access the financial performance data of various companies, present financial performance data in data tables, and perform data visualizations as well as statistical analyses [ABSTRACT FROM AUTHOR]

In identifying the status of cloud computing in a state of sustainability in terms of contribution and impact of cloud computing, the authors have gathered articles and journals and applied different methods to achieve the purpose of the study. The author hopes to identify the dominant challenges or issues that data centers have faced today in achieving cloud sustainability, specifically in the context of promoting greener practices within companies. Furthermore, the study will try to evaluate how cloud computing technologies help businesses in economics, environment, and social systems, also focusing on their environmental impact reduction strategies. Lastly, assessing the Gartner Hype Cycle for 2023, particularly to Cloud Sustainability, exploring when cloud sustainability is expected to reach its plateau of productivity, which is projected to occur within 2 to 5 years from the current timeframe. The study used a qualitative analysis framework that focuses on collecting data through experiences, opinions, and ideas related to the topic. Further, the author used thematic analysis to deeply understands the collected data systematically. Lastly, the author used mind mapping to provide a visual presentation and easily categorized the collected data. The paper discussed the contribution and advantages of cloud computing in economics, environment, and social systems. Furthermore, it also identified challenges and issues of data centers that offer cloud services. Thus, Data Centers are now starting to address these issues and this paper discusses all possible solutions that data centers are now starting to implement. Moreover, to support this argument this paper cited top 5 data center companies promoting sustainability. The authors conclude cloud computing contributes to all businesses enhancing their efficiency and competitiveness in the market. Further, cloud computing helps other technologies to enhance their capacity. However, since there is no 100% perfect technology there are implication in cloud computing. Thus, businesses and data centers provide solutions and keep on improving and capacitate these solutions to achieve sustainability. [ABSTRACT FROM AUTHOR]

Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles. Scientific contribution This pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries. [ABSTRACT FROM AUTHOR]

Teachers' judgment accuracy is a core competency in their daily business. Due to its importance, several meta-analyses have estimated how accurately teachers judge students' academic achievements by measuring teachers' judgment accuracy (i.e., the correlation between teachers' judgments of students' academic abilities and students' scores on achievement tests). In our study, we considered previous meta-analyses and updated these databases and the analytic combination of data using a psychometric meta-analysis to explain variations in results across studies. Our results demonstrate the importance of considering aggregation and publication bias as well as correcting for the most important artifacts (e.g., sampling and measurement error), but also that most studies fail to report the data needed for conducting a meta-analysis according to current best practices. We find that previous reviews have underestimated teachers' judgment accuracy and overestimated the variance in estimates of teachers' judgment accuracy across studies because at least 10% of this variance may be associated with common artifacts. We conclude that ignoring artifacts, as in classical meta-analysis, may lead one to erroneously conclude that moderator variables, instead of artifacts, explain any variation. We describe how online data repositories could improve the scientific process and the potential for using psychometric meta-analysis to synthesize results and assess replicability. [ABSTRACT FROM AUTHOR]

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements. Author summary: Traditional GWAS approaches require the transfer of sensitive genetic data to a central location for analysis, raising privacy concerns and data security issues. We propose a federated learning technique named FedGMMAT to conduct large-scale genetic analyses without the need to share sensitive information. By allowing data repositories to perform local computations and only share the aggregated results in encryption, FedGMMAT ensures that individual genetic data remains secure and private. This novel algorithm enhances the capability of researchers to collaborate on GWAS across multiple sites, thereby increasing the statistical power and robustness of genetic discoveries while maintaining the confidentiality of participant data. This approach not only addresses privacy concerns but also paves the way for larger scale genetic studies by enabling the participation of diverse data sets from various institutions. [ABSTRACT FROM AUTHOR]

Aims: European registries and retrospective cohort studies have highlighted the failure to achieve low-density lipoprotein-cholesterol (LDL-C) targets in many very high-risk patients. Hospitalized patients are often frail, and frailty is associated with all-cause and cardiovascular mortality. The aim of this study is to evaluate LDL-C levels in a real-world inpatient setting, identifying cardiovascular risk categories and highlighting treatment gaps in the implementation of LDL-C management. Methods: This retrospective, observational study included all adult patients admitted to an Italian hospital between 2021 and 2022 with available LDL-C values during hospitalization. Disease-related real-world data were collected from Hospital Information System using automated data extraction strategies and through the implementation of a patient-centered data repository (the Dyslipidemia Data Mart). We performed assessment of cardiovascular risk profiles, LDL-C target achievement according to the 2019 ESC/EAS guidelines, and use of lipid-lowering therapies (LLT). Results: 13,834 patients were included: 17.15%, 13.72%, 16.82% and 49.76% were low (L), moderate (M), high (H) and very high-risk (VH) patients, respectively. The percentage of on-target patients was progressively lower towards the worst categories (78.79% in L, 58.38% in M, 33.3% in H and 21.37% in VH). Among LLT treated patients, 28.48% were on-target in VH category, 47.60% in H, 69.12% in M and 68.47% in L. We also analyzed the impact of monotherapies and combination therapies on target achievement. Conclusions: We found relevant gaps in LDL-C management in the population of inpatients, especially in the VH category. Future efforts should be aimed at reducing cardiovascular risk in these subjects. [ABSTRACT FROM AUTHOR]

While criminal legal involvement is a structural determinant of health, both administrative and national longitudinal cohort data are collected and made available in a way that prevents a full understanding of this relationship. Administrative data are both collected and overseen by the same entity and are incomplete, delayed, and/or uninterpretable. Cohort data often only ask these questions to the most vulnerable, and do not include all types of criminal legal involvement, when this involvement occurs in someone's life, or family and community involvement. To achieve a more optimized data landscape and to facilitate population-level research on criminal legal involvement and health, (1) individual administrative level data must be made available and able to be linked across carceral systems, (2) a national data archive must be made to maintain and make criminal legal data available to researchers, and (3) a nationally representative, longitudinal study focused on those with criminal legal involvement is necessary. By beginning to critically think about how future data could be collated and collected, we can begin to provide more robust evidence around how the criminal legal system impacts the health of our society and, in turn, create policy reform. [ABSTRACT FROM AUTHOR]

Ancient DNA is producing a rich record of past genetic diversity in humans and other species. However, unless the primary data is appropriately archived, its long-term value will not be fully realised. I surveyed publicly archived data from 42 recent ancient genomics studies. Half of the studies archived incomplete datasets, preventing accurate replication and representing a loss of data of potential future use. No studies met all criteria that could be considered best practice. Based on these results, I make six recommendations for data producers: (1) archive all sequencing reads, not just those that aligned to a reference genome, (2) archive read alignments too, but as secondary analysis files, (3) provide correct experiment metadata on samples, libraries and sequencing runs, (4) provide informative sample metadata, (5) archive data from low-coverage and negative experiments, and (6) document archiving choices in papers, and peer review these. Given the reliance on destructive sampling of finite material, ancient genomics studies have a particularly strong responsibility to ensure the longevity and reusability of generated data. [ABSTRACT FROM AUTHOR]

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier's Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of 18 k m 2 per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of 0 - 7 k m at an interval of 1 k m . Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of 6. 4 ∘ C , 6.9 m W / m 2 and 0.04 W / m - K , respectively. This thorough modeling of the Earth's thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592. [ABSTRACT FROM AUTHOR]

Informative and scalable cartography plays a pivotal role in curbing urban pollution, waste management, and mitigating environmental damage in the development of informal settlements. The contemporary capabilities of cloud computing facilitate streamlined access to comprehensive data repositories, computational infrastructure, and proficient tools that have rapidly advanced the execution of sprawl mapping procedures. This study tests the performance of four machine-learning algorithms, namely: Gradient Boost, K Nearest Neighbor [KNN], Random Forest [RF], and Support Vector Machine [SVM] with data extracted from cloud computing repositories for delineating informal settlements in Gcuwa, Eastern Cape, South Africa, using low-cost datasets. A systematic approach comprising iterative phases, encompassing data acquisition, the development of a training dataset, modeling, and evaluation was employed. The delineation process involved the extraction of both spectral and textural features from Sentinel-2 imagery. The Random Forest algorithm emerged as the top performer, exhibiting the highest levels of accuracy and F1 score, followed by the gradient boosting, support vector machine, and then the K-nearest neighbor algorithms. Consequently, this innovative use of machine learning algorithms with low-cost datasets and the scalable, resilient approach for detecting informal settlements offers a promising avenue for enhancing urban planning and addressing sustainable development challenges. [ABSTRACT FROM AUTHOR]

Meteorological parameters play a major role in air pollutant concentrations as they create conditions that either hinder or facilitate the reaction and dispersion of pollutants in our environments. This is particularly evident in Europe, where frequent alternation of meteorological parameters has the potential to significantly impact pollutant concentrations. This study applied the R openair package to comparatively analyse the relationship between key meteorological parameters and NO2, O3, PM2.5, and PM10 concentrations measured at selected air quality monitoring stations in Krakow, Milan, and Paris in the year 2021. The study made use of meteorological data acquired from National Aeronautics and Space Administration (NASA) Power data repository, and air pollutants data measured at air quality monitoring stations in each of the three cities. The air pollutants data were retrieved from European Environmental Agency's Airbase. Concentration and correlation analyses were conducted using the relevant functions of the R openair package. Findings in the study revealed a positive relationship between temperature and O3, wind speed and O3; and a negative relationship between temperature and NO2/PM2.5/PM10. The study further revealed a negative relationship between wind speed and NO2/PM2.5/PM10, as well as a negative relationship between precipitation and NO2/PM2.5/PM10. NO2, PM2.5, and PM10 concentrations were higher in winter periods, weekdays, nights, and evenings, but lower in summer periods, weekends, and midday. Whereas O3 concentration was higher in summer periods, weekends, midday, and lower in winter periods, weekdays, nights, and evenings. NO2, PM10, and PM2.5 concentrations were higher during the periods without precipitation than periods with precipitation. In addition, temperature inversions were found to be linked with higher concentrations of NO2, PM2.5, and PM10, but lower concentrations of O3 in Krakow, Paris and Milan. Accordingly, the study recommends effective monitoring, increased awareness, the use of pollutant removing devices, and further research to enhance adaptation and advance knowledge. [ABSTRACT FROM AUTHOR]

Scientific data are essential to advancing scientific knowledge and are increasingly valued as scholarly output. Understanding what drives dataset downloads is crucial for their effective dissemination and reuse. Our study, analysing 55,473 datasets from 69 data repositories, identifies key factors driving dataset downloads, focusing on interpretability, reliability, and accessibility. We find that while lengthy descriptive texts can deter users due to complexity and time requirements, readability boosts a dataset's appeal. Reliability, evidenced by factors like institutional reputation and citation counts of related papers, also significantly increases a dataset's attractiveness and usage. Additionally, our research shows that open access to datasets increases their downloads and amplifies the importance of interpretability and reliability. This indicates that easy access enhances the overall attractiveness and usage of datasets in the scholarly community. By emphasizing interpretability, reliability, and accessibility, this study offers a comprehensive framework for future research and guides data management practices toward ensuring clarity, credibility, and open access to maximize the impact of scientific datasets. [ABSTRACT FROM AUTHOR]

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

To explore complex biological questions, it is often necessary to access various data types from public data repositories. As the volume and complexity of biological sequence data grow, public repositories face significant challenges in ensuring that the data is easily discoverable and usable by the biological research community. To address these challenges, the National Center for Biotechnology Information (NCBI) has created NCBI Datasets. This resource provides straightforward, comprehensive, and scalable access to biological sequences, annotations, and metadata for a wide range of taxa. Following the FAIR (Findable, Accessible, Interoperable, and Reusable) data management principles, NCBI Datasets offers user-friendly web interfaces, command-line tools, and documented APIs, empowering researchers to access NCBI data seamlessly. The data is delivered as packages of sequences and metadata, thus facilitating improved data retrieval, sharing, and usability in research. Moreover, this data delivery method fosters effective data attribution and promotes its further reuse. This paper outlines the current scope of data accessible through NCBI Datasets and explains various options for exploring and downloading the data. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusions To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter‐ and hospital‐submitted inpatient claims.Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim.We combined hospital‐ and MAO‐submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO‐submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures.After removing void and duplicate claims, identifying overlapped claims between the hospital‐ and MAO‐submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital‐submitted claims were submitted within 3 months after a one‐year performance period, versus 85.2% of MAO‐submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code.Inpatient MA data are feasible for use in CMS claims‐based hospital outcome measures. We recommend prioritizing hospital‐submitted over MAO‐submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors. [ABSTRACT FROM AUTHOR]

Sediments provide valuable information for geologists and geophysicists whenever they strive to understand, and reproduce, the geological evolution, lithology, rock properties, seismic response, and geohazards of a region. The analysis of sedimentary sequences is thus useful to the interpretation of depositional environments, sea-level change, climate change, and to a recognition of the sediments' source areas, amongst other aspects. By integrating sedimentary data in geophysical modelling, such interpretations are improved in terms of their accuracy and reliability. To help our further understanding of Iberia's geological evolution, geological resources and geohazards, this work presents to the scientific community the SedDARE-IB data repository. This repository includes available data of the depth to the Base Cenozoic and Top Paleozoic stratigraphic markers for the Iberian Peninsula and surrounding Western Atlantic and Mediterranean Neogene basins, or to the acoustic basement as interpreted for the Valencia Trough and Alboran Mediterranean basins. As an example of the broad applicability of the data included in SedDARE-IB, we investigate how sediment thickness affects the depth to the 150 oC isotherm at specific basins, as commonly used in geothermal exploration. The calculated trend suggests that, given constant measured surface heat flow and thermal conductivity, the 150 oC isotherm becomes shallower as a function of sediment thickness, until a critical threshold value is reached for the latter.SedDARE-IB database has been built thanks to a Portuguese-Spanish collaboration promoting open data exchange among institutions and research groups. SedDARE-IB is freely available at https://doi.org/10.20350/digitalCSIC/16277 (Torne et al., 2024) bringing opportunities to the scientific, industrial, and educational communities for diverse applications. [ABSTRACT FROM AUTHOR]

There is an urgent need to increase the global data storage capacity, as current approaches lag behind the exponential growth of data generation driven by the Internet, social media, and cloud technologies. In addition to increasing storage density, new solutions should provide long‐term data archiving that goes far beyond traditional magnetic memory, optical disks, and solid‐state drives. Here, a concept of energy‐efficient, ultralong, high‐density data archiving is proposed, based on optically active atomic‐size defects in a radiation resistance material, silicon carbide (SiC). The information is written in these defects by focused ion beams and read using photoluminescence or cathodoluminescence. The temperature‐dependent deactivation of these defects suggests a retention time minimum over a few generations under ambient conditions. With near‐infrared laser excitation, grayscale encoding and multi‐layer data storage, the areal density corresponds to that of Blu‐ray discs. Furthermore, it is demonstrated that the areal density limitation of conventional optical data storage media due to the light diffraction can be overcome by focused electron‐beam excitation. [ABSTRACT FROM AUTHOR]

This document focuses on the area of machine learning from data, applied to internal processes of a library. This is practical work associated with the development of an application in Python that uses libraries developed for automated learning work. An unsupervised data analysis methodology was applied, called the K-means method (K-medias in Spanish), which allows the data to be segmented or classified into groups to extract common characteristics. Data associated with the library collections were used. The developed code is shared, and visualisations of the data are shown. [ABSTRACT FROM AUTHOR]

A long-range forecast system based on the improved version of the SLAV072L96 global atmosphere model has been verified at the Hydrometcenter of Russia. The model has the horizontal resolution of 0.9° 0.72° in longitude and latitude and 96 vertical levels and includes modern parametrizations for the subgrid-scale processes in the atmosphere and active soil layer. Main features and particularities of this model version are presented along with a brief description of ensemble long-range meteorological forecast technology using this model. Some verification scores for long-range forecasts based on the archive data from ERA5 reanalysis for 1991–2015 and on the data for 2023 are presented. [ABSTRACT FROM AUTHOR]

This ongoing study aims to map the technological and systemic challenges found in the process of implementing connected open data practices in libraries to make their catalogs available online. The study is developed as applied and exploratory research, based on the use of bibliographic and documentary research. The technique adopted is Bardin's Content Analysis, comprising the phases of elaboration of records for the initial evaluation of the texts, creation of categories for analysis, grouping in the respective created categories, followed by data analysis and interpretation. It is expected to achieve results that identify the challenges and potential of bibliographic records for use in open data format and the benefits provided by connecting these data. [ABSTRACT FROM AUTHOR]

We present the spectral and timing study of V404 Cygni from all its available Chandra observations and which recently come up in public domain of Chandra data archive. The data reduction and analysis were done using CIAO 4.14 and HEASOFT 6.30.1. The spectral analysis was done using spectral fitting package XSPEC version 12.12.1, available in the Heasoft package. The spectra of the source is fitted in the energy range 0.3 -8.0 keV using two empirical spectral models the absorbed power law and an absorbed disk-blackbody. The X-ray binary source V404 Cygni is found to be in the quiescent state having the X-ray luminosity in the range with few times 1032 erg s-1. The source is found to be in the hard state and is well explained by power-law model with a powerlaw photon index ~ 2 with ny in the range~ (0.7-1.2) x 1022 cm-2. From timing analysis, Src-1 (V404 Cygni), in all the time bins- 0.5, 1 and 2 ks, the probability for the count rate to be constant is 0.17 x10-33 in all the observations in the year 2021 and 2023 (ObsID 23421, ObsID 23422, ObsID 23423 & ObsID 28927). However, in the year 2017 observation it is found to be less variable. This clearly shows the presence of short-term variability in kilo-seconds time-scales with the currently available Chandra data. So, it is indicative that the binary source V404 Cygni is more likely to be variable source both in long-term (years) as well as short-term (kiloseconds) scales. [ABSTRACT FROM AUTHOR]

The results of the validation of the code package MCPV for the calculation of radiation transport within VVER-1000 reactor type near-vessel space using a continuous-energy data library are presented. The validation is carried out based on the experimental data obtained in mock-up and full-scale experiments. It is shown that the code package MCPV can be used to simulate neutrons and gamma quanta transport through the complicated heterogeneous environment of a nuclear reactor and to obtain the valid values of the functionals of the neutron and gamma fluxes within VVER-1000 near-vessel space. [ABSTRACT FROM AUTHOR]

Since the first established library in the 7th century B.C., libraries have played a main role in civilization development due to their ability to help humans reach the required information easily. However, in the last decades, after the huge propagation of information from various sources, including the addition of books, other data types such as audio, video, and images have emerged. Therefore, finding an integrated system to manage this huge amount of library data became necessary. As a result, the integrated library system (ILS) improved its efficiency and ability to manage various and enormous amounts of data in addition to following the lending processes and inventory management. There are many commercial and open-source integrated systems used in libraries; selecting the best open-source integrated library system to achieve the library requirements is still challenging due to the variety of available libraries. Although these libraries are open source and freely available, the installation process requires a long time and expert effort, making selecting the best library system a sensitive and important mission. This survey demonstrates the most famous opensource integrated library systems and the essential technologies used to build them, along with the system specifications, to help librarians and educational and academic institutions select the best one for their requirements. Through reviewing the previous academic research, it was noticed that many of them had the task of selecting the right ILS, but most of them did not handle the programming principles based on it, which is the milestone in its building, so we recommended using an automated technique to evaluate ILS and select the best one. [ABSTRACT FROM AUTHOR]

A recent study on the classical capacitated vehicle routing problem (CVRP) introduced an adaptive version of the widely used iterated local search paradigm, hybridized with a path-relinking (PR) strategy. The solution method, called adaptive iterated local search (AILS)-PR, outperformed existing meta-heuristics for the CVRP on benchmark instances. However, tests on large-scale instances suggest that PR is too slow, making AILS-PR less advantageous in this case. To overcome this challenge, this paper presents an AILS combined with mechanisms to handle large CVRP instances, called AILS-II. The computational cost of this implementation is reduced, whereas the algorithm also searches the solution space more efficiently. AILS-II is very competitive on smaller instances, outperforming the other methods from the literature with respect to the average gap to the best-known solutions. Moreover, AILS-II consistently outperforms the state of the art on larger instances with up to 30,000 vertices. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo [Grants 2013/07375-0, 2019/22067-6, and 2022/05803-3] and the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 309385/2021-0 and 403735/2021-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0106) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0106). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The optimization community has made significant progress in solving vehicle routing problems (VRPs) to optimality using sophisticated branch-cut-and-price (BCP) algorithms. VRPSolver is a BCP algorithm with excellent performance in many VRP variants. However, its complex underlying mathematical model makes it hardly accessible to routing practitioners. To address this, VRPSolverEasy provides a Python interface to VRPSolver that does not require any knowledge of mixed integer programming modeling. Instead, routing problems are defined in terms of familiar elements, such as depots, customers, links, and vehicle types. VRPSolverEasy can handle several popular VRP variants and arbitrary combinations of them. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by Faperj [Grant E-26/202.887/2017] and Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant 305684/2022-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0103) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0103). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We introduce PyVRP, a Python package that implements hybrid genetic search in a state-of-the-art vehicle routing problem (VRP) solver. The package is designed for the VRP with time windows (VRPTW) but can be easily extended to support other VRP variants. PyVRP combines the flexibility of Python with the performance of C++ by implementing (only) performance-critical parts of the algorithm in C++ while being fully customizable at the Python level. PyVRP is a polished implementation of the algorithm that ranked first in the 2021 DIMACS VRPTW challenge and, after improvements, ranked first on the static variant of the EURO meets NeurIPS 2022 vehicle routing competition. The code follows good software engineering practices and is well documented and unit tested. PyVRP is freely available under the liberal MIT license. Through numerical experiments, we show that PyVRP achieves state-of-the-art results on the VRPTW and capacitated VRP. We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: Funding was provided by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0055) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0055). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. There is a video associated with this paper. Click here to view the Video Overview. To save the file, right click and choose "Save Link As" from the menu. [ABSTRACT FROM AUTHOR]

The classic pump scheduling or optimal water flow (OWF) problem for water distribution networks (WDNs) minimizes the cost of power consumption for a given WDN over a fixed time horizon. In its exact form, the OWF is a computationally challenging mixed-integer nonlinear program (MINLP). It is complicated by nonlinear equality constraints that model network physics, discrete variables that model operational controls, and intertemporal constraints that model changes to storage devices. To address the computational challenges of the OWF, this paper develops tight polyhedral relaxations of the original MINLP, derives novel valid inequalities (or cuts) using duality theory, and implements novel optimization-based bound tightening and cut generation procedures. The efficacy of each new method is rigorously evaluated by measuring empirical improvements in OWF primal and dual bounds over 45 literature instances. The evaluation suggests that our relaxation improvements, model strengthening techniques, and a thoughtfully selected polyhedral relaxation partitioning scheme can substantially improve OWF primal and dual bounds, especially when compared with similar relaxation-based techniques that do not leverage these new methods. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms & Applications. Funding: This work was supported by the U.S. Department of Energy (DOE) Advanced Grid Modeling project, Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability. Incorporation of the PolyhedralRelaxations Julia package was supported by Los Alamos National Laboratory's Directed Research and Development program under the project Fast, Linear Programming-Based Algorithms with Solution Quality Guarantees for Nonlinear Optimal Control Problems [Grant 20220006ER]. All work at Los Alamos National Laboratory was conducted under the auspices of the National Nuclear Security Administration of the U.S. DOE, Contract No. 89233218CNA000001. This work was also authored in part by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. DOE, Contract No. DE-AC36-08GO28308. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0233) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0233). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Discrete-event simulation models generate random variates from input distributions and compute outputs according to the simulation logic. The input distributions are typically fitted to finite real-world data and thus are subject to estimation errors that can propagate to the simulation outputs: an issue commonly known as input uncertainty (IU). This paper investigates quantifying IU using the output confidence intervals (CIs) computed from bootstrap quantile estimators. The standard direct bootstrap method has overcoverage due to convolution of the simulation error and IU; however, the brute-force way of washing away the former is computationally demanding. We present two new bootstrap methods to enhance direct resampling in both statistical and computational efficiencies using shrinkage strategies to down-scale the variabilities encapsulated in the CIs. Our asymptotic analysis shows how both approaches produce tight CIs accounting for IU under limited input data and simulation effort along with the simulation sample-size requirements relative to the input data size. We demonstrate performances of the shrinkage strategies with several numerical experiments and investigate the conditions under which each method performs well. We also show advantages of nonparametric approaches over parametric bootstrap when the distribution family is misspecified and over metamodel approaches when the dimension of the distribution parameters is high. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Science Foundation [CAREER CMMI-1834710, CAREER CMMI-2045400, DMS-1854659, and IIS-1849280]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0044) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0044). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov decision processes (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming-based approach to approximate dynamic programming. This approach can provide both feasible control policies and bounds on the MDPs' optimal policy value, which can be used to establish optimality gaps. However, the approximate linear programs (ALPs) resulting from this approach can often be difficult to solve. To address this computational challenge, we derive novel ALP reformulations that can be used for a broad class of dynamic stochastic matching problems that incorporate, among others, possible match failures and certain restrictions on feasible matchings. We show that these ALP reformulations can be solved efficiently and applied to a broad class of dynamic matching problems. In addition, our numerical results indicate that our ALP reformulations can produce tight bounds that allow us to establish near-optimal policy performance for a broad set of problem instances. Thus, ALP reformulations can present an attractive alternative for applications that involve dynamic stochastic matching. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0203) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0203). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In this research, we pioneer a novel method to evaluate the maximum admissible load (MAL) for virtual machines (VMs) in physical machines (PMs) in data centre networks (DCNs), without restricting DCN topologies. This unique approach simplifies the issue into a single-source, multiple-sink maximum flow problem. It also resolves the maximum elastic scheduling problem by determining the optimal load for consistent growth without reassigning tasks. An effective strategy for these challenges is introduced and validated through extensive simulations. [ABSTRACT FROM AUTHOR]

Aim: There are several case reports describing patients with both optic nerve hypoplasia/septo‐optic‐pituitary dysplasia (ONH/SOD) and gastroschisis (GS). Our aim was to investigate whether ONH/SOD is associated with GS. Methods: A retrospective population‐based study was undertaken using the Population Research Data Repository at the Manitoba Center for Health Policy in Manitoba, Canada to investigate if any patient with ONH/SOD also had GS. In addition, Winnipeg's Surgical Database of Outcomes and Management (WiSDOM), a hospital‐based paediatric surgical database, was searched to ascertain if any of the patients with GS also have ONH/SOD. Results: Cases were 124 patients with ONH/SOD diagnosed during 1990–2019. None had GS. The surgical database had 188 patients from Manitoba with GS during 1991–2019. None had ONH/SOD. Conclusion: There does not appear to be an association between ONH/SOD and GS in our cohorts of patients with these two disorders. [ABSTRACT FROM AUTHOR]

Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States. The data were extracted from a JD data repository, maintained at the Council on Dairy Cattle Breeding, and initially supplied by 2 dairy record processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, IDEXX Paratuberculosis Screening Ab Test (IDX) and Parachek 2 (PCK), were used to analyze milk samples with the ELISA technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of EBVs of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant. The correlations of bulls' PTA with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a nonoverlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs. [ABSTRACT FROM AUTHOR]

Summary: Objective: This systematic review aims to summarize the current body of evidence concerning the prevalence of obesity among clergy (i.e., the officially designated leaders of a religious group) in the United States. Method: From November 2022 to February 2023, five databases, one data repository, and gray matter were searched for articles and data sources. The search was restricted to articles published or raw data collected from 2001 to 2021. Study quality was assessed with a template, and heterogeneity was assessed using the I2 statistic. The protocol for this review was registered with PROSPERO (CRD42022376592). Results: Forty‐seven studies of clergy obesity involving 35,064 individuals were eligible. The pooled prevalence estimate of obesity across studies was 34.8% (95% confidence interval [CI]: 32.5–37.2). Obesity prevalence was found to be increasing over time and to vary considerably between clergy from different religious traditions. Compared to national estimates, from 2005 onwards, obesity prevalence was higher than in the US adult population. [ABSTRACT FROM AUTHOR]

Introduction Patient demographics, such as sex and age, are known risk factors for undergoing revision following primary total hip arthroplasty (THA). The military population is unique because of the increased rates of primary and secondary osteoarthritis of the hip. Treatment options are limited for returning patients to their line of duty; however, THA has been shown to be an effective option. The primary purpose of this study was to evaluate and contrast the demographic differences of patients undergoing primary THA between the U.S. active duty military population and the general population. The secondary goal was to identify the proportion of primary THA performed at the MTF within the military health system (MHS). Methods This was an exempt study determined by the local institutional review board. A retrospective analysis of the MHS Data Repository (MDR) and the National Surgical Quality Improvement Program (NSQIP) was performed. The databases were used to identify the patients who underwent THA from January 1, 2015 to December 31, 2020. The MDR was used to identify demographics such as sex, age, setting of surgery, geographic location, previous military deployments, history of deployment-related injuries, branch of service, and rank. The NSQIP database was queried for sex and age. The median age of the population was compared using the Mann–Whitney U test and gender was compared using the Chi-square test. Results The MDR was used to evaluate 2,734 patients, whereas the NSQIP database was used to evaluate 223,832 patients. In the military population, patients who underwent THA were 87.7% male with an average age of 45 years, whereas in the general population as measured via the NSQIP database, 45.2% patients were male with an average age of 66.0 years. Comparing the two groups, we demonstrated that the military patients were significantly more likely to be younger (P  < .001) and males (P  < .001). Only 29.6% of primary THAs were performed within the MTF. Conclusions Patients in the MHS are undergoing THA at a younger age and are more likely to be male compared to the general population. A significant portion of primary THAs in the MHS are also being performed at civilian institutions. These demographics may result in increased risk of revision; however, long-term studies are warranted to evaluate survivorship in this unique population. [ABSTRACT FROM AUTHOR]

Introduction The aims of this project were to assess (1) the prevalence and timing of post-traumatic osteoarthritis (PTOA) after a traumatic lower limb injury, (2) the risk of PTOA based on injury type, and (3) the association of PTOA with psychological health and quality of life (QoL). Materials and Methods The Wounded Warrior Recovery Project (WWRP) database and the Expeditionary Medical Encounters Dataset were queried to identify service members injured during deployment. The Military Health System Data Repository was utilized to extract medical record data to identify individuals with PTOA. Data on PTSD, depression symptoms, and QoL were extracted from the WWRP. Results Of the 2,061 WWRP participants with lower limb injuries, 124 (6%) were diagnosed with PTOA, with first PTOA diagnosis occurring 3.8 ± 3.1 years after injury. Of the injury categories identified, only fractures were associated with high odds of lower limb PTOA (adjusted odds ratio [OR] = 3.92, 95% confidence interval [CI]: 2.38, 6.44). Individuals with PTOA diagnoses reported lower QoL scores relative to those without PTOA (F (1,2057) = 14.21, B  = −0.05, P  < .05). Additionally, rates of PTSD and depression symptoms were high but not different between those with or without PTOA. Conclusions Despite a low prevalence of lower limb PTOA in our study, fractures increased the risk of PTOA after deployment-related injuries. Additionally, those with PTOA reported lower QoL scores relative to those without PTOA. The findings of this study highlight the personalized needs of patients with trauma beyond just the repair of the immediate injury. [ABSTRACT FROM AUTHOR]

Background Sexual assault remains a high priority challenge for leaders in the U.S. military. The COVID-19 pandemic further complicated the issue by disrupting work and lifestyles, potentially affecting reporting and care-seeking by victims. To date, there are no studies addressing the impact of changes in access to health services and reporting of sexual assault by active duty service members to inform medical and public health interventions in a post-COVID-19 era and during future public health emergencies. Methods Using the Military Health System Data Repository (MDR), we identified a retrospective open cohort of active duty servicemembers receiving care in the Military Health System (MHS) from fiscal years 2018 to 2021. Through the use of ICD-10 diagnostic codes, all corresponding sexual assault care was identified for the cohort before (October 1, 2017—February 28, 2020; pre-pandemic period) and after (March 1, 2020—September 30, 2021; pandemic period) the institution of COVID-19 pandemic protective measures. Pre-pandemic and pandemic period rates of sexual assault associated encounters per 10,000 total health encounters were calculated by geographic location and compared using Wilcoxon's sign rank test. Multivariate log binomial regression models were performed to estimate associated risk of sexual assault among active duty servicemembers and compared across both periods. Results The study identified 47,067,073 encounters for servicemembers in the pre-pandemic period, of which 7,813 (0.016%) were associated with sexual assault. During the pandemic period, 4,377 (0.015%) of 28,926,480 encounters were associated with sexual assault, indicating a 44% reduction in total encounters and a 9% reduction in the proportion of encounters associated with sexual assault. Eighteen installations within the MHS had statistically significant increases in rates of encounters. Most sexual assault encounters occurred in the 20-24 year age, Female, White, Junior Enlisted, Army, and Unmarried categories. During both time-frames, younger age and being female were associated with an increased risk of sexual assault encounters. Race, service branch, and marital status were also associated with the risk of encounters. Conclusion Contrary to reports indicating a modest increase in the number of sexual assaults reported within the DoD, we found a reduction in the frequency of healthcare encounters associated with sexual assault during the pandemic which may be related to reduced access to care. Men comprised approximately 22% of sexual assault encounters delivered through the MHS although the racial distribution of encounters approximates the estimates of the DoD-published demographic statistics. This study illustrates that COVID-19 measures appear to have reduced access to care for sexual assault within the MHS but did not demonstrably alter the demographics of servicemembers seeking care. These findings suggest that the MHS may require contingency plans for future disruptions to care and public health emergencies. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks—low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations. As a result, macrocyclic peptides represent attractive drug candidates. Moreover, many drugs are macrocycles that originated from natural product scaffolds, suggesting that nature offers solutions to the challenges faced by AMPs. In this review, we explore natural cyclic peptides from the DBAASP database. DBAASP is a comprehensive repository of data on antimicrobial/cytotoxic activities and structures of peptides. We analyze the data on small (≤25 AA) ribosomal and non-ribosomal cyclic peptides from DBAASP according to their amino acid composition, bonds used for cyclization, targets they act on, and mechanisms of action. This analysis will enhance our understanding of the small cyclic peptides that nature has provided to defend living organisms. [ABSTRACT FROM AUTHOR]

We present an ice-penetrating radar data set acquired over the Greenland ice sheet by aircraft during the years 1971, 1972, 1974, 1978, and 1979. The data set comprises over 177 000 km of flight lines and contains a wealth of information on the state of the Greenland ice sheet, including information on ice thickness and englacial properties. During data collection in the 1970s, the data were recorded on optical film rolls, and in this paper, we document the digitization of these film rolls and their associated geographical information. Our data digitization enables interaction with and analysis of the data and facilitates comparison with modern-day radar observations. The complete data set in full resolution is available in the Stanford Digital Repository (10.25740/wm135gp2721;), with the associated technical reports. Part of the data set is available as low-resolution JPG files at the Technical University of Denmark's data repository (10.6084/m9.figshare.c.7235299.v1;), with associated technical reports and digitized geographical information. The Stanford Digital Repository serves as long-term storage, providing archival historic preservation in perpetuity, and is not intended as a primary data access point. The Technical University of Denmark (DTU) data repository serves as a primary entry point for data access, with files organized according to acquisition year and flight line in a simple folder structure. Here, we release the full data sets to enable the larger community to access and interact with the data. [ABSTRACT FROM AUTHOR]

We present a comprehensive photometric study of RR Lyrae stars in the M3 globular cluster, utilizing a vast data set of 3140 optical (UBVRI) CCD images spanning 35 yr from astronomical data archives. We have successfully identified previously known 238 RR Lyrae stars from the photometric data, comprising 178 RRab, 49 RRc, and 11 RRd stars. Multiband periodogram was used to significantly improve the long-term periods of 65 per cent of RR Lyrae stars in our sample, thanks to the unprecedentedly long temporal coverage of the observations. The light curve templates were used to obtain accurate and precise mean magnitudes and amplitudes of all RR Lyrae variables. We combined optical (UBVRI) and near-infrared (NIR, JHKs) photometry of RR Lyrae variables to investigate their location in the colour-magnitude diagrams as well as the pulsation properties such as period distributions, Bailey diagrams and amplitude ratios. The period–luminosity relations in R and I bands and Period–Wesenheit relations were derived after excluding outliers identified in CMDs. The Period–Wesenheit relations calibrated via the theoretically predicted relations were used to determine a distance modulus of |$\mu = 15.04 \pm 0.04 \, {\rm (stats)} \pm 0.19 \, {\rm {(syst.)}}$|  mag (using metal-independent WBV Wesenheit) and |$\mu = 15.03 \pm 0.04 \, {\rm (stats)} \pm 0.17 \, {\rm {(syst.)}}$|  mag (using metal-dependent WVI Wesenheit). Our distance measurements are in excellent agreement with published distances to M3 in the literature. We also employed an artificial neural network based comparison of theoretical and observed light curves to determine physical parameters (mass, luminosity, and effective temperature) for 79 non-Blazhko RRab stars that agree with limited literature measurements. [ABSTRACT FROM AUTHOR]