Search

The document titled "INFORMATION FOR CONTRIBUTORS: EDITORIAL POLICIES AND STYLE GUIDELINES" is a resource for authors looking to submit their work to the Academy of Management Learning & Education (AMLE) journal. The journal focuses on management teaching and learning and has sections for research, reviews, critiques, and resources. The document provides guidelines for authors on the submission process, style and format, headings and sections, and citations. It also includes information on plagiarism detection and the review and publication process. [Extracted from the article]

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; in another, DFTB+receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+ or enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities. [ABSTRACT FROM AUTHOR]

Background: Physical inactivity prevalence estimates for youth and adults have been published on a global scale and for various geographical and geopolitical permutations. Only one such study has presented estimates for adults in Muslim countries, and it is nearly 10 years old. I conducted an update of this study by incorporating newer data, refining methods, and including youth estimates. Methods: I identified 47 Muslim countries with physical inactivity data for youth, adults, or both. Data were extracted by country primarily from global estimates reported by Guthold et al in 2018 and 2020 and from World Health Organization surveillance data repositories. Weighted prevalence calculations for total prevalence and by sex, ethnicity (Arab vs non-Arab), and country income group accounted for country population, study sample size, and a country's proportion of Muslims. Z tests and chi-square tests, and follow-up odds ratios and percentage deviations, respectively, were used to determine differences by sex, ethnicity, and country income group. Results: Overall physical inactivity prevalence was 84.2% (youth) and 29.6% (adults). Gaps favoring males over females were observed for youth (5.6% lower prevalence) and adults (9.6% lower prevalence). Gaps favoring non-Arabs over Arabs were observed for youth (3.9% lower) and adults (3.8% lower). No pattern emerged for country income group for youth; however, prevalence for adults trended upward across income groups from low (22.7%) to high (62.0%). Conclusions: Gaps by sex and ethnicity have narrowed since the original report and prevalence values are somewhat higher than current global estimates. [ABSTRACT FROM AUTHOR]

Study Design. Prospective cohort using routinely collected health data. Objective. To compare opioid use based on surgery intensity (low or high). Summary of Background Data. Many factors influence an individual's experience of pain. The extent to which postsurgical opioid use is influenced by the severity of spine surgery is unknown. Methods. The participants were individuals undergoing spine surgery in a large military hospital. Procedures were categorized as low intensity (eg, microdiscectomy and laminectomy) and high intensity (eg, fusion and arthroplasty). The Surgical Scheduling System and Military Health System Data Repository were queried for healthcare utilization the 1 year before and after surgery. We compared opioid use after surgery between groups, adjusting for prior opioid use and surgical complications. Results. A total of 342 individuals met the inclusion criteria, with mean age 45.4 years (SD 10.9), and 33.0% were women. Of these, 221 (64.6%) underwent a low-intensity procedure and 121 (35.4%) underwent a high-intensity procedure. Mean postoperative opioid prescription fills were greater in the high- versus low-intensity group (9.0 vs. 5.7; P < 0.001), as were the mean total days' supply (158.9 vs. 81.8; P < 0.001). Median morphine milligram equivalents (MMEs) were not significantly different (40.2 vs. 42.7; P = 0.287). Of the cohort, 26.3% were chronic opioid users after surgery. Adjusted rates of long-term opioid use were not different between groups when only accounting for prior opioid use but significantly higher for the high-intensity group when adjusting for surgical complications (OR = 2.08; 95% CI 1.09-3.97). Of the entire cohort, 52.5% was still filling opioid prescriptions after 6 months. Conclusions. Higher-intensity procedures were associated with greater postoperative opioid use than lower-intensity procedures. Chronic opioid use was not significantly different between surgical intensity groups when considering only prior opioid use. Chronic opioid use was significantly higher among higher intensity procedures when accounting for surgical complications. The presence of surgical complications is a stronger predictor of postsurgical long-term opioid use in high-intensity surgeries than history of opioid use alone. Level of Evidence. Level III. [ABSTRACT FROM AUTHOR]

Fungi are arguably the most diverse eukaryotic kingdom of organisms in terms of number of estimated species, trophic and life history strategies, and their functions in ecosystems. However, our knowledge of fungi is limited due to a distributional bias; the vast majority of available data on fungi have been compiled from non-tropical regions. Far less is known about fungi from tropical regions, with the bulk of these data being temporally limited surveys for fungal species diversity. Long-term studies (LTS), or repeated sampling from the same region over extended periods, are necessary to fully capture the extent of species diversity in a region, but LTS of fungi from tropical regions are almost non-existent. In this paper, we discuss the contributions of LTS of fungi in tropical regions to alpha diversity, ecological and functional diversity, biogeography, hypothesis testing, and conservation—with an emphasis on an ongoing tropical LTS in the Pakaraima Mountains of Guyana. We show how these contributions refine our understanding of Fungi. We also show that public data repositories such as NCBI, IUCN, and iNaturalist contain less information on tropical fungi compared to non-tropical fungi, and that these discrepancies are more pronounced in fungi than in plants and animals. [ABSTRACT FROM AUTHOR]

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings. [ABSTRACT FROM AUTHOR]

This publication presents data on geophysical measurements performed in the Rospuda wetlands located in North-Eastern Poland. The measurements were carried out by means of the the Electrical Resistivity Imaging (ERI) method, which so far was to our best knowledge never used in the River Rospuda wetland valley. The ERI data were collected in single survey campaign in November 2022 to account for the wet season. During the campaign two ERI profiles were measured. The aim of the field works was to provide the material for illustration of the arrangement of geological layers creating the wetland. The data repository contains detailed data descriptions for each survey site. The ERI data from the selected survey sites can be used first of all to create the conceptual numerical model of groundwater and surface water interaction in this environmentally valuable area, which is to a certain extent a scientific terra incognita, but also for hydrological investigation of hydraulic conductivity and hydrodynamic field, identify geological structure, and characterize engineering properties of the organic soils. [ABSTRACT FROM AUTHOR]

Background: Data sharing improves the value, synthesis, and integrity of research, but rates are low. Data sharing might be improved if data sharing policies were prominent and actionable at every stage of research. We aimed to systematically describe the epidemiology of data sharing policies across the health research lifecycle. Methods: This was a cross‐sectional analysis of the data sharing policies of the largest health research funders, all national ethics committees, all clinical trial registries, the highest‐impact medical journals, and all medical research data repositories. Stakeholders' official websites, online reports, and other records were reviewed up to May 2022. The strength and characteristics of their data sharing policies were assessed, including their policies on data sharing intention statements (a.k.a. data accessibility statements) and on data sharing specifically for coronavirus disease studies. Data were manually extracted in duplicate, and policies were descriptively analysed by their stakeholder and characteristics. Results: Nine hundred and thirty‐five eligible stakeholders were identified: 110 funders, 124 ethics committees, 18 trial registries, 273 journals, and 410 data repositories. Data sharing was required by 41% (45/110) of funders, no ethics committees or trial registries, 19% (52/273) of journals and 6% (24/410) of data repositories. Among funder types, a higher proportion of private (63%, 35/55) and philanthropic (67%, 4/6) funders required data sharing than public funders (12%, 6/49). Conclusion: Data sharing requirements, and even recommendations, were insufficient across health research. Where data sharing was required or recommended, there was limited guidance on implementation. We describe multiple pathways to improve the implementation of data sharing. Public funders and ethics committees are two stakeholders with particularly important untapped opportunities. [ABSTRACT FROM AUTHOR]

The 112Cd(n,2n)111mCd and 112Cd(n,p)112Ag reaction cross sections have been measured at the neutron energy of 14.54 ± 0.24 MeV by using the method of activation and off-line γ -ray spectroscopy. The mano-energetic neutrons were obtained from the D–T reaction using the Purnima Neutron Generator at BARC. The 197Au(n,2n)196Au and115In(n, n1)115mIn reactions were used for neutron flux monitors. The covariance analysis for the uncertainties in the measured reaction cross sections has been done based on various uncertained attributes. The experimental values were compared with the theoritically calculated values from TALYS-1.95 code, evaluated data from different libraries and literature data based on EXFOR compilation. [ABSTRACT FROM AUTHOR]

We are approaching the third decade since the establishment of the very first proteomics repositories back in the mid‐'00s. New experimental approaches and technologies continuously enrich the field while producing vast amounts of mass spectrometry data. Together with initiatives to establish standard terminology and file formats, proteomics is rapidly transforming into a mature component of systems biology. Here we describe the ProteomeXchange consortium repositories. We specifically search, collect and evaluate public human tissue datasets (categorized as "complete" by the repository) submitted in 2015–2022, to both map the existing information and assess the data set reusability. Human tissue data are variably represented in the repositories reviewed, ranging between 10% and 25% of the total data submitted, with cancers being the most represented, followed by neuronal and cardiovascular diseases. About half of the retrieved data sets were found to lack annotations or metadata necessary to directly replicate the analysis. This poses a rough challenge to data reusability and highlights the need to increase awareness of the mage‐tab file format for metadata in the community. Overall, proteomics repositories have evolved greatly over the past 7 years, as they have grown in size and become equipped with various powerful applications and tools that enable data searching and analytical tasks. However, to make the most of this potential, priority must be given to finding ways to secure detailed metadata for each submission, which is likely the next major milestone for proteomics repositories. [ABSTRACT FROM AUTHOR]

The crowing of male Gallus exhibits diverse sound patterns. This is believed to be related to the phenotypic diversity of vocal organs, one of which is influenced by the nucleotide diversity of the associated genes. The SOX9 gene, involved in cartilaginous tissue growth and development, is reported to contribute e in the development of larynx and syrinx. This study aimed to characterize the CDS regions of exons 1 and 2 of the SOX9 gene in junglefowl to assess its diversity. Genomic DNA was extracted from ten individuals of G. varius from Lombok and Sumbawa. The CDS regions of SOX9 gene exons 1 and 2 were amplified using two primer pairs. Additionally, the CDS regions of SOX9 gene exons 1 and 2 from 54 junglefowl SRA data in an online repository were mapped and analyzed. The study identified all nucleotide sequences as CDS regions of SOX9 gene exons 1 and 2. Six shared, and 24 unique haplotypes were constructed. A putative amino acid sequence common to all Gallus species was also identified. The diversity observed in the CDS regions of SOX9 gene exons 1 and 2 nucleotide sequence showed a different level with the diversity observed in its amino acid sequence. [ABSTRACT FROM AUTHOR]

A new nuclear data library for the advanced lattice physics code CASMO5 has been prepared based on JENDL-5. In JENDL-5, the range of data, such as the number of nuclides, has been dramatically expanded for general-purpose applications. At the same time, many essential nuclides for conventional light water reactor (LWR) analysis have also been modified based on state-of-the-art evaluations. The new JENDL-5–based CASMO5 library was prepared by replacing as much of the nuclear data of the current CASMO5 ENDF/B-VII.1–based library as possible with JENDL-5. This study performs initial verification and validation of the new library under typical LWR conditions. Verifications were performed based on the Organisation for Economic Co-operation and Development/Nuclear Energy Agency burnup credit criticality safety benchmark phase III-C, and the calculated kinf and fuel compositions of the boiling water reactor fuel assembly were compared with reported benchmark results. Comparison with the MCNP6.2 result was also performed using the same benchmark model. In addition, a tank-type critical assembly critical experiment and Takahama-3 postirradiation experiment were used for validation. The results indicate that the new library performs well and is comparable to the ENDF/B-VII.1–based library in predictions of reactivity and fuel compositions for typical LWR systems. [ABSTRACT FROM AUTHOR]

This cross-sectional study examines 4 preexisting datasets to determine how symptom-based definitions and assessments contribute to a distinct probability pattern for the occurrence of symptom combinations across mental disorders. Key Points: Question: Is there a common pattern of symptom combinations across mental disorders? Finding: This cross-sectional study found a specific pattern across 4 empirical samples (N = 155 474), with 41.7% to 99.8% of symptom combinations being reported by less than 1% of the sample, while the 1% most frequent combinations were highly prevalent in 33.1% to 78.6% of the corresponding sample. Because of the interdependence of a disorder's symptoms, not all symptom combinations are equally likely. Meaning: Polythetic definitions lead to a common pattern of symptom heterogeneity: the presence of few prototypical and many atypical symptom combinations. Importance: DSM criteria are polythetic, allowing for heterogeneity of symptoms among individuals with the same disorder. In empirical research, most combinations were not found or only rarely found, prompting criticism of this heterogeneity. Objective: To elaborate how symptom-based definitions and assessments contribute to a distinct probability pattern for the occurrence of symptom combinations. Design, Setting, and Participants: This cross-sectional study involved a theoretical argument, simulation, and secondary data analysis of 4 preexisting datasets, each consisting of symptoms from 1 of the following syndromes: posttraumatic stress disorder, depression, schizophrenia, and anxiety. Data were obtained from various sources, including the National Institute of Mental Health Data Archive and Department of Veteran Affairs. A total of 155 474 participants were included (individual studies were 3930 to 63 742 individuals in size). Data were analyzed between July 2021 and January 2024. Exposure: For each participant, the presence or absence of each assessed symptom and their combination was determined. The number of all combinations and their individual frequencies were assessed. Main Outcome and Measure: Probability or frequency of unique symptom combinations and their distribution. Results: Among the 155 474 participants, the mean (SD) age was 47.5 (14.8) years; 33 933 (21.8%) self-identified as female and 121 541 (78.2%) as male. Because of the interrelation between symptoms, some symptom combinations were significantly more likely than others. The distribution of the combinations' probability was heavily skewed with most combinations having a very low probability. Across all 4 empirical samples, the 1% most common combinations were prevalent in a total of 33.1% to 78.6% of the corresponding sample. At the same time, many combinations (ranging from 41.7% to 99.8%) were reported by less than 1% of the sample. Conclusions and Relevance: This study found that within-disorder symptom heterogeneity followed a specific pattern consisting of few prevalent, prototypical combinations and numerous combinations with a very low probability of occurrence. Future discussions about the revision of diagnostic criteria should take this specific pattern into account by focusing not only on the absolute number of symptom combinations but also on their individual and cumulative probabilities. Findings from clinical populations using common diagnostic criteria may have limited generalizability to the large group of individuals with a low-probability symptom combination. [ABSTRACT FROM AUTHOR]

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data's storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58 %, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event. [ABSTRACT FROM AUTHOR]

Currently, online learning systems in the education sector are widely used and have become a new trend, generating large amounts of educational data based on students' activities. In order to improve online learning experiences, sophisticated data analysis techniques are required. Adding value to E-learning platforms through the efficient processing of big learning data is possible with Big Data. With time, the E-learning management system's repository expands and becomes a rich source of learning materials. Subject matter experts may benefit from using E-learning resources to reuse previously created content when creating online content. In addition, it might be beneficial to the students by giving them access to the pertinent documents for achieving their learning objectives effectively. An improved intelligent information retrieval and reliable storage (OIIRS) scheme is proposed for E-learning using hybrid deep learning techniques. Assume that relevant E-learning documents are stored in cloud and dynamically updated according to users' status. First, we present a highly robust and lightweight crypto, i.e., optimized CLEFIA, for securely storing data in local repositories that improve the reliability of data loading. We develop an improved butterfly optimization algorithm to provide an optimal solution for CLEFIA that selects private keys. In addition, a hybrid deep learning method, i.e., backward diagonal search-based deep recurrent neural network (BD-DRNN) is introduced for optimal intelligent information retrieval based on keywords rather than semantics. Here, feature extraction and key feature matching are performed by the modified Hungarian optimization (MHO) algorithm that improves searching accuracy. Finally, we test our proposed OIIRS scheme with different benchmark datasets and use simulation results to test the performance. [ABSTRACT FROM AUTHOR]

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future. [ABSTRACT FROM AUTHOR]

Calls to make scientific research more open have gained traction with a range of societal stakeholders. Open Science practices include but are not limited to the early sharing of results via preprints and openly sharing outputs such as data and code to make research more reproducible and extensible. Existing evidence shows that adopting Open Science practices has effects in several domains. In this study, we investigate whether adopting one or more Open Science practices leads to significantly higher citations for an associated publication, which is one form of academic impact. We use a novel dataset known as Open Science Indicators, produced by PLOS and DataSeer, which includes all PLOS publications from 2018 to 2023 as well as a comparison group sampled from the PMC Open Access Subset. In total, we analyze circa 122'000 publications. We calculate publication and author-level citation indicators and use a broad set of control variables to isolate the effect of Open Science Indicators on received citations. We show that Open Science practices are adopted to different degrees across scientific disciplines. We find that the early release of a publication as a preprint correlates with a significant positive citation advantage of about 20.2% (±.7) on average. We also find that sharing data in an online repository correlates with a smaller yet still positive citation advantage of 4.3% (±.8) on average. However, we do not find a significant citation advantage for sharing code. Further research is needed on additional or alternative measures of impact beyond citations. Our results are likely to be of interest to researchers, as well as publishers, research funders, and policymakers. [ABSTRACT FROM AUTHOR]

One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical clinical measurements that have been used by clinicians for years to describe disease processes and interpret therapeutic outcomes. Without such integration, even the most promising data from emerging technologies may have limited, if any, clinical utility. This paper presents an approach to address this challenge, illustrated through an example in Parkinson's Disease (PD) management. We show how data from various sensing sources can be integrated with traditional clinical measurements used in PD; furthermore, we show how leveraging Big Data frameworks, augmented by Artificial Intelligence (AI) algorithms, can distinctively enrich the data resources available to clinicians. We showcase the potential of this approach in a cohort of 50 PD patients who underwent both evaluations with an Integrated Motion Analysis Suite (IMAS) composed of a battery of multimodal, portable, and wearable sensors and traditional Unified Parkinson's Disease Rating Scale (UPDRS)-III evaluations. Through techniques including Principal Component Analysis (PCA), elastic net regression, and clustering analysis we demonstrate how this combined approach can be used to improve clinical motor assessments and to develop personalized treatments. The scalability of our approach enables systematic data generation and analysis on increasingly larger datasets, confirming the integration potential of IMAS, whose use in PD assessments is validated herein, within Big Data paradigms. Compared to existing approaches, our solution offers a more comprehensive, multi-dimensional view of patient data, enabling deeper clinical insights and greater potential for personalized treatment strategies. Additionally, we show how IMAS can be integrated into established clinical practices, facilitating its adoption in routine care and complementing emerging methods, for instance, non-invasive brain stimulation. Future work will aim to augment our data repositories with additional clinical data, such as imaging and biospecimen data, to further broaden and enhance these foundational methodologies, leveraging the full potential of Big Data and AI. [ABSTRACT FROM AUTHOR]

The article introduces AquiParameter, an interactive web-based tool for statistical assessment of hydrogeological parameters. It provides over 6000 records of hydrogeological variables for various geological materials, making it a valuable resource for groundwater assessments and research. AquiParameter is user-friendly, free, and does not require coding experience, making it accessible to a wide range of users, including students, researchers, and groundwater professionals. The tool offers modules for data visualization, boxplots, histograms, correlation analysis, and data download, enhancing its utility for diverse users in the field of hydrogeology. [Extracted from the article]

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998–2014), PROBA-V (2013–2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications. [ABSTRACT FROM AUTHOR]

Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. This research can contribute toward developing a more refined, accurate and effective therapy accounting for specific patient needs. Methods: To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately. Results: The developed novel prediction algorithm demonstrates the ability to support complex clinical decision-making processes in the treatment of NSCLC. It effectively addresses patient heterogeneity, offering predictions that are both refined and personalized in improving the precision of chemotherapy regimens prescribed to the eligible patients. Conclusion: This research should contribute substantial advancement of cancer treatments by improving the accuracy and efficacy of chemotherapy treatments for a targeted group of patients who need the right treatment. The integration of complex machine learning techniques with genomic data holds substantial potential to transform current cancer treatment paradigms by providing robust support in clinical decision-making. [ABSTRACT FROM AUTHOR]

To enhance the stability of floating wind turbine platforms, this study combines the structural characteristics of helical side plates and proposes the installation of serrated helical side plates on Spar wind turbine platforms. Based on potential flow theory, the present study employs blade element momentum theory and radiation‐diffraction theory. Upon establishing a dynamic data link library, wind‐wave coupling was implemented, and the dynamic response characteristics of platforms with different helical side plates were compared. The results indicate that in the frequency domain analysis, the platform with serrated helical side plates demonstrates reduced sensitivity to waves, with a particularly notable increase in added mass in the heave direction, suggesting excellent hydrodynamic performance. In the time domain analysis, improvements in the platform's surge and heave stability performances were observed, measuring 10.99% and 10.64%, respectively, in extreme conditions. Owing to the unique features of the serrated structure, the tension in the mooring chains on the wave‐facing side is reduced, thereby effectively lowering the fatigue load on the mooring chains. [ABSTRACT FROM AUTHOR]

Data augmentation is commonly utilized to increase the size and diversity of training sets for deep learning tasks. In this study, we propose a novel application of an existing image generation approach in the domain of realistic eye images that leverages data collected from 40 subjects. This hybrid method combines the benefits of precise control over the image content provided by 3D rendering, while introducing the previously lacking photorealism and diversity into synthetic images through neural style transfer. We prove its general efficacy as a data augmentation tool for appearance-based gaze estimation when generated data are mixed with a sparse train set of real images. It improved the results for 39 out of 40 subjects, with an 11.22 % mean and a 19.75 % maximum decrease in gaze estimation error, achieving similar metrics for train and held-out subjects. We release our data repository of eye images with gaze labels used in this work for public access. [ABSTRACT FROM AUTHOR]

We report a new image-based inter-analyst study to investigate fission-track grain selection and analysis by 13 participants from an image data set that included grains of variable quality. Results suggest that participants with less experience show a higher rate of selecting unsuitable grains, while participants from the same laboratories generally provide similar results. Less analysis experience may result in the rejection of suitable grains, or inclusion of unsuitable ones. While inappropriate omission and inclusion can both bias results, the latter is more pernicious due to the standard practice of achieving a predecided number of analyses; particularly in difficult samples, there is a danger of "squeezing the rock" by weakening selection criteria. Juxtaposing selected regions of interest (ROIs) on the same grains indicates that zoned grains and grains with inclusions and defects yield varying track density estimates, indicating that ROI placement can be an influential factor. We propose developing image data repositories for global data transparency, a global guidance for fission-track analysis, digital teaching modules, and open science. We also point out the need for new approaches for zeta calibration that include consideration of grain quality, methods of uranium determination, and etching protocols. [ABSTRACT FROM AUTHOR]

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a "one-pot" process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches. [ABSTRACT FROM AUTHOR]

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available. Scientific contribution Here, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies. [ABSTRACT FROM AUTHOR]

Attentional bias and deficits in attentional control are associated with posttraumatic stress disorder (PTSD) symptoms. Attention control training (ACT) may address these factors. We reviewed randomized controlled trials (RCTs) of ACT for PTSD to address unanswered questions about ACT's effectiveness, tolerability, and implementation. Studies were included if they were an RCT that used an adult sample, recruited participants with a PTSD diagnosis, and had ACT as at least one treatment arm. The PTSD Trials Standardized Data Repository (PTSD‐Repository) and additional databases were searched to identify PTSD RCTs published through May 2024. Seven studies met the inclusion criteria (N = 407). The effect size for ACT versus a comparison condition on PTSD symptoms was large, but the confidence interval (CI) overlapped with 0, g = 0.75, 95% CI [‐0.63, 2.12]. The same pattern was observed for attention bias variability, g = 1.04, 95% CI [‐0.90, 2.98]. There was a significant within‐group effect of ACT on self‐reported PTSD symptoms, g = ‐1.43, 95% CI [‐2.83, ‐0.03]. Risk of bias varied, with high risk of bias being primarily due to bias in the measurement of the outcome. These effects should be interpreted cautiously given the significant heterogeneity and wide confidence intervals observed. It remains unclear for whom and under what conditions ACT may be most effective. Future studies should move beyond response time measures, employ an inactive comparator, and examine the mechanism of action to determine whether ACT could be a viable intervention for PTSD. [ABSTRACT FROM AUTHOR]

Racial and ethnic minorities bear a disproportionate burden of type 2 diabetes (T2D) and its complications, with social determinants of health (SDoH) recognized as key drivers of these disparities. Implementing efficient and effective social needs management strategies is crucial. We propose a machine learning analytic pipeline to calculate the individualized polysocial risk score (iPsRS), which can identify T2D patients at high social risk for hospitalization, incorporating explainable AI techniques and algorithmic fairness optimization. We use electronic health records (EHR) data from T2D patients in the University of Florida Health Integrated Data Repository, incorporating both contextual SDoH (e.g., neighborhood deprivation) and person-level SDoH (e.g., housing instability). After fairness optimization across racial and ethnic groups, the iPsRS achieved a C statistic of 0.71 in predicting 1-year hospitalization. Our iPsRS can fairly and accurately screen patients with T2D who are at increased social risk for hospitalization. Racial and ethnic minorities bear a disproportionate burden of type 2 diabetes (T2D) and its complications, with social determinants of health (SDoH) recognized as key drivers of these disparities. Here, the authors developed an individualized polysocial risk score (iPsRS) to screen for unmet social needs essential to hospitalization risk in patients with Type 2 Diabetes, incorporating fairness optimization and explainable AI. [ABSTRACT FROM AUTHOR]

Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, refactored ETL (extract, transform and load) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled, making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the "WoSIS 2023 snapshot". A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures and standardisation of the units of measurement. The 2023 snapshot considers soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity) and physical properties (soil texture (sand, silt and clay), bulk density, coarse fragments and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil / solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for "fitness for intended use" are provided to facilitate informed data use: (a) positional uncertainty of the profile's site location, (b) possible uncertainty associated with the operationally defined analytical procedures and (c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various web services. To permit consistent referencing and citation, we also provide a static snapshot (in this case, December 2023). This snapshot comprises quality-assessed and standardised data for 228 000 geo-referenced profiles. The data come from 174 countries and represent more than 900 000 soil layers (or horizons) and over 6 million records. The number of measurements for each soil property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of "public" soil data by prospective data contributors; possible solutions for this are discussed. The WoSIS 2023 snapshot is archived and freely available at 10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023). [ABSTRACT FROM AUTHOR]

Motivation Antibody−antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein−protein docking tools to use machine learning generated input structures for information-driven docking. Results In an information-driven scenario, we find that HADDOCK can generate accurate models of antibody−antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high-quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. Availability and implementation The source code of HADDOCK3 is freely available at github.com/haddocking/haddock3. The code to generate and analyse the data is available at github.com/haddocking/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org/labs/32/1139) at the SBGRID data repository. [ABSTRACT FROM AUTHOR]

DNA‐based data storage is a new technology in computational and synthetic biology, that offers a solution for long‐term, high‐density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule‐based quaternary transcoding method that satisfies bio‐constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio‐constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval. [ABSTRACT FROM AUTHOR]

We installed a meteorological recording system at Badwater (elev. −75 m), the lowest point in Death Valley, California and recorded data over the period 1998–2019. A second station (the Outhouse Station) was established nearby from 2014 to 2019. Here, we report on and publicly archive the data from these two stations. Of interest was the comparison between two air temperature measurements at the Badwater Station, the first with an aspirated platinum resistance temperature device and the second with a thermistor probe in a passive sun shield. During the hottest periods of the summer when temperatures were typically between 30°C at night and 50°C daily peak, the passively shielded sensor indicated up to 0.5°C warmer than the aspirated temperature sensor due to radiative effects. The data suggest a correction for radiative heating of (T–35)/30, for T > 35°C, where, T, is the uncorrected temperature reading of a passively shielded sensor subtracted after any calibration at lower temperatures. Our station was the first precision temperature measurements at Badwater. A longer record exists for the reporting station near the visitor's centre at the Furnace Creek. The summer temperature maxima at the Badwater site correlate well with the values the same day from the Furnace Creek site. The daily maximum temperatures in winter at the Badwater site appear to be about 1°C lower than at the Furnace Creek site. The largest differences are in the minimum temperatures for which the Badwater site averages about 2–3°C warmer than the Furnace Creek site. [ABSTRACT FROM AUTHOR]

The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard‐copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good‐quality historical observational data. The aim of this innovative project was to use classroom‐based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second‐year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data‐sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll‐out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs. [ABSTRACT FROM AUTHOR]

Chert is a sedimentary rock abundant and conspicuous throughout the geologic record. It serves as an essential geological archive and is vital for palaeogeographic reconstruction. The Chert Database Working Group is part of the OneSediment Working Groups of the Deep‐time Digital Earth (DDE) Big Science Program. To facilitate the sharing of chert information and promote chert research, we have compiled a summary of literature containing chert information worldwide and established the Global Chert Database (GCDB). The main body of the current database consists of seven data tables, each providing details on references, lithology, depositional age, geographic location, depositional environment and geochemical information for each sample. As of December 2023, the GCDB contains 8417 sample data from 617 pieces of literature, which can be downloaded from the 'DDE Data Publish & Repository'. [ABSTRACT FROM AUTHOR]

For more than three decades, satellite altimetry has provided valuable measurement data for the monitoring and analysis of ocean and inland water surfaces. Since 1992, there have always been at least two simultaneous missions providing continuous measurement data, starting with TOPEX/Poseidon and ERS‐1 in the early 1990s and continuing with about 10 satellites active today, including ICESat‐2, Sentinel‐6A and SWOT. Most mission data are freely available, but in different formats, processing levels and with respect to different references (e.g. ellipsoid or time), making common multi‐mission applications difficult. In addition, the derivation of ready‐to‐use and high‐quality scientific products requires expertise that not every user is willing to acquire. Over the years, DGFI‐TUM has developed and maintained an Open Altimeter Database (OpenADB) that allows consistent data management and combination. It consists of the internal Multi‐Version Altimetry (MVA) data repository and the OpenADB web portal. OpenADB provides user‐friendly access to derived along‐track products, such as sea surface heights and ocean tides. It also provides general information about the satellite altimetry missions, their observing configurations and about the data provided in the database. All products are freely available on the OpenADB web portal (https://openadb.dgfi.tum.de) after registration. [ABSTRACT FROM AUTHOR]

Complications of detrital zircons databases provide a means for statistically analysing a variety of significant geological problems. In this work, we tried to collect a database about the South China Phanerozoic detrital zircon geochronology data. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, the database contains a total of 699 samples with 55,532 U–Pb ages and 3,770 effective Hf isotope data, from 130 papers. Abundant information including reference title, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages, εHf(t) values, etc., have been involved in our database, and all data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. Through the integrated data, we can improve the previous studies and avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

Detrital zircon U–Pb chronology database of Precambrian deposits provides a context for the interpretation of the origin and evolution of ancient crust. Here, we tried to summarize the published literature containing detrital zircon U–Pb ages and Hf isotope data of Precambrian deposits in South China and then established a database, which contains details of information, such as reference, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages and εHf(t) values. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, 610 samples with 38,278 U–Pb ages and 8,798 Lu‐Hf isotope data were collected from 136 papers, and these data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. The purpose of the establishment of the dataset is to provide guidance and convenience for the research direction of future generations in South China and to improve the previous studies through the integrated data to avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

Firearm injuries are the leading cause of death among children aged 0 to 17 years in the United States. This study examines the factors associated with recurrent firearm injury among children who presented with a nonfatal firearm injury in the St. Louis, Missouri, region. Visual Abstract. Factors Associated With Recurrent Pediatric Firearm Injury: Firearm injuries are the leading cause of death among children aged 0 to 17 years in the United States. This study examines the factors associated with recurrent firearm injury among children who presented with a nonfatal firearm injury in the St. Louis, Missouri, region. Background: Firearm injuries are the leading cause of death among children aged 0 to 17 years in the United States. Objective: To examine the factors associated with recurrent firearm injury among children who presented with acute (index) nonfatal firearm injury in the St. Louis region. Design: Multicenter, observational, cohort study. Setting: 2 adult and 2 pediatric level I trauma hospitals in St. Louis, Missouri. Participants: Pediatric patients aged 0 to 17 years presenting with an index firearm injury between 2010 and 2019. Measurements: From the St. Louis Region-Wide Hospital-Based Violence Intervention Program Data Repository, we collected data on firearm-injured patient demographics, hospital and diagnostic information, health insurance status, and mortality. The Social Vulnerability Index was used to characterize the social vulnerability of the census tracts of patients' residences. Analysis included descriptive statistics and time-to-event analyses estimating the cumulative incidence of experiencing a recurrent firearm injury. Results: During the 10-year study period, 1340 children presented with an index firearm injury. Most patients were Black (87%), non-Hispanic (99%), male (84%), and between the ages of 15 and 17 years (67%). The estimated risk for firearm reinjury was 6% at 1 year and 14% at 5 years after initial injury. Male children and those seen at an adult hospital were at increased risk for reinjury. Limitation: Our data set does not account for injuries occurring outside of the study period and for reinjuries presenting to nonstudy hospitals. Conclusion: Children who experience an initial firearm injury are at high risk for experiencing a recurrent firearm injury. Interventions are needed to reduce reinjury and address inequities in the demographic and clinical profiles within this cohort of children. Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

With the growing number of genomic data in public repositories, efficient search methodologies have become a basic need to reach the relevant genomic data. However, this need cannot be fulfilled with the current repositories because they offer a limited search option which is a lexical matching of textual descriptions or metadata of the experiments. This technique is insufficient to get the required information needed to detect similarities between experiments within a large data collection. Due to the limitation of the existing repositories, in this study, we develop a text-based experiment retrieval framework by using both lexical and semantic similarity approaches to find similarities between experiments, and their retrieval performance was compared. This study is the first attempt to use text-driven semantic analysis approaches for developing a retrieval framework for experiments. An empirical study was conducted on a large textual description of Arabidopsis microarray experiments from the Gene Expression Omnibus database. In the proposed model, Jaccard similarity was used as a lexical similarity approach; Latent Semantic Analysis, Probabilistic Latent Semantic Analysis and Latent Dirichlet allocation were used as semantic similarity approaches to detect similarities between the textual descriptions of the experiments. According to the experimental results, relevant experiments can be retrieved successfully by text-driven semantic similarity approaches compared with the lexical similarity approach. [ABSTRACT FROM AUTHOR]

Context. Species connectivity mapping is a technically challenging task for conservation practitioners and nongovernment organisations to undertake as it requires experience in geographic information systems and often some computer programming. Aims. We developed a decision support tool to provide spatial information and data on potential habitat connectivity and optimum connectivity pathways for a selection of forest-dependent vertebrate fauna in eastern and south-western Australia. Methods. We systematically searched spatial data repositories for Australian spatial datasets for modelling connectivity. A least cost paths and patch connectivity approach was used to map potential habitat connectivity for (1) a single species - the glossy black cockatoo (Calyptorhynchus lathami) of South East Queensland, and (2) four species guilds - rainforest pigeons, gliding possums, the black cockatoos of south-western Western Australia and a landscape level forest connectivity. Optimum connectivity pathways were assessed for protection status. Key results. In total 71 spatial datasets useful for habitat connectivity mapping were identified. Species and guild modelling found that the protection status for optimum connectivity pathways varied between 24.7% and 53.3%. A decision support mapping tool was then created to enable users to interactively explore the connectivity data and download the spatial datasets for further analysis. Conclusions. The development of a decision support tool for mapping habitat connectivity in eastern and south-western Australia represents a useful platform for conservation practitioners as it provides valuable spatial information on potential connectivity pathways for forest-dependent vertebrate fauna. Implications. The tool can aid in the prioritisation of conservation actions aimed at enhancing habitat connectivity and mitigating the impacts of habitat fragmentation on biodiversity in the two regions. [ABSTRACT FROM AUTHOR]

Billions of genomic sequences and records of species occurrence are available in public repositories (e.g. National Center for Biotechnology Information, NCBI and the Global Biodiversity Information Facility, GBIF). By implementing analytical tools from different scientific disciplines, data mining these databases can aid in the global surveillance of zoonotic pathogens that circulate among wildlife. We illustrate this by investigating the Hantavirus–rodent system in the Americas, i.e. New World Hantaviruses (NWH). First, we considered the circulation of pathogenic NWH among Cricetidae rodents, by inferring the phylogenetic links among 277 genomic samples of the S segment (N protein) of NWH found in 55 species. Second, we used machine learning to assess the impact of land use on the probability of presence of the rodent species linked with reservoirs of pathogenic Hantaviruses. Our results show that hosts are widely present across the Americas. Some hosts are present in the primary forest and agricultural land, but not in the secondary forest, whereas other hosts are present in secondary forest and agricultural land. The diversity of host species allows Hantavirus to circulate in a wide spectrum of habitats, in particular rural rather than urban. We highlight that public repositories of genomic data and species occurrence are very useful resources for monitoring potential enzootic transmission and spillover of zoonotic viruses in relation with the changes that humans produce in the biosphere. [ABSTRACT FROM AUTHOR]

A unique dataset of marine atmospheric electric field observations over the Atlantic Ocean is described. The data are relevant not only for atmospheric electricity studies, but more generally for studies of the Earth's atmosphere and climate variability, as well as space-earth interactions studies. In addition to the atmospheric electric field data, the dataset includes simultaneous measurements of other atmospheric variables, including gamma radiation, visibility, and solar radiation. These ancillary observations not only support interpretation and understanding of the atmospheric electric field data, but are also of interest in themselves. The entire framework from data collection to final derived datasets has been duly documented to ensure traceability and reproducibility of the whole data curation chain. All the data, from raw measurements to final datasets, are preserved in data repositories with a corresponding assigned DOI. Final datasets are available from the Figshare repository (https://figshare.com/projects/SAIL%5fData/178500) and computational notebooks containing the code used at every step of the data curation chain are available from the Zenodo repository (https://zenodo.org/communities/sail). [ABSTRACT FROM AUTHOR]

This document discusses the shift in first-line therapies for United States Veterans Affairs (VA) patients with chronic lymphocytic leukemia (CLL). CLL is a manageable but incurable condition, and a watch-and-wait approach is often used for asymptomatic patients. However, for patients requiring treatment, targeted therapies have revolutionized the treatment landscape. This study analyzed data from the VA system and found that the use of targeted therapies has increased over time, surpassing traditional chemotherapy/chemoimmunotherapy. Specifically, the use of ibrutinib accounted for the greatest proportion of targeted therapy use, followed by venetoclax and acalabrutinib. The study highlights the importance of understanding treatment patterns and the impact of targeted therapies in CLL management. [Extracted from the article]

The 2023 Editor's Report for the journal Economic Inquiry highlights a successful year with increasing submission numbers and quick decision times. The report also includes tables showing the manuscript disposition and the number of articles published by topic area. The editorial board has undergone changes, with some members stepping down and new members joining. The journal has transitioned to an online-only publishing model and has implemented a data policy and plagiarism detection software. The submission statistics for 2023 have increased, and the journal maintains a commitment to rapid decision-making and low revision rates. The impact factor has slightly fluctuated, and awards have been given for outstanding articles and review service. The editorial board expresses gratitude to the referees who contributed to the journal, including individuals from diverse backgrounds and expertise. The author also acknowledges the assistance of Andra Johnson, the journal's Editorial Assistant, and looks forward to another successful year in 2024. [Extracted from the article]

Purpose: The current investigation aimed to establish preliminary normative data for endoscopic swallow studies (FEES). The investigators collected data for three timing measures (time to whiteout, duration of whiteout, and total swallow time), three swallowing outcomes (safety, efficiency, and number of swallows per bolus), and one physiologic event (glottal response), for both healthy young and older adults using two liquid volumes, one pureed bolus and a solid bolus. Methods: Blinded raters retrospectively analyzed 65 randomly selected, deidentified videos of endoscopic swallowing examinations from a pool of 163 young and older adults with typical swallowing abilities. Timing measures and analysis of airway invasion, amount of residue, number of swallows, and glottal response were obtained. Results: Preliminary means and quartiles were established for healthy adults in two age groups (young and old), for time to whiteout (WO), number of swallows per bolus, glottal response, Yale Residue Rating Scale Scores, Penetration-Aspiration Scale scores, duration of WO, and total swallow duration. Differences were found between the older and younger groups. Conclusion: The current study represents a preliminary attempt to provide quantitative and normative values for FEES. These data represent reference values to which other bolus presentations and populations can be compared. The data represents proof of concept and merits additional investigation. IRB ID: 1756246-2: Approved 2022/06/06. Clinical Trial Registration: Study does not meet criteria. Data Repository: https://doi.org/10.6084/m9.figshare.25800025. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, public health measures were implemented, yet it is unknown whether these measures affected medication access in those with schizophrenia (SCZ). This study aimed to assess whether the antipsychotic utilization in SCZ changed during the pandemic. We used dispensed prescription drug data from the Canadian province of Manitoba in individuals with SCZ using linked administrative data from the Manitoba Population Research Data Repository. The quarterly incident and prevalent dispensation of antipsychotics at two periods were compared with the expected trend (April 1, 2015 to April 1, 2020 and 2021) using linear autoregression. We stratified the primary results by age and sex and examined multiple subgroups. There were 9045 individuals with SCZ in the first fiscal quarter of 2020. The prevalent use of the most common antipsychotics were: olanzapine (206.7/1000), risperidone (190.8/1000), quetiapine (174.4/1000), and clozapine (100.9/1000). The overall prevalent use of antipsychotics remained stable during the pandemic compared with the expected trend. A significant decrease in the incident use in April–June 2020 (estimate: -1.3, 95%CI:-2.2,-0.3) was noted compared with the expected. A significantly higher incidence of atypical antipsychotics (estimate: 1.4, 95%CI: 0.2,2.5) and risperidone separately (estimate: 1.8, 95%CI: 0.2,3.3) was noted in 2021 compared with expected. This study found a decline in the receipt of antipsychotics for people with SCZ during the initial implementation of COVID-19 public health measures, particularly on the overall incidence. Future work on investigating the impact of these trends on SCZ outcomes is needed to inform future pandemic-related policies. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine the kinetic parameters of the RSG-GAS equilibrium core. The calculated kinetic parameters are the effective delayed neutron fraction βeff, the neutron generation time Ʌ, and the prompt neutron lifetime ℓ since they are related to the safety of nuclear operations. The kinetic parameters were calculated using the Serpent 2 code with the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries. Calculations were performed using various adjoint-weighted methods such as Meulekamp's method, Nauchi's method, the Iterated Fission Probability method, and the Perturbation Technique. The calculated results of the six-group delayed neutron fraction by the Meulekamp and the IFP methods showed no significant difference. Choosing the IFP method as the reference, the maximum difference for βeff (694 pcm) is 0.73%, and the maximum difference for Ʌ and ℓ is 1.89%. The calculated kinetic parameters with ENDF/B-VII.1 and ENDF/B-VIII.0 are quite close, with a maximum difference of 0.9%. The sensitivity analysis results indicate several nuclides and reaction types that dominantly affect the βeff and Λ. The results of the kinetic parameter calculations can be used for the safety analysis of the RSG-GAS equilibrium core. [ABSTRACT FROM AUTHOR]

U.S. copyright law does not account for Indigenous knowledge. These items, such as stories, dances, songs, and oral teachings are data and works authored by a sovereign community, not just individuals. Indigenous data sovereignty provides that data and cultural knowledge are subject to Tribal protections. Tribes have the right as a sovereign nation to govern the collection, ownership, and application of its own data and cultural knowledge. Assimilating Indigenous knowledge into non-Indigenous works is a copyright issue from an Indigenous perspective. Librarians can identify these Indigenous copyright issues to support local Indigenous Peoples and promote efforts toward achieving Indigenous data sovereignty. [ABSTRACT FROM AUTHOR]

Fraud detection plays a crucial role in the modern banking sector, aiming to mitigate financial losses affecting both individuals and financial institutions. With a significant portion of the population regularly using credit cards, efforts to enhance financial inclusivity have led to increased card usage. Additionally, the rise of e-commerce has brought about a surge in credit card fraud incidents. Unfortunately, traditional statistical methods used for identifying credit card fraud are time-consuming and may not provide accurate results. As a result, machine learning algorithms have become widely adopted for effective credit card fraud detection. This study addresses the challenge of an imbalanced credit card dataset by employing three sampling strategies: cluster centroid-based majority under-sampling technique (CCMUT), synthetic minority oversampling technique (SMOTE), and oversampling technique. The training dataset is then used to train nine machine learning algorithms, including Random Forest (RF), k nearest neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Ada-boost, Extra-trees, MLP classifier, Naive Bayes, and Gradient Boosting Classifier. The performance of these approaches is assessed using metrics such as accuracy, precision, recall, f1 score, and f2 score. The dataset used in this study was obtained from the Kaggle data repository. [ABSTRACT FROM AUTHOR]

As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization. [ABSTRACT FROM AUTHOR]