bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Rise in the Participation of Asia Pacific States in the Proceedings of the International Court of Justice (2010-2023)
Authors: de la Rasilla Ignacio 
Published: September 2024

Abstract
Recent State practice shows a unprecedented rise in participation by Asia Pacific States in proceedings before the International Court of Justice. Section II provides a holistic view with consideration of these recent developments in the light of sub-regional variation regarding the ICJ in the Asia Pacific region providing four detailed tables and three maps to that effect. Section III follows on by providing a thematically ordered analytical survey of all eighteen recent contentious cases brought by, or involving, Asia Pacific States before the Court since 2010. Section IV examines a rising pattern in participation by Asia Pacific States, including China, in ICJ advisory proceedings over the last twenty-five years, culminating with their all-time record participation in the Obligations of States in Respect of Climate Change. Section V illustrates the epistemological value of the regional and sub-regional lens in complementing country-specific lenses when studying the engagement of sovereign States with the ICJ in the light of recent State practice from the Asia Pacific region. The conclusion considers the potential for, and limitations of, future engagement by Asia Pacific States with the ICJ.
CPMKG: a condition-based knowledge graph for precision medicine
Authors: Yang Jiaxin , Zhuang Xinhao , Li Zhenqi , Xiong Gang , Xu Ping , Ling Yunchao , Zhang Guoqing 
Published: September 2024

Abstract
Personalized medicine tailors treatments and dosages based on a patient’s unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information—such as dosage, effectiveness, and side effects—affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems. To address these challenges, we developed CPMKG, a condition-based platform that enables comprehensive knowledge representation. Through information extraction and meticulous curation, we compiled 307 614 knowledge entries, encompassing thousands of drugs, diseases, phenotypes (complications/side effects), genes, and genomic variations across four key categories: drug side effects, drug sensitivity, drug mechanisms, and drug indications. CPMKG facilitates drug-centric exploration and enables condition-based multiknowledge inference, accelerating knowledge discovery through three pivotal applications. To enhance user experience, we seamlessly integrated a sophisticated large language model that provides textual interpretations for each subgraph, bridging the gap between structured graphs and language expressions. With its comprehensive knowledge graph and user-centric applications, CPMKG serves as a valuable resource for clinical research, offering drug information tailored to personalized genetic profiles, syndromes, and phenotypes.
Database URL: https://www.biosino.org/cpmkg/
Widespread occurrence of dissolved oxygen anomalies, aerobic microbes, and oxygen-producing metabolic pathways in apparently anoxic environments
Authors: Ruff S Emil , Schwab Laura , Vidal Emeline , Hemingway Jordon D , Kraft Beate , Murali Ranjani 
Published: September 2024

Abstract
Nearly all molecular oxygen (O2) on Earth is produced via oxygenic photosynthesis by plants or photosynthetically active microorganisms. Light-independent O2 production, which occurs both abiotically, e.g. through water radiolysis, or biotically, e.g. through the dismutation of nitric oxide or chlorite, has been thought to be negligible to the Earth system. However, recent work indicates that O2 is produced and consumed in dark and apparently anoxic environments at a much larger scale than assumed. Studies have shown that isotopically light O2 can accumulate in old groundwaters, that strictly aerobic microorganisms are present in many apparently anoxic habitats, and that microbes and metabolisms that can produce O2 without light are widespread and abundant in diverse ecosystems. Analysis of published metagenomic data reveals that the enzyme putatively capable of nitric oxide dismutation forms four major phylogenetic clusters and occurs in at least 16 bacterial phyla, most notably the Bacteroidota. Similarly, a re-analysis of published isotopic signatures of dissolved O2 in groundwater suggests in situ production in up to half of the studied environments. Geochemical and microbiological data support the conclusion that “dark oxygen production" is an important and widespread yet overlooked process in apparently anoxic environments with far-reaching implications for subsurface biogeochemistry and ecology.
Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock
Authors: Mello Beatriz , Schrago Carlos G 
Published: September 2024

Abstract
Relaxing the molecular clock using models of how substitution rates change across lineages has become essential for addressing evolutionary problems. The diversity of rate evolution models and their implementations are substantial, and studies have demonstrated their impact on divergence time estimates can be as significant as that of calibration information. In this review, we trace the development of rate evolution models from the proposal of the molecular clock concept to the development of sophisticated Bayesian and non-Bayesian methods that handle rate variation in phylogenies. We discuss the various approaches to modeling rate evolution, provide a comprehensive list of available software, and examine the challenges and advancements of the prevalent Bayesian framework, contrasting them to faster non-Bayesian methods. Lastly, we offer insights into potential advancements in the field in the era of big data.
Examining the Effects of Environment, Geography, and Elevation on Patterns of DNA Methylation Across Populations of Two Widespread Bumble Bee Species
Authors: Heraghty Sam D , Rahman Sarthok Rasique , Verble Kelton M , Lozier Jeffrey D 
Published: September 2024

Abstract
Understanding the myriad avenues through which spatial and environmental factors shape evolution is a major focus in biological research. From a molecular perspective, much work has been focused on genomic sequence variation; however, recently there has been increased interest in how epigenetic variation may be shaped by different variables across the landscape. DNA methylation has been of particular interest given that it is dynamic and can alter gene expression, potentially offering a path for a rapid response to environmental change. We utilized whole genome enzymatic methyl sequencing to evaluate the distribution of CpG methylation across the genome and to analyze patterns of spatial and environmental association in the methylomes of two broadly distributed montane bumble bees (Bombus vancouverensis Cresson and Bombus vosnesenskii Radoszkowski) across elevational gradients in the western US. Methylation patterns in both species are similar at the genomic scale with ∼1% of CpGs being methylated and most methylation being found in exons. At the landscape scale, neither species exhibited strong spatial or population structuring in patterns of methylation, although some weak relationships between methylation and distance or environmental variables were detected. Differential methylation analysis suggests a stronger environment association in B. vancouverensis given the larger number of differentially methylated CpG's compared to B. vosnesenskii. We also observed only a handful of genes with both differentially methylated CpGs and previously detected environmentally associated outlier SNPs. Overall results reveal a weak but present pattern in variation in methylation over the landscape in both species.
Identifying Clinically Meaningful Muscle Power Enhancements and Their Functional Correlates in Hospitalized Older Patients
Authors: Cadore Eduardo L , Izquierdo Mikel , Martínez-Velilla Nicolás , Blanco-Rambo Eduarda , Zambom-Ferraresi Fabricio , Sáez de Asteasu Mikel L 
Published: September 2024

Abstract

Background
This study aimed to determine the threshold of muscle power and strength enhancements that lead to functional gains after exercise intervention in an acute care unit.

Methods
A total of 302 older patients (intervention: 169, control: 133) from 2 randomized clinical trials were included (mean age: 86.7 years). We measured maximal strength (1RM) and muscle power via a velocity transducer during leg press exercise at 30% and 60% of 1RM. A multicomponent exercise program, including power training, balance, and gait exercises performed over 3 to 6 consecutive days, served as the intervention. We used an anchor-based method to correlate muscle function increases with the Short Physical Performance Battery (SPPB) and gait velocity (GVT) to define clinically meaningful improvements.

Results
In the intervention group, marked differences were found in maximal power at 30% of 1RM between SPPB responders and nonresponders (relative 83.5% vs 34.8%; absolute 33.0 vs 12.8 W; p &lt; .05) and at 60% of 1RM (relative 61.1% vs 22.4%; p &lt; .05). GVT responders demonstrated significantly greater improvements in both relative and absolute maximal power than nonresponders at both 30% and 60% of 1RM (p &lt; .05), as well as greater absolute 1RM gains (21.2 vs 15.2 kg, p &lt; .05). Clinically meaningful improvements for muscle power based on SPPB and GVT ranged from 30.2% to 48.7%, whereas for 1RM, it was 8.2% based on GVT.

Conclusions
Muscle power gains were most notable in patients with improvements in the SPPB and GVT, highlighting the critical role of muscle power in functional recovery in these patients.
Uncertainty quantification in electrical resistivity tomography inversion: hybridizing block-wise bootstrapping with geostatistics
Authors: Khabaz Zahra Tafaghod , Ghanati Reza , Bérubé Charles L 
Published: September 2024

SUMMARY
Electrical resistivity tomography inversion often encounters uncertainty stemming from two primary sources: epistemic uncertainty, arising from imperfect underlying physics and improper initial approximation of model parameters, and aleatory variability in observations due to measurement errors. Despite the widespread application of electrical resistivity tomography in imaging, the resistivity distribution of subsurface structures for various hydro-geophysical and engineering purposes, the assessment of uncertainty is seldom addressed within the inverted resistivity tomograms. To explore the combined impact of epistemic and aleatory uncertainty on resistivity models, we initially perturb the observed data using non-parametric block-wise bootstrap resampling with an optimal choice of the block size, generating different realizations of the field data. Subsequently, a geostatistical method is applied to stochastically generate a set of initial models for each bootstrapped data set from the previous step. Finally, we employ a globally convergent homotopic continuation method on each bootstrapped data set and initial model realization to explore the posterior resistivity models. Uncertainty information about the inversion results is provided through posterior statistical analysis. Our algorithm’s simplicity enables easy integration with existing gradient-based inversion methods, requiring only minor modifications. We demonstrate the versatility of our approach through its application to various synthetic and real electrical resistivity tomography experiments. The results reveal that this approach for quantifying uncertainty is straightforward to implement and computationally efficient.
Addressing magnetometer inconsistencies in drone-borne total field gradiometer surveys: case study from Rånbogen, Arctic Norway
Authors: Petersen Jacob Thejll , Døssing Arne 
Published: September 2024

SUMMARY
Inconsistencies between (lightweight optical pumped) magnetometers in total field gradiometer surveys can introduce instrument-dependent responses, complicating geological interpretation. Here, we present an equivalent source-based processing method that combines equivalent sources with kernel regression to eliminate these instrument-dependent responses. We evaluate the method using a drone-borne triangular gradiometer survey from Rånbogen, Arctic Norway. The Rånbogen survey exhibits instrument-dependent responses in the measured total field differences, likely resulting from differences in the magnetometer’s heading error and the complexities from the induced field from the system itself. A synthetic gradiometer survey was generated to test the method, where different nonlinear synthetic heading errors were added to the synthetic magnetometer readings. The equivalent source-based processing method effectively removed these heading errors while constructing an equivalent source model that fits the actual underlying synthetic model. Furthermore, applying the method to the Rånbogen survey removes the data’s correlation to its orientation, thereby minimizing the effect of the instrument-dependent response. The method condenses the gradiometer data to a single equivalent source surface where the derivatives of the total field can be calculated to facilitate interpretation. The results suggest that the equivalent source-based method removes the inconsistencies between the magnetometers in a total field gradiometer survey, allowing for drone-borne gradiometer systems with multiple lightweight magnetometers.
The Genome Architecture of the Copepod Eurytemora carolleeae — the Highly Invasive Atlantic Clade of the Eurytemora affinis Species Complex
Authors: Du Zhenyong , Gelembiuk Gregory , Moss Wynne , Tritt Andrew , Lee Carol Eunmi 
Published: September 2024

Abstract
Copepods are among the most abundant organisms on the planet and play critical functions in aquatic ecosystems. Among copepods, populations of the Eurytemora affinis species complex are numerically dominant in many coastal habitats and serve as food sources for major fisheries. Intriguingly, certain populations possess the unusual capacity to invade novel salinities on rapid time scales. Despite their ecological importance, high-quality genomic resources have been absent for calanoid copepods, limiting our ability to comprehensively dissect the genome architecture underlying the highly invasive and adaptive capacity of certain populations. Here, we present the first chromosome-level genome of a calanoid copepod, from the Atlantic clade (Eurytemora carolleeae) of the E. affinis species complex. This genome was assembled using high-coverage PacBio long-read and Hi-C sequences of an inbred line, generated through 30 generations of full-sib mating. This genome, consisting of 529.3 Mb (contig N50 = 4.2 Mb, scaffold N50 = 140.6 Mb), was anchored onto four chromosomes. Genome annotation predicted 20,262 protein-coding genes, of which ion transport-related gene families were substantially expanded based on comparative analyses of 12 additional arthropod genomes. Also, we found genome-wide signatures of historical gene body methylation of the ion transport-related genes and the significant clustering of these genes on each chromosome. This genome represents one of the most contiguous copepod genomes to date and is among the highest quality marine invertebrate genomes. As such, this genome provides an invaluable resource to help yield fundamental insights into the ability of this copepod to adapt to rapidly changing environments.
Assessing the feasibility and likelihood of policy options to lower specialty drug costs
Authors: Taylor Erin A , Khodyakov Dmitry , Predmore Zachary , Buttorff Christine , Kim Alice 
Published: September 2024

Abstract
Specialty drugs are high-cost medications often used to treat complex chronic conditions. Even with insurance coverage, patients may face very high out-of-pocket costs, which in turn may restrict access. While the Inflation Reduction Act of 2022 included policies designed to reduce specialty drug costs, relatively few policies have been enacted during the past decade. In 2022-2023, we conducted a scoping literature review to identify a range of policy options and selected a set of 9 that have been regularly discussed or recently considered to present to an expert stakeholder panel to seek consensus on (1) the feasibility of implementing each policy and (2) its likely impact on drug costs. Experts rated only 1 policy highly on both feasibility and impact: grouping originator biologics and biosimilars under the same Medicare Part B reimbursement code. They rated 3 policies focused on setting payment limits as likely to have positive (downward) impact on costs but of uncertain feasibility. They considered 4 policies as uncertain on both criteria. Experts rated capping monthly out-of-pocket costs as feasible but unlikely to reduce specialty drug costs. Based on these results, we offer 4 recommendations to policymakers considering ways to reduce specialty drug costs.
Venous obstruction of the upper extremity caused by subclavian vein valve hypertrophy
Authors: Liao Jianyu , Wu Zhoupeng 
Published: September 2024

Abstract
This case report detailed a rare case of upper limb venous outflow obstruction due to primary subclavian vein valve hypertrophy in a 26-year-old male. Misdiagnosed initially, the condition was identified through advanced Doppler ultrasound and Computed Tomography Venography (CTV) Endovascular treatment using Wallstent stents effectively resolved the obstruction, preserving collateral venous circulations. This case highlights the importance of combined colour Doppler ultrasonography for diagnosing unusual venous obstructions and demonstrates the potential of endovascular treatments in managing rare venous conditions.
A novel approach to estimate building electric power consumption based on machine learning method: toward net-zero energy, low carbon and smart buildings
Authors: Alotaibi Badr Saad , Abuhussain Mohammed Awad , Dodo Yakubu Aminu , Al-Tamimi Nedhal , Maghrabi Ammar , Ojobo Henry , Naibi Ahmad Usman , Benti Natei Ermias 
Published: September 2024

Abstract
The modern era has witnessed a surge in energy consumption and its dependence on fossil fuels, which are harmful to the environment, prompting researchers to examine techniques for regulating energy usage in buildings, specifically with regard to residential electricity consumption. The pursuit of net-zero energy consumption and low carbon emission buildings is a significant undertaking that nations across the globe are actively endeavoring to accomplish. In order to accomplish this goal, the structure in question must efficiently oversee its overall energy usage while concurrently capitalizing on sustainable energy sources. The precise estimation of future electricity usage in buildings is an essential element in the process of energy efficiency planning and optimization. The present study introduces a soft computing methodology and data decomposition as approaches for evaluating the energy usage of residential structures. An innovative machine learning approach is introduced for the purpose of estimating the initial cost required to construct a green structure that consumes no net energy. By utilizing wavelet decomposition, it is possible to determine how to transform the structure into one that is intelligent and energy efficient. Following wavelet parallel converter analysis, the data were processed with an estimator model based on an ideal neural network. The results indicate that the mean estimation errors for recurrent neural network, Autoregressive fractionally integrated moving average (ARFIMA), and gene expression programming (GEP) were reduced by 72%, 65%, and 77%, respectively, using this method. Conversely, when the proposed methodology is applied to the smart management of building energy consumption, the examined structures experience an average reduction of 8% in energy consumption. Moreover, the outcomes of CO2 gas emissions demonstrate that the suggested model possesses the capability to accurately forecast CO2 emissions. The study highlights the necessity of employing innovative techniques such as machine learning to decrease building energy usage and CO2 emissions. The discovery of these results can assist policymakers and stakeholders in the energy sector in advancing the adoption of smart building technologies.
A novel optimization approach for the design of a hybrid energy system based on a modified version of a subtraction-average-based optimizing method
Authors: Wu Naixin , Sun Ning , Razmjooy Saeid 
Published: September 2024

Abstract
A critical challenge lies in developing an energy-efficient and eco-friendly power supply system. Despite the enhanced energy efficiency offered by combined cooling, heating, and power (CCHP) systems, optimizing them poses challenges due to conflicting goals like reducing fuel consumption and carbon dioxide emissions while maximizing cost savings. To address these issues, this research suggests a solution that merges a modified subtraction-average-based optimizer with a multiobjective optimization strategy. This proposed framework attains a superior equilibrium among competing objectives compared to three existing optimization algorithms. It leads to a 12% decrease in fuel consumption, a 15% drop in carbon dioxide emissions, and a 10% cost reduction for shopping center proprietors. Moreover, the optimized CCHP system outperforms a stand-alone production system and a nonoptimized CCHP system, yielding 20% and 15% fuel savings annually, respectively. By offering a more comprehensive and balanced approach to CCHP system optimization, the proposed framework contributes to the progression of energy system optimizer, fostering the creation of more sustainable and environmentally friendly energy systems of shopping centers.
Research on energy-saving guidance strategies in different interactions periods of heterogeneous subject in shared residential spaces
Authors: Liu Xiaodan , Tang Zihao , Li Ruitian , Liao Ling , Li Danyao , Lin Yi , Hu Wei 
Published: September 2024

Abstract
Guiding residents in shared residential spaces to implement energy-saving behaviors contributes to improving building energy efficiency and promoting the achievement of China's "dual carbon" goals. The key lies in formulating reasonable and effective energy-saving guidance strategies. In shared residential spaces, there are heterogeneous subjects with different energy consumption behaviors. Due to the varying energy demands during different interaction periods, significant differences exist in the required energy-saving guidance strategies. Therefore, based on Maslow's hierarchy of needs theory, this article proposes the guiding logic of energy-saving strategies in shared residential spaces. Subsequently, taking student dormitories as an example, empirical data is obtained through questionnaire surveys. Social network analysis is then used to analyze the interrelationships among various elements of energy-saving guidance strategies. The results indicate that during the initial stage of subject interaction, the implementation of energy-saving behaviors by heterogeneous subjects results from the synergistic effects of five dimensions: social needs, environmental protection needs, comfort needs, economic needs, and effort. During the stable stage of subject relationships, the elements of economic needs and comfort needs play a key role, but they also rely on the support of elements from environmental protection needs, social needs, and effort dimensions. During the dissolution stage of subject relationships, the elements of social needs and comfort needs play a key role. However, they also rely on the support of elements from economic needs, environmental protection needs, and effort dimensions.