bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations of severe climate conditions and race/ethnic-specific ischemic heart disease mortality among middle-aged adults in the United States
Authors: Majeed Haris , Majeed Hamnah , Moss Emmanuel , Cecere Renzo , Wong Evan G 
Published: December 2024

Abstract
Ischemic heart disease (IHD) is the leading cause of death worldwide. In the United States, IHD deaths affect millions of adults, with substantial age and race/ethnic-specific variability. In recent years, emphasis has been placed on reducing the rate of IHD events among middle-aged adults. Non-Hispanic (NH) Black populations are known to have greater IHD mortality rates compared to other races/ethnicities. Researchers have established several biological, clinical, and socioeconomic IHD risk factors, but severe climate conditions have not been explored by race/ethnicity among middle-aged adults. Using generalized linear models, this study documents associations between the Palmer Drought Severity Index and race/ethnic-specific IHD mortality rates from January 1999 to December 2020 among middle-aged adults across four census regions of the United States. When controlling for covariates, we found that during months of severe droughts (in comparison to neutral phases) IHD mortality rates had an increased risk for NH Whites (RR 1.017, P = 0.017) and NH Blacks (RR 1.029, P = 0.015). Furthermore, we found that surface air temperature is a modifier, where during warm periods (⩾20 °C) throughout the United States, severe drought months exacerbated the risk of IHD mortality rates among NH White (RR 1.024, P = 0.007) and NH Blacks (RR 1.033, P = 0.039). Further studies are needed to understand the mechanism between severe climate conditions and race/ethnic-specific IHD events.
Modeling the impact of electric vehicle adoption and charging strategies in ERCOT
Authors: Murillo Sara , Potts Jerry , Castellanos Sergio 
Published: December 2024

Abstract
Electric vehicle (EV) deployment provides an opportunity to reduce emissions in Texas, which has the greatest transportation emissions in the United States. This study aims to evaluate the impact of light-duty EV (LDEV) adoption through a capacity expansion model of the Texas power grid (Electric Reliability Council of Texas, ERCOT). Particularly, this work investigates how various LDEV adoption rates and charging strategies (i.e., managed and unmanaged) affect the electricity generation capacity mix, hourly dispatch, transmission capacity, carbon emissions, and costs in ERCOT. Our findings show that unmanaged charging strategies can lead to 30%–70% cumulative tailpipe CO2 emission reductions in the power and transportation sectors by 2050, depending on whether the LDEV adoption is 50% or 100%, respectively. These CO2 reductions are associated with 2.5%–6.7% increases in total power system costs compared to a scenario with no EV adoption. Furthermore, we find that managed charging can lead to 10% less transmission capacity compared to unmanaged charging in the complete LDV electrification scenarios. These results indicate that policies incentivizing EV adoption and managed charging strategies can facilitate sustainable decarbonization in the transportation sector while minimally increasing costs relative to a scenario without increased EV adoption.
How many L2 word meanings can learners recall? A latent trait approach to vocabulary size estimation
Authors: Hamada Akira , Hoshino Yuko , Kojima Masumi , Aizawa Kazumi , Iso Tatsuo , Kobayashi Yuichiro 
Published: December 2024

Abstract
This study investigated the size of meaning-recall vocabulary knowledge and its variations across word-frequency and second language (L2) proficiency levels. Despite the extensive research history in determining lexical coverage and thresholds for effective L2 use, much of the evidence is based on measures of meaning-recognition vocabulary knowledge. Using a latent-trait approach, we calculated the probability that learners could recall the meaning of each word in a vocabulary size test. Differences in vocabulary size between recognition and recall formats were analyzed across word-frequency bands and L2 proficiency levels. Results indicate that most learners struggle to recall the meanings of word families beyond the second-most frequent band. Moreover, the gap between meaning-recall and meaning-recognition vocabulary sizes was substantial and expanded as word-frequency and L2 proficiency levels decreased. These findings contribute to defining realistic L2 vocabulary teaching goals and selecting appropriate teaching methods. Construct-irrelevant measurements lead to a misunderstanding of L2 lexical proficiency, emphasizing the importance of test format selection according to the developmental stages of learners’ vocabulary knowledge.
Reasons and reasoning in arbitral awards
Authors: Thong Eugene , Guo Yvonne 
Published: December 2024

Abstract
While the need for international arbitral awards to be supported by reasons appears self-evident, a closer analysis of relevant treaty and statutory provisions as well as tribunal practice reveals a surprising lack of consensus as to the extent to which arbitral awards should be reasoned. Through an analysis of the factors for and against reasoned awards and the reasons for the evolution towards reasoned awards in international arbitration, this article considers the practical implications of dispensing with such reasons and analyses what standard of reasoning is generally recommended in arbitral awards. It concludes with several broad principles on the recommended standard of reasoning which would enable parties to an arbitration to know the basis upon which a decision was made and allow for the decision’s review by national courts.
CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases
Authors: Liao Junlei , Yi Hongyang , Wang Hao , Yang Sumei , Jiang Duanmei , Huang Xin , Zhang Mingxia , Shen Jiayin , Lu Hongzhou , Niu Yuanling 
Published: December 2024

Abstract
In the context of the global damage caused by coronavirus disease 2019 (COVID-19) and the emergence of the monkeypox virus (MPXV) outbreak as a public health emergency of international concern, research into methods that can rapidly test potential therapeutics during an outbreak of a new infectious disease is urgently needed. Computational drug discovery is an effective way to solve such problems. The existence of various large open databases has mitigated the time and resource consumption of traditional drug development and improved the speed of drug discovery. However, the diversity of cell lines used in various databases remains limited, and previous drug discovery methods are ineffective for cross-cell prediction. In this study, we propose a correlation-dependent connectivity map (CDCM) to achieve cross-cell predictions of drug similarity. The CDCM mainly identifies drug–drug or disease–drug relationships from the perspective of gene networks by exploring the correlation changes between genes and identifying similarities in the effects of drugs or diseases on gene expression. We validated the CDCM on multiple datasets and found that it performed well for drug identification across cell lines. A comparison with the Connectivity Map revealed that our method was more stable and performed better across different cell lines. In the application of the CDCM to COVID-19 and MPXV data, the predictions of potential therapeutic compounds for COVID-19 were consistent with several previous studies, and most of the predicted drugs were found to be experimentally effective against MPXV. This result confirms the practical value of the CDCM. With the ability to predict across cell lines, the CDCM outperforms the Connectivity Map, and it has wider application prospects and a reduced cost of use.
Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes
Authors: Llaneza-Lago Sergio , Fraser William D , Green Darrell 
Published: December 2024

Abstract
Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer subtyping is based on the analysis of RNA sequencing (RNA-seq) data from patient cohorts using unsupervised machine learning methods such as hierarchical cluster analysis, but these computational approaches disregard the heterogeneous composition of individual cancer samples. Here, we used a more sophisticated unsupervised Bayesian model termed latent process decomposition (LPD), which handles individual cancer sample heterogeneity and deconvolutes the structure of transcriptome data to provide clinically relevant information. The work was performed on the pediatric tumor osteosarcoma, which is a prototypical model for a rare and heterogeneous cancer. The LPD model detected three osteosarcoma subtypes. The subtype with the poorest prognosis was validated using independent patient datasets. This new stratification framework will be important for more accurate diagnostic labeling, expediting precision medicine, and improving clinical trial success. Our results emphasize the importance of using more sophisticated machine learning approaches (and for teaching deep learning and artificial intelligence) for RNA-seq data analysis, which may assist drug targeting and clinical management.
NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data
Authors: Chen Yunlu , Ruan Feng , Wang Ji-Ping 
Published: December 2024

Abstract

Summary
Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv’s competitive statistical performance and superior computational efficiency.

Availability and implementation
NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.
C21ORF2 mutations point towards primary cilia dysfunction in amyotrophic lateral sclerosis
Authors: De Decker Mathias , Zelina Pavol , Moens Thomas G , Beckers Jimmy , Contardo Matilde , Dittlau Katarina Stoklund , Van Schoor Evelien , Ronisz Alicja , Eggermont Kristel , Moisse Matthieu , Chandran Siddharthan , Veldink Jan H , Thal Dietmar Rudolf , Van Den Bosch Ludo , Pasterkamp R Jeroen , Van Damme Philip 
Published: December 2024

Abstract
Progressive loss of motor neurons is the hallmark of the neurodegenerative disease amyotrophic lateral sclerosis (ALS), but the underlying disease mechanisms remain incompletely understood. In this study, we investigate the effects of C21ORF2 mutations, a gene recently linked to ALS, and find that primary cilia are dysfunctional. Human patient-derived mutant C21ORF2 motor neurons have a reduced ciliary frequency and length.
We report that C21ORF2 is located at the basal body of the primary cilium, and mutations associated with ALS alter this localization. Furthermore, we show that a reduction of C21ORF2 levels in cell lines and motor neurons is sufficient to cause fewer primary cilia and reduced cilial length. This ciliary dysfunction leads to defective downstream sonic hedgehog signalling and reduces the expression of cellular retinoic acid binding protein 1 (CRABP1), a protein involved in motor neuron maintenance and survival.
In a compartmentalized co-culture system of motor neurons and muscle cells, these ciliary defects were associated with a reduced ability of neuromuscular junction formation. Interestingly, these cilia defects are seemingly not restricted to C21ORF2 ALS, as we also observed perturbed primary cilia in cultured motor neurons and post-mortem motor cortex from patients with the most common genetic subtype of ALS caused by repeat expansions in the C9ORF72 gene. Finally, overexpression of C21ORF2 in mutant C21ORF2 motor neurons rescued the ciliary frequency and length, CRAPBP1 expression and neuromuscular junction formation, confirming the importance of primary cilia for motor neuron function.
These results point towards primary cilia dysfunction contributing to motor neuron degeneration in ALS and open new avenues for further research and interventions for this as yet untreatable disease.
Influence of self-assembling nonsubstrates on the enzymatic postmodification of peptide supramolecules
Authors: Higuchi Ayato , Wakabayashi Rie , Kawamura Izuru , Goto Masahiro , Kamiya Noriho 
Published: December 2024

Abstract
The enzymatic postmodification of supramolecules with functional motifs is a promising approach for preparing tailor-made bioactive materials. We attempted to improve the enzyme reactivity of the previously developed, self-assembling aromatic peptide amphiphile, Fmoc-L3QG, by incorporating other self-assembling components, Fmoc-Ln (n = 2, 3), without the enzyme recognition moiety. Although microscopic and spectroscopic studies suggested neither the nonsubstrate peptide amphiphile coassembled with Fmoc-L3QG, the presence of the short fibril–forming Fmoc-L2 and long fibril–forming Fmoc-L3 increased and decreased, respectively, the enzymatic reactivity of Fmoc-L3QG. The findings here suggest that the supramolecular morphologies play a role in modulating the enzyme reaction environment.
Post-pubertal effects of the Face Mask Protocol with and without Bite Block appliance in the orthopedic treatment of Class III malocclusion: a comparative evaluation
Authors: Gazzani Francesca , Rosa Marco , Manti Giuseppe , De Razza Francesca Chiara , Pavoni Chiara , Cozza Paola , Lione Roberta 
Published: December 2024

Abstract

Objectives
The purpose of this retrospective study was to compare the dento-skeletal changes observed in growing Class III patients treated with the Face Mask Protocol (FMP) with and without Bite Block (BB).

Materials
Thirty subjects (12 f, 18 m) who underwent FM/BB therapy were compared to a matched group (FM) of 29 patients (15 f, 14 m) treated without BB. All patients were evaluated before treatment (T0), at the end of active treatment (T1), and at a post-pubertal follow-up observation (T2). A control group (CG) of 20 subjects (10 f, 10 m) with untreated Class III disharmony was used for the comparison of post-pubertal changes. Intergroup statistical comparisons were performed with the independent samples t-test (P &lt; .05).

Results
The comparison between treated samples showed a significant improvement of SN^GoGn (FM/BB vs FM, −2.1°), Overbite (FM/BB vs FM, +1.2 mm), and vertical position of lower molars FM/BB vs FM, −3 mm). When compared with the controls, both treated groups revealed a significant improvement of SNA (FM/BB, +1.8°; FM +2.1°), ANB (FM/BB +1.6°, FM +2.4°), gonial angle (FM/BB −4.9°; FM −4°), incisor inclination (Upper, FM/BB +1.7°, FM +2.3°; Lower, FM/BB −2.5°, FM −2.7°), and Overjet (FM/BB +3.2 mm; FM +4 mm). Improvement of SN^GoGn (−2.1°), Overbite (+1.8 mm), and vertical position of lower molars (−3.8 mm) were observed when FM/BB was compared with CG.

Limitations
Limitations are related to the difficulty to recruit a larger contemporary long-term control group due to ethical reasons.

Conclusion
Both FM protocols induced favorable changes in the treatment of Class III malocclusion with a good post-pubertal stability. The BB allows a more efficient control of the vertical skeletal relationship.
CelEst: a unified gene regulatory network for estimating transcription factor activities in C. elegans
Authors: Perez Marcos Francisco 
Published: December 2024

Abstract
Transcription factors (TFs) play a pivotal role in orchestrating critical intricate patterns of gene regulation. Although gene expression is complex, differential expression of hundreds of genes is often due to regulation by just a handful of TFs. Despite extensive efforts to elucidate TF-target regulatory relationships in Caenorhabditis elegans, existing experimental datasets cover distinct subsets of TFs and leave data integration challenging. Here, I introduce CelEst, a unified gene regulatory network designed to estimate the activity of 487 distinct C. elegans TFs—∼58% of the total—from gene expression data. To integrate data from ChIP-seq, DNA-binding motifs, and eY1H screens, optimal processing of each data type was benchmarked against a set of TF perturbation RNA-seq experiments. Moreover, I showcase how leveraging TF motif conservation in target promoters across genomes of related species can distinguish highly informative interactions, a strategy which can be applied to many model organisms. Integrated analyses of data from commonly studied conditions including heat shock, bacterial infection, and sex differences validates CelEst's performance and highlights overlooked TFs that likely play major roles in coordinating the transcriptional response to these conditions. CelEst can infer TF activity on a standard laptop computer within minutes. Furthermore, an R Shiny app with a step-by-step guide is provided for the community to perform rapid analysis with minimal coding required. I anticipate that widespread adoption of CelEsT will significantly enhance the interpretive power of transcriptomic experiments, both present and retrospective, thereby advancing our understanding of gene regulation in C. elegans and beyond.
cfDNA UniFlow: a unified preprocessing pipeline for cell-free DNA data from liquid biopsies
Authors: Röner Sebastian , Burkard Lea , Speicher Michael R , Kircher Martin 
Published: December 2024

Abstract

Background
Cell-free DNA (cfDNA), a broadly applicable biomarker commonly sourced from urine or blood, is extensively used for research and diagnostic applications. In various settings, genetic and epigenetic information is derived from cfDNA. However, a unified framework for its processing is lacking, limiting the universal application of innovative analysis strategies and the joining of data sets.

Findings
Here, we describe cfDNA UniFlow, a unified, standardized, and ready-to-use workflow for processing cfDNA samples. The workflow is written in Snakemake and can be scaled from stand-alone computers to cluster environments. It includes methods for processing raw genome sequencing data as well as specialized approaches for correcting sequencing errors, filtering, and quality control. Sophisticated methods for detecting copy number alterations and estimating and correcting GC-related biases are readily incorporated. Furthermore, it includes methods for extracting, normalizing, and visualizing coverage signals around user-defined regions in case-control settings. Ultimately, all results and metrics are aggregated in a unified report, enabling easy access to a wide variety of information for further research and downstream analysis.

Conclusions
We provide an automated pipeline for processing cell-free DNA sampled from liquid biopsies, including a wide variety of additional functionalities like bias correction and signal extraction. With our focus on scalability and extensibility, we provide a foundation for future cfDNA research and faster clinical applications. The source code and extensive documentation are available on our GitHub repository (https://github.com/kircherlab/cfDNA-UniFlow).
Diaci v3.0: chromosome-level assembly, de novo transcriptome, and manual annotation of Diaphorina citri, insect vector of Huanglongbing
Authors: Shippy Teresa D , Hosmani Prashant S , Flores-Gonzalez Mirella , Mann Marina , Miller Sherry , Weirauch Matthew T , Vosberg Chad , Massimino Crissy , Tank Will , de Oliveira Lucas , Chen Chang , Hoyt Stephanie , Adams Rebekah , Adkins Samuel , Bailey Samuel T , Chen Xiaoting , Davis Nina , DeLaFlor Yesmarie , Espino Michelle , Gervais Kylie , Grace Rebecca , Harper Douglas , Hasan Denisse L , Hoang Maria , Holcomb Rachel , Jernigan Margaryta R , Kemp Melissa , Kennedy Bailey , Kercher Kyle , Klaessan Stefan , Kruse Angela , Licata Sophia , Lu Andrea , Masse Ron , Mathew Anuja , Michels Sarah , Michels Elizabeth , Neiman Alan , Norman Seantel , Norus Jordan , Ortiz Yasmin , Panitz Naftali , Paris Thomson , Perentesis Kitty M R , Perry Michael , Reynolds Max , Sena Madison M , Tamayo Blessy , Thate Amanda , Vandervoort Sara , Ventura Jessica , Weis Nicholas , Wise Tanner , Shatters Robert G , Heck Michelle , Benoit Joshua B , Hunter Wayne B , Mueller Lukas A , Brown Susan J , D'Elia Tom , Saha Surya 
Published: December 2024

Abstract

Background
Diaphorina citri is an insect vector of “Candidatus Liberibacter asiaticus” (CLas), the gram-negative bacterial pathogen associated with citrus greening disease. Control measures rely on pesticides with negative impacts on the environment, natural ecosystems, and human and animal health. In contrast, gene-targeting methods have the potential to specifically target the vector species and/or reduce pathogen transmission.

Results
To improve the genomic resources needed for targeted pest control, we assembled a D. citri genome based on PacBio long reads followed by proximity ligation-based scaffolding. The 474-Mb genome has 13 chromosomal-length scaffolds. In total, 1,036 genes were manually curated as part of a community annotation project, composed primarily of undergraduate students. We also computationally identified a total of 1,015 putative transcription factors (TFs) and were able to infer motifs for 337 TFs (33%). In addition, we produced a genome-independent transcriptome and genomes for D. citri endosymbionts.

Conclusions
Manual annotation provided more accurate gene models for use by researchers and provided an excellent training opportunity for students from multiple institutions. All resources are available on CitrusGreening.org and NCBI. The chromosomal-length D. citri genome assembly serves as a blueprint for the development of collaborative genomics projects for other medically and agriculturally significant insect vectors.
Parsimonious Green function data bases for global centroid moment tensor inversions
Authors: Sawade Lucas , Ekström Göran , Ding Liang , Nettles Meredith , Tromp Jeroen 
Published: December 2024

SUMMARY
The calculation of synthetic seismograms for global centroid moment tensor (GCMT) inversions relies on advanced 3-D Earth models. However, use of the path-average approximation for mode summation and surface-wave ray theory limits the method’s accuracy. This can cause incorrect predictions of ground motion amplitude and polarization, and other unaccounted-for effects, which can bias the estimated earthquake parameters. To address this issue, we have developed a new and efficient way to calculate, store and access high-fidelity, long-period synthetic seismograms for state-of-the-art 3-D tomographic Earth models. We adapted the spectral-element wave-equation solver SPECFEM3D_GLOBE to generate a data base of Green functions on a global, sparse spectral-element grid of hypocenters for a large set of 180 station locations, using source–receiver reciprocity to speed up the calculation. The seismograms are organized and stored in a format that facilitates rapid access to a particular source region and stations of the Global Seismographic Network. Seismograms for any centroid location can be calculated efficiently via spatial interpolation without losing accuracy compared to full forward calculation. As a proof-of-concept, we perform $\sim$9000 CMT inversions using the Sawade et al. approach, with GCMT solutions as starting models and without restriction on the number of iterations. Although the location updates are consistent with Sawade et al., we find a reduction in non-double-couple components in all types of events except for shallow strike-slip events. Given these encouraging results for future routine implementation, we present a first test and an outlook for routine 3-D GCMT analysis.