bluuebunny/crossref_metadata_2025_split huggingface dataset:
Misidentification of short GRBs as magnetars in nearby galaxies
Authors: Schösser Elisa C , Burgess J Michael , Greiner Jochen 
Published: January 2025

ABSTRACT
GRB 200415A, a short and very bright pulse of $\gamma$-rays, has been claimed to be an extragalactic magnetar giant flare (MGF) in the galaxy ${\mathrm{NGC}\, 253}$. However, as the redshift of the transient was not measured, it is possible that the measured location of the transient on the celestial sphere and the location of the local galaxy merely coincided. Thus, its real progenitor could have been arbitrarily far away, leading to a much larger luminosity of the transient, and leaving the standard model of short gamma-ray bursts (sGRBs), the merger of two compact objects, as an explanation for the observations. Prior to that event, three other sGRBs were proposed to be extragalactic MGFs, which, however, had a substantially wider localization error box. A very recent example is GRB 231115A with its error circle projecting on to M82. We compute the probability for the misinterpretation of sGRBs as extragalactic MGFs by simulating synthetic surveys of sGRB observations. Applying five criteria – incorporating factors such as host galaxy star formation rate, gamma-ray burst monitor trigger, GRB duration, isotropic energy release, and fluence – we identify candidates within the simulated surveys that mimic MGF characteristics. In a non-negligible fraction, 12.5 per cent, of the simulated surveys, we identify at least one sGRB with same characteristics as an MGF. Thus, we conclude that the selection criteria proposed in previous works for the identification of extragalactic MGFs, are not sufficient to reject the sGRB hypothesis. However, our analysis cannot prove that a specific one of the previously proposed extragalactic MGFs is an sGRB.
The VMC Survey – LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm
Authors: Pennock Clara M , van Loon Jacco Th , Cioni Maria-Rosa L , Maitra Chandreyee , Oliveira Joana M , Craig Jessica E M , Ivanov Valentin D , Aird James , Anih Joy O , Cross Nicholas J G , Dresbach Francesca , de Grijs Richard , Groenewegen Martin A T 
Published: January 2025

Abstract
We used a supervised machine learning algorithm (probabilistic random forest) to classify ∼130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ∼79% (SMC) and ∼87% (LMC). Restricting to the 56,696,719 sources with class probabilities (Pclass) &gt; 80% yields accuracies of ∼90% (SMC) and ∼98% (LMC). After removing sources classed as ‘Unknown’, we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including &gt;77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour–colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN–galaxy proportions vary substantially with radio flux density. We have found: &gt;49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; &gt;26,500 new galaxy candidates and &gt;2800 new Young Stellar Object (YSO) candidates.
Distinct structural and functional heterochromatin partitioning of lamin B1 and lamin B2 revealed using genome-wide nicking enzyme epitope targeted DNA sequencing
Authors: Sen Sagnik , Estève Pierre-Olivier , Raman Karthikeyan , Beaulieu Julie , Chin Hang Gyeong , Feehery George R , Vishnu Udayakumar S , Xu Shuang-yong , Samuelson James C , Pradhan Sriharsa 
Published: January 2025

Abstract
Gene expression is regulated by chromatin DNA methylation and other features, including histone post-translational modifications (PTMs), chromatin remodelers and transcription factor occupancy. A complete understanding of gene regulation will require the mapping of these chromatin features in small cell number samples. Here we describe a novel genome-wide chromatin profiling technology, named as Nicking Enzyme Epitope targeted DNA sequencing (NEED-seq). NEED-seq offers antibody-targeted controlled nicking by Nt.CviPII-pGL fusion to study specific protein–DNA complexes in formaldehyde fixed cells, allowing for both visual and genomic resolution of epitope bound chromatin. When applied to nuclei, NEED-seq yielded genome-wide profile of chromatin-associated proteins and histone PTMs. Additionally, NEED-seq of lamin B1 and B2 demonstrated their association with heterochromatin. Lamin B1- and B2-associated domains (LAD) segregated to three different states, and states with stronger LAD correlated with heterochromatic marks. Hi-C analysis displayed A and B compartment with equal lamin B1 and B2 distribution, although methylated DNA remained high in B compartment. LAD clustering with Hi-C resulted in subcompartments, with lamin B1 and B2 partitioning to facultative and constitutive heterochromatin, respectively, and were associated with neuronal development. Thus, lamin B1 and B2 show structural and functional partitioning in mammalian nucleus.
Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients
Authors: Teele Sarah A. , Gazit Avihu Z. , Futterman Craig , La Cava William G. , Cooper David S. , Schwartz Steven M. , Salvin Joshua W. 
Published: January 2025

BACKGROUND:
Accurate assessment of oxygen delivery relative to oxygen demand is crucial in the care of a critically ill patient. The central venous oxygen saturation (Svo
2) enables an estimate of cardiac output yet obtaining these clinical data requires invasive procedures and repeated blood sampling. Interpretation remains subjective and vulnerable to error. Recognition of patient’s evolving clinical status as well as the impact of therapeutic interventions may be delayed.

OBJECTIVE:
The predictive analytics algorithm, inadequate delivery of oxygen (IDo
2) index, was developed to noninvasively estimate the probability of a patient’s Svo
2 to fall below a preselected threshold.

DERIVATION COHORT:
A retrospective multicenter cohort study was conducted using data temporally independent from the design and development phase of the IDo
2 index.

VALIDATION COHORT:
A total of 20,424 Svo
2 measurements from 3,018 critically ill neonates, infants, and children were retrospectively analyzed. Collected data included vital signs, ventilator data, laboratory data, and demographics.

PREDICTION MODEL:
The ability of the IDo
2 index to predict Svo
2 below a preselected threshold (30%, 40%, or 50%) was evaluated for discriminatory power, range utilization, and robustness.

RESULTS:
Area under the receiver operating characteristic curve (AUC) was calculated for each index threshold. Datasets with greater amounts of available data had larger AUC scores. This was observed across each configuration. For the majority of thresholds, Svo
2 values were observed to be significantly lower as the IDo
2 index increased.

CONCLUSIONS:
The IDo
2 index may inform decision-making in pediatric cardiac critical care settings by providing a continuous, noninvasive assessment of oxygen delivery relative to oxygen demand in a specific patient. Leveraging predictive analytics to guide timely patient care, including support for escalation or de-escalation of treatments, may improve care delivery for patients and clinicians.
Compound heterozygous mutations (p.L68R∗37 and p.T241N) lead to abnormal protein levels and structures in hereditary FVII deficiency
Authors: Luo Bingqing , Wu Xia , Zhu Jing , Chen Shu , Lou Shifeng , Tan Xiaoyan 
Published: January 2025

Background
Congenital factor VII (FVII) deficiency is a genetic disorder characterized by decreased FVII activity, which sometimes leads to fatal bleeding. Numerous variants have been found in FVII deficiency, but mutations vary among patients. Each mutation deserves further exploration for each patient at risk of bleeding. We previously reported a Chinese patient with p.L68R∗37 and p.T241N compound heterozygous mutations. In this study, we further investigated the impact of these two mutations on the FVII expression through in vitro expression experiments.

Methods
Mutations were introduced into the FVII coding region using site-directed mutagenesis, and recombinant FVII was combined with two different plasmids, and then quantitative PCR and western blot analyses were performed subsequently.

Results
The p.L68R∗37 mutation had no effect on mRNA levels but caused a significant decrease in protein levels. In the p.T241N mutant vector, mRNA levels did not show a noticeable decrease, but protein levels exhibited a slight decrease. Structural analysis revealed that the p.T241N mutation resulted in an altered secondary structure and protein instability, indicating impaired functional properties.

Conclusion
Our study demonstrated that the p.L68R∗37 and p.T241N mutations impacted the protein levels and function of FVII, ultimately leading to a severe reduction in FVII activity. This study may contribute to further understanding of the molecular pathogenesis of FVII deficiency and offer insights for genetic counseling.
Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation
Authors: Chen Jie , Yan Alice Shijia 
Published: January 2025

Objective:
To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation.

Background:
AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI’s impact on health equity.

Methods:
We used linked datasets from the 2022 American Hospital Association Annual Survey and the 2023 American Hospital Association Information Technology Supplement. The data were further linked to the 2022 Area Deprivation Index (ADI) for each hospital’s service area. State fixed-effect regressions were employed. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher versus lower ADI areas.

Results:
Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to apply ML or other predictive models (coef = −0.10, P = 0.01) and provided fewer AI/ML-related workforce applications (coef = -0.40, P = 0.01), compared with those in the least vulnerable areas. Decomposition results showed that our model specifications explained 79% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1–Q3. In addition, Accountable Care Organization affiliation accounted for 12%–25% of differences in AI/ML utilization across various measures.

Conclusions:
The underuse of AI/ML in economically disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care. Our results further indicate that value-based payment models could be strategically used to support AI integration.
Pediatric Health Assessment Tracker: A Quality Improvement Initiative to Obtain Weights Consistently and Appropriately in a Tertiary Pediatric Intensive Care Unit
Authors: Marron Alonso , Totapally Abhinav , Weatherly Allison , De Subhendu , Barber Madeline , Lifsey Allyson , Boyle Katharine 
Published: January 2025

Introduction:
Weight is vital for tracking fluid status and nutrition and assuring patients have accurate dosing weights in the pediatric intensive care unit (PICU). Challenges in acquiring weights in critically ill patients include clinical instability, limited equipment, and lack of appropriate orders in the electronic medical record (EMR).

Methods:
We implemented interventions that targeted EMR weight orders and actual collection of weights in the 42-bed PICU of a children’s hospital. Preintervention data were collected from February to March 2023 for all patients admitted to the PICU with a length of stay (LOS) ≥3 days. We surveyed PICU nurses to identify barriers to collecting weights. Interventions included a multidisciplinary team approach, safety checklist, nursing education, and automatization of weight orders. The study team monitored the number of patients with weight orders and weights obtained as ordered twice weekly from March 2023 to April 2024 using statistical process control charts.

Results:
There were 1728 patient instances of LOS ≥3 days. Preintervention data showed 70.4% of patients with appropriate weight orders and 35.5% with weights obtained. Implementing a safety checklist, nursing education, EMR changes, and automatizing weight orders, the centerline for weight orders shifted to 94.3% and for weights obtained to 69.5%. Reminder emails to all ICU providers and nursing check-ins maintained the centerline. No increase in unplanned extubations occurred.

Conclusions:
Through interventions involving rounding providers, nurses, and the EMR, the frequency of weights ordered and obtained in a busy PICU sustainably increased.
Brain RNA profiling highlights multiple disease pathways in persons with HAND: uncovering determinants of biotype diversity
Authors: Branton William G. , Zhang Na , Cohen Eric A. , Brew Bruce J. , Gill M. John , Gelman Benjamin B. , Kong Linglong , Power Christopher 
Published: January 2025

Objective:
To discover microRNA (miRNA)-RNA transcript interactions dysregulated in brains from persons with HIV-associated neurocognitive disorder (HAND), we investigated RNA expression using machine learning tools.

Design:
Brain-derived host RNA transcript and miRNA expression was examined from persons with or without HAND using bioinformatics platforms.

Methods:
By combining next generation sequencing, droplet digital (dd)PCR quantitation of HIV-1 genomes, with bioinformatics and statistical tools, we investigated differential RNA expression in frontal cortex from persons without HIV (HIV[-]), with HIV without brain disease (HIV[+]), with HIV-associated neurocognitive disorder (HAND), or HAND with encephalitis (HIVE).

Results:
Expression levels for 147 transcripts and 43 miRNAs showed a minimum 4-fold difference between clinical groups with a predominance of antiviral (Type I interferon) signaling-, neural cell maintenance-, and neurodevelopmental disorder-related genes that was validated by gene ontology and molecular pathway inferences. Scale of signal-to-noise ratio (SSNR) and biweight midcorrelation (bicor) analyses identified 14 miRNAs and 45 RNA transcripts, which were highly correlated and differentially expressed (p ≤ 0.05). Machine learning applications compared regression models predicated on HIV-1 DNA, or RNA viral quantities that disclosed miR-4683 and miR-154-5p were dominant variables associated with differential expression of host RNAs. These miRNAs were also associated with antiviral-, cell maintenance-, and neurodevelopmental disorder-related genes.

Conclusions:
Antiviral as well as neurodevelopmental disorder-related pathways in brain were associated with HAND, based on correlated RNA transcripts and miRNAs. Integrated molecular methods with machine learning offer insights into disease mechanisms, underpinning brain-related biotypes among persons with HIV that could direct clinical care.
Analysis of HIV transmission characteristics and intervention effects in Guangxi based on molecular networks
Authors: Pang Xianwu , Ma Jie , He Qin , Tang Kailing , Huang Jinghua , Fang Ningye , Xie Haoming , Lan Guanghua , Liang Shujia 
Published: January 2025

Objective:
This study evaluates changes in HIV transmission and the effectiveness of interventions after two rounds of the Guangxi AIDS Conquering Project (GACP) in Guangxi, China.

Methods:
Samples and epidemiological data from newly diagnosed people living with HIV (PLWH) between 2014-2020 were analyzed. Molecular networks were constructed using nested PCR amplification and sequencing of the pol region, and multivariable logistic regression identified factors associated with clustering and high-degree nodes.

Results:
A total of 4,227 valid sequences (73.12% inclusion rate) were analyzed. Demographic changes included an increasing proportion of individuals aged ≥50 years (49.66%), with lower education (50.51%), peasants (76.82%), and heterosexual transmission (90.29%). The overall clustering rate was 86.89%, with higher clustering among individuals aged ≥50 (92.57%), those with primary school or below (89.09%), peasants (88.11%), and CRF08_BC infections (91.48%). Annual declines in cluster growth rate and clustering rates were observed, particularly among individuals aged <30, college graduates, men who have sex with men (MSM), and people who inject drugs (PWID). Key transmission hotspots were identified in Lingshan, particularly among older, less-educated individuals, and peasants. Factors associated with clustering included being male (aOR: 1.27), aged ≥50 (aOR: 3.84), and infected with CRF08_BC (aOR: 2.12). From 2017 to 2020, the risk of clustering and high-degree nodes was lower compared to 2014–2016, suggesting the effectiveness of interventions.

Conclusion:
Interventions in Guangxi effectively reduced HIV transmission among younger, high-degree populations. However, older, less-educated individuals remain at high risk, necessitating targeted strategies to address their specific needs and achieve better HIV control.
A comprehensive study of quantum arithmetic circuits
Authors: Wang Siyi , Li Xiufan , Lee Wei Jie Bryan , Deb Suman , Lim Eugene , Chattopadhyay Anupam 
Published: January 2025

In recent decades, the field of quantum computing has experienced remarkable progress. This progress is marked by the superior performance of many quantum algorithms compared with their classical counterparts, with Shor’s algorithm serving as a prominent illustration. Quantum arithmetic circuits, which are the fundamental building blocks in numerous quantum algorithms, have attracted much attention. Despite extensive exploration of various designs in the existing literature, researchers remain keen to develop novel designs and improve existing ones. In this review article, we aim to provide a systematically organized and easily comprehensible overview of the current state of the art in quantum arithmetic circuits. Specifically, this study covers fundamental operations such as addition, subtraction, multiplication, division and modular exponentiation. We delve into the detailed quantum implementations of these prominent designs and evaluate their efficiency considering various objectives. We also discuss potential applications of the presented arithmetic circuits and suggest future research directions.
This article is part of the theme issue ‘Emerging technologies for future secure computing platforms’.
AxLaM: energy-efficient accelerator design for language models for edge computing
Authors: Glint Tom , Mittal Bhumika , Sharma Santripta , Ronak Abdul Qadir , Goud Abhinav , Kasture Neerja , Momin Zaqi , Krishna Aravind , Mekie Joycee 
Published: January 2025

Modern language models such as bidirectional encoder representations from transformers have revolutionized natural language processing (NLP) tasks but are computationally intensive, limiting their deployment on edge devices. This paper presents an energy-efficient accelerator design tailored for encoder-based language models, enabling their integration into mobile and edge computing environments. A data-flow-aware hardware accelerator design for language models inspired by Simba, makes use of approximate fixed-point POSIT-based multipliers and uses high bandwidth memory (HBM) in achieving significant improvements in computational efficiency, power consumption, area and latency compared to the hardware-realized scalable accelerator Simba. Compared to Simba, AxLaM achieves a ninefold energy reduction, 58% area reduction and 1.2 times improved latency, making it suitable for deployment in edge devices. The energy efficiency of AxLaN is 1.8 TOPS/W, 65% higher than FACT, which requires pre-processing of the language model before implementing it on the hardware.
This article is part of the theme issue ‘Emerging technologies for future secure computing platforms’.
An Interpretable Sparse Graph Contrastive Learning Approach for Identifying Breast Cancer Risk Variants
Authors: Naga Raju Gudhe , Hartikainen Jaana M. , Tengström Maria , Pylkäs Katri , Winqvist Robert , Kosma Veli-Matti , Behravan Hamid , Mannermaa Arto 
Published: January 2025

SUMMARYGenome-wide association studies (GWASs) have identified over 2,400 genetic variants associated to breast cancer. Conventional GWASs methods that analyze variants independently often overlook the complex genetic interactions underlying disease susceptibility. Machine and deep learning approaches present promising alternatives, yet encounter challenges, including overfitting due to high dimensionality (∼10 million variants) and limited sample sizes, as well as limited interpretability. Here, we present GenoGraph, a graph-based contrastive learning framework designed to address these limitations by modeling high-dimensional genetic data in low-sample-size scenarios. We demonstrate GenoGraph’s efficacy in breast cancer case-control classification task, achieving accuracy of 0.96 using the Biobank of Eastern Finland dataset. GenoGraph identified rs11672773 (ZNF8) as a key risk variant in Finnish population, with significant interactions with rs10759243 (KLF4) and rs3803662 (TOX3). Furthermore, in silico validation confirmed the biological relevance of these findings, underscoring GenoGraph’s potential to advance breast cancer risk prediction and elucidate genetic interactions for personalized medicine.