bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emulating inconsistencies in stratospheric aerosol injection
Authors: Farley Jared , MacMartin Douglas G , Visioni Daniele , Kravitz Ben 
Published: May 2024

Abstract
Stratospheric aerosol injection (SAI) would involve the addition of sulfate aerosols in the stratosphere to reflect part of the incoming solar radiation, thereby cooling the climate. Studies trying to explore the impacts of SAI have often focused on idealized scenarios without explicitly introducing what we call ‘inconsistencies’ in a deployment. A concern often discussed is what would happen to the climate system after an abrupt termination of its deployment, whether inadvertent or deliberate. However, there is a much wider range of plausible inconsistencies in deployment than termination that should be evaluated to better understand associated risks. In this work, we simulate a few representative inconsistencies in a pre-existing SAI scenario: an abrupt termination, a decade-long gradual phase-out, and 1 year and 2 year temporary interruptions of deployment. After examining their climate impacts, we use these simulations to train an emulator, and use this to project global mean temperature response for a broader set of inconsistencies in deployment. Our work highlights the capacity of a finite set of explicitly simulated scenarios that include inconsistencies to inform an emulator that is capable of expanding the space of scenarios that one might want to explore far more quickly and efficiently.
Increasing fluctuations in the Arctic summer sea ice cover are expected with future global warming
Authors: Poltronieri Anna , Bochow Nils , Boers Niklas , Rypdal Martin 
Published: May 2024

Abstract
The loss of Arctic sea ice (ASI) represents a major transformation in the Arctic region, impacting regional and global climate, ecosystems, and socio-economic structures. Observational and reanalysis data have consistently shown a notable shift in polar environmental conditions over recent decades, marked by a substantial reduction in the ASI area and a rise in the variability in its coverage and distribution. Utilizing data from the latest Coupled Model Intercomparison Project phase, our study reveals a consistent pattern highlighting a fundamental shift in ASI dynamics preceding total loss. We observe increasing fluctuations in the September ASI area as the threshold for an ice-free Arctic is approached across various scenarios and models. This pattern is particularly concentrated in the Central Arctic (CA) sub-region. Spatial analyses reveal increasing variance along the CA’s northern coastlines, accompanied by a substantial increase in open water coverage, underscoring the shift from stable to highly variable ice conditions in this region. Additionally, our findings suggest a potential link between increased ASI fluctuations and variability in surface wind speeds. These specific results underscore the urgency of multidisciplinary approaches in addressing the challenges posed by ASI variability, with implications for marine ecosystems, Indigenous communities, and navigational safety.
Western Europe’s extreme July 2019 heatwave in a warmer world
Authors: de Vries Hylke , Lenderink Geert , van Meijgaard Erik , van Ulft Bert , de Rooy Wim 
Published: May 2024

Abstract
Summertime heatwaves are extreme events with a large societal impact. Intensity, duration and spatial extent, all heatwave properties are projected to increase in a warming world, implying that summers that qualified as extreme in the past will become increasingly normal. In this paper we quantify how the changes play out for the July 2019 European heatwave that shattered temperature records throughout Western Europe. We combine a storyline approach with ensemble Pseudo Global Warming (PGW) and high-resolution dynamical downscaling. The downscaling is done with a regional climate model (RACMO2, 12 km resolution) and a convection-permitting model (HCLIM-AROME, 2.5 km resolution). Under PGW the maximum temperature during the heatwave rises 1.5 to 2.5 times faster than the global mean, implying that even at moderate warming levels the heatwave impact changes are tangible. Moreover, there is no sign that the increase in the maximum temperature levels off at higher warming levels, implying that at +4K above present-day temperatures could reach 50 ∘C. During heatwaves cities become islands of heat where daily maxima and night-time minima are up to 5 ∘C higher than in rural areas as we show in ultra-high resolution HCLIM-AROME simulations at 150 m resolution.
Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar
Authors: Pritchard Emma , Vihta Karina-Doris , Eyre David W , Hopkins Susan , Peto Tim E A , Matthews Philippa C , Stoesser Nicole , Studley Ruth , Rourke Emma , Diamond Ian , Pouwels Koen B , Walker Ann Sarah , Infection Survey Team COVID-19 
Published: May 2024

Abstract
Detecting and quantifying changes in the growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers’ rationale for implementing or continuing interventions aimed at reducing their impact. Substantial changes in SARS-CoV-2 prevalence with the emergence of variants have provided an opportunity to investigate different methods for doing this. We collected polymerase chain reaction (PCR) results from all participants in the United Kingdom’s COVID-19 Infection Survey between August 1, 2020, and June 30, 2022. Change points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalized additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8 799 079 study visits, 147 278 (1.7%) were PCR-positive. Change points associated with the emergence of major variants were estimated to occur a median of 4 days earlier (IQR, 0-8) when using GAMs versus ISR. When estimating recent change points using successive data periods, 4 change points (4/96) identified by GAMs were not found when adding later data or by ISR. Change points were detected 3-5 weeks after they occurred under both methods but could be detected earlier within specific subgroups. Change points in growth rates of SARS-CoV-2 can be detected in near real time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories, both methods could be used in parallel.
Invited commentary: raising a high-pressure alarm about pediatric hypertension
Authors: Weiss Scott L 
Published: May 2024

Abstract
Hypertension is a common “silent killer” in adult medicine, but epidemiologic estimates of elevated blood pressure in children and adolescents are challenged by under-diagnosis and resultant low utilization of relevant administrative or billing codes. In the article by Horgan et al (Am J Epidemiol 2024), children and adolescents with hypertension and elevated blood pressure were identified using direct assessment of blood pressure measurements available in the electronic health record from both inpatient and outpatient visits (“clinical cohort”) in comparison to diagnosis codes (“claims-based cohort”). The study population included 3.75 million pediatric healthcare visits available in the US Food and Drug Administration’s Sentinel System. While the study applied a relatively novel methodology to interrogate available clinical data within the EHR to better understand the prevalence of pediatric hypertension and raised concern for a higher occurrence of hypertension among children and adolescents than previously realized using claims codes, the utility of the prevalence estimates may be limited by the potential for misclassification bias inherent in electronic health records data. However, these data raise important concerns about relaying solely on ICD-9-CM/ICD-10-CM codes to quantify the epidemiology of pediatric hypertension and highlight opportunities to address elevated blood pressure in children that could improve long-term cardiovascular health.
Mother-reliant or self-reliant: the germination strategy of seeds in a species-rich alpine meadow is associated with the existence of pericarps
Authors: Li Xiao-Qing , Zhu Hong-Yu , He Yong-Deng , Ochola Anne Christine , Qiong La , Yang Chun-Feng 
Published: May 2024

Abstract

Background and Aims
Some plants germinate their seeds enclosed by a pericarp, whereas others lack the outer packaging. As a maternal tissue, the pericarp might impart seeds with different germination strategies. Plants in a community with different flowering times might separately disperse and germinate their seeds; therefore, flowering time can be considered as one manifestation of maternal effects on the offspring. The mass of the seed is another important factor influencing germination and represents the intrinsic resource of the seed that supports germination. Using seeds from a species-rich alpine meadow located in the Hengduan Mountains of China, a global biodiversity hotspot, we aimed to illustrate whether and how the type of seed (with or without a pericarp) modulates the interaction of flowering time and seed mass with germination.

Methods
Seeds were germinated in generally favourable conditions, and the speed of germination [estimated by mean germination time (MGT)] was calculated. We quantified the maternal conditions by separation of flowering time for 67 species in the meadow, of which 31 produced seeds with pericarps and 36 yielded seeds without pericarps. We also weighed 100 seeds of each species to assess their mass.

Key Results
The MGT varied between the two types of seeds. For seeds with pericarps, MGT was associated with flowering time but not with seed mass. Plants with earlier flowering times in the meadow exhibited more rapid seed germination. For seeds without a pericarp, the MGT depended on seed mass, with smaller seeds germinating more rapidly than larger seeds.

Conclusions
The distinct responses of germination to flowering time and seed mass observed in seeds with and without a pericarp suggest that germination strategies might be mother-reliant for seeds protected by pericarps but self-reliant for those without such protection. This new finding improves our understanding of seed germination by integrating ecologically mediated maternal conditions and inherent genetic properties.
Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding
Authors: Gan Yanglan , Yu Jiacheng , Xu Guangwei , Yan Cairong , Zou Guobing 
Published: May 2024

Abstract

Motivation
Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships.

Results
In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets.

Availability and implementation
Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.
MultiRNAflow: integrated analysis of temporal RNA-seq data with multiple biological conditions
Authors: Loubaton Rodolphe , Champagnat Nicolas , Vallois Pierre , Vallat Laurent 
Published: May 2024

Abstract

Motivation
The dynamic transcriptional mechanisms that govern eukaryotic cell function can now be analyzed by RNA sequencing. However, the packages currently available for the analysis of raw sequencing data do not provide automatic analysis of complex experimental designs with multiple biological conditions and multiple analysis time-points.

Results
The MultiRNAflow suite combines several packages in a unified framework allowing exploratory and supervised statistical analyses of temporal data for multiple biological conditions.

Availability and implementation
The R package MultiRNAflow is freely available on Bioconductor (https://bioconductor.org/packages/MultiRNAflow/), and the latest version of the source code is available on a GitHub repository (https://github.com/loubator/MultiRNAflow).
ReactomeGSA: new features to simplify public data reuse
Authors: Grentner Alexander , Ragueneau Eliot , Gong Chuqiao , Prinz Adrian , Gansberger Sabina , Oyarzun Inigo , Hermjakob Henning , Griss Johannes 
Published: May 2024

Abstract

Motivation
ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different ‘omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species.

Results
We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI’s Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA’s web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA.

Availability and implementation
The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.
Representations of lipid nanoparticles using large language models for transfection efficiency prediction
Authors: Moayedpour Saeed , Broadbent Jonathan , Riahi Saleh , Bailey Michael , V. Thu Hoa , Dobchev Dimitar , Balsubramani Akshay , N.D. Santos Ricardo , Kogler-Anele Lorenzo , Corrochano-Navarro Alejandro , Li Sizhen , U. Montoya Fernando , Agarwal Vikram , Bar-Joseph Ziv , Jager Sven 
Published: May 2024

Abstract

Motivation
Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency.

Results
To optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs.

Availability and implementation
Code and data links available at: https://github.com/Sanofi-Public/LipoBART.
FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug–target interaction prediction
Authors: Zhang Qi , Zuo Le , Ren Ying , Wang Siyuan , Wang Wenfa , Ma Lerong , Zhang Jing , Xia Bisheng 
Published: May 2024

Abstract

Motivation
Identifying drug–target interactions (DTI) is crucial in drug discovery. Fragments are less complex and can accurately characterize local features, which is important in DTI prediction. Recently, deep learning (DL)-based methods predict DTI more efficiently. However, two challenges remain in existing DL-based methods: (i) some methods directly encode drugs and proteins into integers, ignoring the substructure representation; (ii) some methods learn the features of the drugs and proteins separately instead of considering their interactions.

Results
In this article, we propose a fragment-oriented method based on a multihead cross attention mechanism for predicting DTI, named FMCA-DTI. FMCA-DTI obtains multiple types of fragments of drugs and proteins by branch chain mining and category fragment mining. Importantly, FMCA-DTI utilizes the shared-weight-based multihead cross attention mechanism to learn the complex interaction features between different fragments. Experiments on three benchmark datasets show that FMCA-DTI achieves significantly improved performance by comparing it with four state-of-the-art baselines.

Availability and implementation
The code for this workflow is available at: https://github.com/jacky102022/FMCA-DTI.
bnglViz: online visualization of rule-based models
Authors: Liguori-Bills Noah , Blinov Michael L 
Published: May 2024

Abstract

Motivation
Rule-based modeling is a powerful method to describe and simulate interactions among multi-site molecules and multi-molecular species, accounting for the internal connectivity of molecules in chemical species. This modeling technique is implemented in BioNetGen software that is used by various tools and software frameworks, such as BioNetGen stand-alone software, NFSim simulation engine, Virtual Cell simulation and modeling framework, SmolDyn and PySB software tools. These tools exchange models using BioNetGen scripting language (BNGL). Until now, there was no online visualization of such rule-based models. Modelers and researchers reading the manuscripts describing rule-based models had to learn BNGL scripting or master one of these tools to understand the models.

Results
Here, we introduce bnglViz, an online platform for visualizing BNGL files as graphical cartoons, empowering researchers to grasp the nuances of rule-based models swiftly and efficiently, and making the exploration of complex biological systems more accessible than ever before. The produced visualizations can be used as supplemental figures in publications or as a way to annotate BNGL models on web repositories.

Availability and implementation
Available at https://bnglviz.github.io/.
Prostate specific membrane antigen PET avidity in a granular cell tumour of the left supraspinatus muscle: a case report
Authors: Hsieh Michael T , Fargah Farokh , Tahir Abdul Rahim Mohd , Le Ngo Tue , Shakespeare Thomas P 
Published: May 2024

Abstract
Granular cell tumour is a rare, mostly benign, soft tissue, neuroectodermal tumour, most commonly seen in the skin and peripheral soft tissue. There are no publications to date of PSMA-PET avidity in a granular cell tumour. In this 60 year old male, staging PSMA-PET for a localized intermediate risk prostate cancer incidentally identified a PSMA-avid left supraspinatus lesion, which was subsequently biopsy-proven as a granular cell tumour. We present the first case of PSMA-avid granular cell tumour and add to the growing literature documenting PSMA-PET avidity in benign and malignant lesions apart from prostate cancer.
The Impact of Social Work Supervision on Interns’ Employment Intention: New Evidence from Mainland China
Authors: Lan Zhensong , Mu Liping , Qiu Xue , Liu Huaqiang , Huang Xuefang 
Published: May 2024

Abstract
Supervision is a key link in social work practice and plays an important role in the cultivation of social work professionals. This study investigated the impact of social work internship supervision on the employment intention of 271 social work interns from five universities in Guangxi, China. The results show that the employment intention of male students is higher than that of female students, and the employment intention of interns with urban household registration is higher than that of interns with rural household registration; internship investment and school–enterprise cooperation have a positive impact on the exertion of the supervision function, but internship investment plays a full mediating role between the supervision function and employment intention, whilst school–enterprise cooperation regulates the relationship between the supervision function and internship investment. Real factors, such as economy and social status, continue to be important factors influencing social work students’ employment intention. When conducting social work internship educational activities, schools should create a favourable internship environment and working conditions for interns and maintain good cooperation with internship organisations. This will facilitate internship supervision and enhance employment intention.
Intraventricular haemorrhage in premature infants: the role of immature neuronal salt and water transport
Authors: Bahari Fatemeh , Dzhala Volodymyr , Balena Trevor , Lillis Kyle P , Staley Kevin J 
Published: May 2024

Abstract
Intraventricular haemorrhage is a common complication of premature birth. Survivors are often left with cerebral palsy, intellectual disability and/or hydrocephalus. Animal models suggest that brain tissue shrinkage, with subsequent vascular stretch and tear, is an important step in the pathophysiology, but the cause of this shrinkage is unknown. Clinical risk factors for intraventricular haemorrhage are biomarkers of hypoxic–ischaemic stress, which causes mature neurons to swell. However, immature neuronal volume might shift in the opposite direction in these conditions. This is because immature neurons express the chloride, salt and water transporter NKCC1, which subserves regulatory volume increases in non-neural cells, whereas mature neurons express KCC2, which subserves regulatory volume decreases. When hypoxic–ischaemic conditions reduce active ion transport and increase the cytoplasmic membrane permeability, the effects of these transporters are diminished. Consequentially, mature neurons swell (cytotoxic oedema), whereas immature neurons might shrink. After hypoxic–ischaemic stress, in vivo and in vitro multi-photon imaging of perinatal transgenic mice demonstrated shrinkage of viable immature neurons, bulk tissue shrinkage and blood vessel displacement. Neuronal shrinkage was correlated with age-dependent membrane salt and water transporter expression using immunohistochemistry. Shrinkage of immature neurons was prevented by prior genetic or pharmacological inhibition of NKCC1 transport. These findings open new avenues of investigation for the detection of acute brain injury by neuroimaging, in addition to prevention of neuronal shrinkage and the ensuing intraventricular haemorrhage, in premature infants.