bluuebunny/crossref_metadata_2025_split huggingface dataset:
The seepage flow state characteristics of SMA-13 asphalt mixture under triaxial fatigue load
Authors: Li Yan , Zhou Zhigang , Li Shuaishuai , Guo Anshun , Guo Shihui 
Published: January 2025

Abstract
The seepage flow state of water in asphalt pavement may not accordant with Darcy law due to the high hydrodynamic pressure generated under high-speed traffic load, In order to study the seepage flow state of water in asphalt pavement under high water pressure conditions and analyze the critical conditions for non Darcy flow to occur, the SMA-13 asphalt mixture with different compaction methods and different percent air voids were prepared, and triaxial fatigue tests and high water pressure seepage tests were conducted on the specimens, hydraulic gradient and critical Reynolds number were used to distinguish Darcy and non Darcy phenomena in the seepage process. The results indicate that the linear flow occurs at low flow velocity in the SMA-13 asphalt mixture with different compaction methods and different percent air voids, it shows obvious nonlinear flow at high flow velocity. The values of linear component coefficient a and nonlinear component coefficient b obtained by nonlinear fitting by using Forchheimer’s law decrease with the increase of percent air voids, and the values of the coefficients of specimens formed by rotary compaction molding are larger than those formed by Marshall compaction molding. The critical flow velocity and critical Reynolds number increase with the increase of percent air voids before and after triaxial fatigue loading with different molding methods.
Multilayer magnetic skyrmion devices for spiking neural networks
Authors: Lone Aijaz H , Rahimi Daniel N , Fariborzi Hossein , Setti Gianluca 
Published: January 2025

Abstract
Spintronic devices -based on magnetic solitons, such as the domain wall motion and the skyrmions, have shown a significant potential for applications in energy-efficient data storage and beyond CMOS computing architectures. Based on the magnetic multilayer hetero-structures, we propose a magnetic skyrmion-magnetic tunnel junction device structure, mimicking leaky integrate and fire LIF neuron characteristics. The device is controlled by spin-orbit torque-SOT driven skyrmion motion in the ferromagnetic thin film. The modified leaky integrate and fire LIF neuron-like characteristics are shown using the combination of SOT and the skyrmion position dependence of the demagnetization energy. The device characteristics are modeled as the modified LIF neuron. The LIF neuron is one of the fundamental spiking neuron models; we integrate the model in the three-layer spiking neural network (SNN) and convolutional CSNN framework to test these spiking neuron models to classify the MNIST and FMNIST datasets. In both architectures, the network achieves classification accuracy above 97.10%. Additionally, the LIF neuron latency is in ns; thus, when integrated with the CMOS, the proposed device structures and associated systems exhibit an excellent future for energy-efficient neuromorphic computing.
Delineating urban flooding when incorporating community stormwater knowledge
Authors: Scolio Madeline , Kremer Peleg , Smith Virginia , Amur Achira , Wadzuk Bridget , Homet Kate , Devlin Eric , Al Mehedi Md Abdullah , Moore Laura 
Published: January 2025

Abstract
Accurately delineating both pluvial and fluvial flood risk is critical to protecting vulnerable populations in urban environments. Although there are currently models and frameworks to estimate stormwater runoff and predict urban flooding, there are often minimal observations to validate results due to the quick retreat of floodwaters from affected areas. In this research, we compare and contrast different methodologies for capturing flood extent in order to highlight the challenges inherent in current methods for urban flooding delineation. This research focuses on two Philadelphia neighborhoods, Manayunk and Eastwick, that face frequent flooding. Overall, Philadelphia, PA is a city with a large proportion of vulnerable populations and is plagued by flooding, with expectations that flood risk will increase as climate change progresses. An array of data, including remotely sensed satellite imagery after major flooding events, Federal Emergency Management Agency’s Special Flood Hazard Areas, First Street Foundation’s Flood Factor, road closures, National Flood Insurance Program claims, and community surveys, were compared for the study areas. Here we show how stakeholder surveys can illuminate the weight of firsthand and communal knowledge on local understandings of stormwater and flood risk. These surveys highlighted different impacts of flooding, depending on the most persistent flood type, pluvial or fluvial, in each area, not present in large datasets. Given the complexity of flooding, there is no single method to fully encompass the impacts on both human well-being and the environment. Through the co-creation of flood risk knowledge, community members are empowered and play a critical role in fostering resilience in their neighborhoods. Community stormwater knowledge is a powerful tool that can be used as a complement to hydrologic flood delineation techniques to overcome common limitations in urban landscapes.
From just transitions to just transformations; observations from Aotearoa New Zealand in addressing the human wellbeing impacts of climate change
Authors: Lenihan-Ikin Isabella , Ariana Proochista , Atuire Caesar 
Published: January 2025

Abstract
This paper interrogates the utility of a just transition framework for climate change and human wellbeing in the context of Aotearoa New Zealand. Drawing on historical conceptualisations and applications of just transitions, their limitations and potential, we argue that an expansive and anti-reductionist conceptualisation of the just transition is best suited to address complex and inter-sectoral issues across time and space. Expansive just transitions are characterised by four features; relationality, systems-thinking, place-based and inter-generational approaches, where emancipation is held as the overarching objective. While we argue in favour of an expansive and anti-reductionist just transition framework being employed in the face of complex issues, such as the human wellbeing impacts of climate change, we introduce the concept of a just transformation: achieving true equity and justice in the face of climate change requires a transformative approach, situated outside of the confines of the hegemonic economic system and linear transitions. Transformations and not transitions are required for the health and wellbeing outcomes desired amidst threats of climate change.
Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation
Authors: Xu Yongjie , Zang Zelin , Hu Bozhen , Yuan Yue , Tan Cheng , Xia Jun , Li Stan Z 
Published: January 2025

Abstract
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches. To address these issues, we propose the Poincaré deep manifold transformation (PoincaréDMT) method, which maps high-dimensional scRNA-seq data to a hyperbolic Poincaré disk. This approach preserves global structure from a graph Laplacian matrix while achieving local structure correction through a structure module combined with data augmentation. Additionally, PoincaréDMT alleviates batch effects by integrating a batch graph that accounts for batch labels into the low-dimensional embeddings during network training. Furthermore, PoincaréDMT introduces the Shapley additive explanations method based on trained model to identify the important marker genes in specific clusters and cell differentiation process. Therefore, PoincaréDMT provides a unified framework for multiple key tasks essential for scRNA-seq analysis, including trajectory inference, pseudotime inference, batch correction, and marker gene selection. We validate PoincaréDMT through extensive evaluations on both simulated and real scRNA-seq datasets, demonstrating its superior performance in preserving global and local data structures compared to existing methods.
Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity
Authors: Huang Wendong , Hu Yaofeng , Wang Lequn , Wu Guangsheng , Zhang Chuanchao , Shi Qianqian 
Published: January 2025

Abstract
Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings. As a novel cross-dimensional transfer learning architecture, SpaGTL aligns spatial graph representations across gene-level graph transformers and cell/spot-level manifold-dominated variational autoencoder. This alignment facilitates the exploration of microenvironmental variations in cell types and functional domains from a molecular regulatory perspective, all within a self-supervised framework. We verified SpaGTL’s precision, robustness, and speed over existing state-of-the-art algorithms and show SpaGTL’s potential that facilitates the discovery of novel regulatory programs that exhibit strong associations with tissue functional regions and cell types. Importantly, SpaGTL could be extended to process multi-slice SRT data and map molecular regulatory landscape associated with three-dimensional spatial-temporal changes during development.
Imp

utation for

Li

pidomics and

Met

abolomics (ImpLiMet): a web-based application for optimization and method selection for missing data imputation
Authors: Ou Huiting , Surendra Anuradha , McDowell Graeme S V , Hashimoto-Roth Emily , Xia Jianguo , Bennett Steffany A L , Čuperlović-Culf Miroslava 
Published: January 2025

Abstract

Motivation
Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data. The optimal imputation method for each dataset depends on the type of data, the cause of the missingness, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset.

Results
ImpLiMet: is a user-friendly web-platform that enables users to impute missing data using eight different methods. For a given dataset, ImpLiMet suggests the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis, and skewness, as well as principal component analysis comparing the impact of the chosen imputation method on the distribution and overall behavior of the data.

Availability and implementation
ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ and https://github.com/complimet/ImpLiMet.
Ileal pouch-anal anastomosis for ulcerative colitis: 30-year analysis on surgical evolution and patient outcome
Authors: Bislenghi Gabriele , Luberto Antonio , De Coster Wout , van Langenhoven Leen , Wolthuis Albert , Ferrante Marc , Vermeire Severine , D’Hoore André 
Published: January 2025

Abstract

Background
Proctocolectomy with ileal pouch-anal anastomosis is the treatment of choice for patients with ulcerative colitis with medical refractory disease or dysplasia. The aim of this research was to describe the evolution of ileal pouch-anal anastomosis surgery and surgical outcomes over a three-decade interval in a high-volume referral centre.

Methods
All consecutive patients undergoing ileal pouch-anal anastomosis for ulcerative colitis between 1990 and 2022 at the University Hospitals of Leuven were retrospectively included. Patients were divided into three interval arms (interval A 1990–2000, interval B 2001–2010 and interval C 2011–2022). The primary outcomes of interest were anastomotic leakage at 30 days and pouch failure.

Results
Overall, 492 patients were included. The use of preoperative advanced therapies increased over time (P &lt; 0.001). An increase in laparoscopic procedures (23.2% in interval A, 66.4% in interval B, 86.0% in interval C; P &lt; 0.001) and a shift towards delayed ileal pouch-anal anastomosis (colectomy-first approach with delayed ileal pouch-anal anastomosis construction: 23.0% in interval A, 40.9% in interval B, 85.8% in interval C; P &lt; 0.001) were observed. Anastomotic leakage rate decreased from 16.7% (interval A) to 8.4% (interval C) (P = 0.04). Delayed ileal pouch-anal anastomosis was the most relevant factor in limiting leakage (OR 0.49 (95% c.i. 0.27 to 0.87); P = 0.016). Median follow-up was 7.5 years (interquartile range 2.5–16). Cumulative pouch failure incidence was 8.2%, not significantly different between the three intervals (P = 0.580). Anastomotic leakage was the only significant risk factor for pouch failure (HR 2.82 (95% c.i. 1.29 to 6.20); P = 0.010).

Conclusion
Significant changes in the management of ulcerative colitis patients occurred. Despite the widespread use of advanced therapies and the expanded surgical indications, anastomotic leakage rate decreased over time. In the context of a delayed ileal pouch-anal anastomosis, diverting ileostomy could be avoided in selected cases. Anastomotic leakage remains the most relevant risk factor for pouch failure. Pouch failure incidence remained stable over the years.
Down-regulation of histone deacetylase 2 attenuates ventricular arrhythmias in a mouse model of cardiac hypertrophy through up-regulation of Kv channel-interacting protein 2 expression
Authors: Liu Wenjuan , Liu Jianping , Wang Gang , Cheng Wanwen , Gong Haochen , Song Yujuan , Song Ming , Zhuge Yixin , Li Ying , Liu Jie 
Published: January 2025

Abstract

Aims
Decrease in repolarizing K+ currents, particularly the fast component of transient outward K+ current (Ito,f), prolongs action potential duration (APD) and predisposes the heart to ventricular arrhythmia during cardiac hypertrophy. Histone deacetylases (HDACs) have been suggested to participate in the development of cardiac hypertrophy, and Class I HDAC inhibition has been found to attenuate pathological remodelling. This study investigated the potential therapeutic effects of HDAC2 on ventricular arrhythmia in pressure overload–induced cardiac hypertrophy.

Methods and results
An in vivo cardiac hypertrophic model was produced by performing transverse aortic constriction (TAC) surgery and an in vitro cardiomyocyte hypertrophy model by stimulating neonatal rat ventricular myocytes (NRVMs) with phenylephrine (PE). HDAC2 expression was up-regulated in TAC mouse hearts and in PE-stimulated cardiomyocytes. Susceptibility to ventricular arrhythmia was increased in TAC mice, while Ito,f was decreased and APD was prolonged in TAC cardiomyocytes. Heart-specific knockdown (HKD) of HDAC2 by RNA interference increased Ito,f, shortened APD, and decreased susceptibility to ventricular arrhythmia. Concomitantly, HKD increased the expression of the obligatory β sub-unit of Ito,f, Kv channel-interacting protein 2 (KChIP2), which is down-regulated in hypertrophic hearts. The effects of HKD on KChIP2 expression, Ito,f and APD were also observed in PE-stimulated cardiomyocytes. Mechanistically, HKD increased H3K4me3 abundance and H3K4me3 enrichment at the Kcnip2 promoter in cardiomyocytes. HKD also decreased the expression of KDM5, the H3K4me3 demethylase, which resulted in H3K4me3 up-regulation. While investigating the regulatory mechanisms underlying the effect of HDAC2 on KDM5 stability, we identified CNOT4 as the active KDM5 ubiquitinase in cardiomyocytes. HKD increased CNOT4 expression and CNOT4–KDM5 interactions and thus enhanced the polyubiquitinated degradation of KDM5.

Conclusion
HDAC2 inhibition serves as a novel therapeutic strategy for preventing cardiac hypertrophy-associated electrophysiological remodelling. Furthermore, we identified a novel signalling pathway of CNOT4-mediated KDM5 degradation contributing to the up-regulation of H3K4me3-mediated KChIP2 expression in response to HDAC2 inhibition.
Reconciling blockchain technology and data protection laws: regulatory challenges, technical solutions, and practical pathways
Authors: Zafar Ammar 
Published: January 2025

Abstract
This paper thoroughly explores the complex interplay between blockchain technology and the General Data Protection Regulation (GDPR) of the European Union, alongside the substantial challenges and potential opportunities stemming from their interaction. While the challenges of decentralization and immutability in blockchain are well-documented, this paper advances the discussion by incorporating legal developments, such as evolving interpretations of joint controllership and new advisory opinions. It also evaluates emerging use cases, including blockchain integration in digital currencies like Worldcoin, highlighting contemporary compliance challenges and innovative solutions. By proposing actionable frameworks that leverage technological advancements like chameleon hashes and zero-knowledge proofs, this paper provides a forward-looking analysis of how blockchain systems can align with GDPR principles, offering theoretical insights and practical pathways for compliance. The conclusion underscores the urgent need for clear regulatory frameworks. These frameworks are crucial to enable a balanced approach that fosters innovation while ensuring robust data protection compliance, and their absence could hinder the potential impact of the research.
Standardized pipelines support and facilitate integration of diverse datasets at the Rat Genome Database
Authors: Smith Jennifer R , Tutaj Marek A , Thota Jyothi , Lamers Logan , Gibson Adam C , Kundurthi Akhilanand , Gollapally Varun Reddy , Brodie Kent C , Zacher Stacy , Laulederkind Stanley J F , Hayman G Thomas , Wang Shur-Jen , Tutaj Monika , Kaldunski Mary L , Vedi Mahima , Demos Wendy M , De Pons Jeffrey L , Dwinell Melinda R , Kwitek Anne E 
Published: January 2025

Abstract
The Rat Genome Database (RGD) is a multispecies knowledgebase which integrates genetic, multiomic, phenotypic, and disease data across 10 mammalian species. To support cross-species, multiomics studies and to enhance and expand on data manually extracted from the biomedical literature by the RGD team of expert curators, RGD imports and integrates data from multiple sources. These include major databases and a substantial number of domain-specific resources, as well as direct submissions by individual researchers. The incorporation of these diverse datatypes is handled by a growing list of automated import, export, data processing, and quality control pipelines. This article outlines the development over time of a standardized infrastructure for automated RGD pipelines with a summary of key design decisions and a focus on lessons learned.
A change language for ontologies and knowledge graphs
Authors: Hegde Harshad , Vendetti Jennifer , Goutte-Gattat Damien , Caufield J Harry , Graybeal John B , Harris Nomi L , Karam Naouel , Kindermann Christian , Matentzoglu Nicolas , Overton James A , Musen Mark A , Mungall Christopher J 
Published: January 2025

Abstract
Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of “apply patch” and “diff” commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders—e.g. ontology edits can be specified by commands like “add synonym ‘arm’ to ‘forelimb’” or “move ‘Parkinson disease’ under ‘neurodegenerative disease’.” We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs.
Database URL: https://github.com/INCATools/kgcl