bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of nano crack and loading direction on the tensile features of FeCr alloy: a molecular dynamics analysis
Authors: Gowthaman S , Jagadeesha T 
Published: January 2024

Abstract
The existence of cracks and variations in loading direction has invoked greater modifications in the material properties. In this work, the tensile features of cracked and non-cracked FeCr polycrystals have been analyzed under numerous temperatures (300 K, 500 K, 700 K, and 900 K) and loading directions (parallel and normal to the crack cross-sectional directions) through molecular dynamics and it is originated that temperature has raised a higher impact on the tensile features trailed by the existence of crack and loading directions, owing to the formation of larger kinetic energy (KE) amidst the atoms. The existence of crack offers a moderate impression on the tensile behavior followed by the loading direction, due to its dominant impact on the tensile behavior through greater stress concentrations. Additionally, it is stated that the greater temperature along with the existence of crack and loading along normal to the crack cross section offers greater reductions in the tensile features of FeCr polycrystal, owed to the interactive effect of larger KE and discontinuity among atoms. Furthermore, the shear strain and displacement contour map and materials feature also confirm a similar occurrence which leads to altering its material behavior.
Study on the preparation and mechanical and fatigue properties of alkali excited recycled concrete
Authors: Su Qing 
Published: January 2024

Abstract
This study aims to explore the preparation process of alkali excited recycled concrete, and evaluate its performance in terms of mechanical and fatigue properties, so as to provide theoretical and practical support for the application of sustainable development and environmental protection building materials. Laboratory test and performance evaluation were used to prepare alkali-stimulated recycled concrete specimens with different proportions by adjusting fly ash, alkali activator and water solid ratio in recycled concrete. Then, the mechanical properties of the specimen were tested, including the influence of the cementing material on its compressive strength and the influence of the aggregate quality on its compressive strength. Finally, the fatigue life test is used to analyze the fatigue performance of alkali stimulated recycled concrete, and the influence of different ages and different stress levels on fatigue damage performance of alkali stimulated recycled concrete specimens is studied. The experimental results show that the mechanical properties of recycled concrete can be significantly improved by using alkali excited recycled concrete preparation technology. It is found that under the same stress ratio, with the increase of age, the corresponding fatigue life will increase, the flexural tensile strength will increase, and the fatigue resistance will improve. At the same age, the fatigue life of specimens decreases with the increase of stress ratio, and the flexural tensile strength and fatigue resistance remain unchanged. This study proves the effectiveness of alkali excited recycled concrete preparation technology in improving the mechanical properties and fatigue properties of recycled concrete. Alkali stimulated recycled concrete is not only conducive to resource reuse, reduce the impact of waste on the environment, but also provides a new choice for the sustainable development of building materials. However, further research and application are needed to promote the widespread application of alkali excited recycled concrete in engineering practice.
Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography
Authors: Shi Baoshun , Jiang Ke , Zhang Shaolei , Lian Qiusheng , Qin Yanwei , Zhao Yunsong 
Published: January 2024

Abstract
Sparse-view computed tomography (SVCT) is regarded as a promising technique to accelerate data acquisition and reduce radiation dose. However, in the presence of metallic implants, SVCT inevitably makes the reconstructed CT images suffer from severe metal artifacts and streaking artifacts due to the lack of sufficient projection data. Previous stand-alone SVCT and metal artifact reduction (MAR) methods to solve the problem of simultaneously sparse-view and metal artifact reduction (SVMAR) are plagued by insufficient correction accuracy. To overcome this limitation, we propose a multi-domain deep unrolling network, called Mud-Net, for SVMAR. Specifically, we establish a joint sinogram, image, artifact, and coding domains deep unrolling reconstruction model to recover high-quality CT images from the under-sampled sinograms corrupted by metallic implants. To train this multi-domain network effectively, we embed multi-domain knowledge into the network training process. Comprehensive experiments demonstrate that our method is superior to both existing MAR methods in the full-view MAR task and previous SVCT methods in the SVMAR task.
Life cycle environmental benchmarks for Flemish dwellings
Authors: Mouton Lise , Ramon Delphine , Trigaux Damien , Allacker Karen , Crawford Robert H 
Published: January 2024

Abstract
To reduce the environmental effects caused by building construction and operation, life cycle assessment (LCA) is increasingly applied. In recent years, national building regulations have implemented LCA requirements to support building life cycle impact reduction. A key element in these regulations are environmental benchmarks which allow designers to compare their building designs with reference values. This study aims to develop bottom-up life cycle environmental benchmarks that represent the range of environmental impact results achieved with conventional construction in Flanders, Belgium. For this purpose, the study investigates the potential of using a database of building energy performance calculations. Specifically, this study considers 39 residential buildings identified as representative of the Flemish energy performance of buildings database of 2015–2016, applying modifications to establish scenarios that are still relevant in 2025. The buildings are assessed with the Belgian LCA tool TOTEM to calculate an aggregated environmental score based on the European product environmental footprint (PEF) weighting approach and including 12 main impact categories. In addition to the aggregated score, the climate change (CC) indicator is analysed individually. In view of the benchmarks, variations were applied to the 39 original buildings in terms of heating system and materialisation. The variation in heating system included changing gas boilers to electric heat pumps to comply with upcoming (2025) Flemish building regulations. The variations in building materials included three sets of conventional Flemish building element compositions that were applied to generate a wider spread of impact results as a basis for benchmarks. Benchmark values were derived through a statistical analysis of the 117 modelled variants: a best-practice value (10th percentile), reference value (median) and limit value (90th percentile). For the environmental score, the benchmark values are 86, 107 and 141 millipoints per square meter of gross heated floor area (GHFA) (mPt m−2GHFA), respectively; and for CC, the benchmark values are 844, 1015 and 1284 kg CO2-eq m−2 GHFA. Finally, the study discusses the representativeness, implications and limitations of the final benchmarks and benchmark approach.
Temporal public perceptions and experiences during water service disruptions: the case of Jackson, Mississippi
Authors: Safarpour Haniye , Spearing Lauryn A 
Published: January 2024

Abstract
Failures in urban water systems are becoming a common occurrence in the US due to disasters, aging infrastructure, and financial constraints, among other concerns. For example, Jackson, Mississippi has experienced reoccurring water outages, burdening community members as they must seek alternative water sources. Prior research has primarily focused on technical, institutional, and public health aspects of water crises, with limited attention to community perspectives. Understanding such social aspects can shed light on, for example, community priorities, levels of trust, mental health concerns, and communication gaps. Here, we document the temporal experiences and perceptions of community members during the Jackson Water Crises. To do so, we qualitatively analyzed news media data and employed topic modeling techniques on social media data from two years, capturing two service disruption events. Our results reveal the cascading impacts of water outages on end-users, including financial, social, and technical issues. For example, over time, results indicate that trust in the government and water providers in Jackson eroded, which may hinder public support. Our results show that social media can be a useful tool for utilities to understand public perceptions in real-time. Recommendations proposed here can inform future responses to water crises in Jackson and other communities, ensuring end-users’ perspectives are incorporated.
GB12-09, a bispecific antibody targeting IL4Rα and IL31Rα for atopic dermatitis therapy
Authors: Deng Feiyan , Qiu Yuxin , Zhang Xiangling , Guo Nining , Hu Junhong , Yang Wenjie , Shang Wei , Liu Bicheng , Qin Suofu 
Published: January 2024

Abstract
Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by dysregulated immune responses. The key mediators of AD pathogenesis are T helper 2 (TH2) cells and TH2 cytokines. Targeting interleukin 4 (IL4), IL13 or IL31 has become a pivotal focus in both research and clinical treatments for AD. However, the need remains pressing for the development of a more effective and safer therapy, as the current approaches often yield low response rates and adverse effects. In response to this challenge, we have engineered a immunoglobulin G—single-chain fragment variable (scFv) format bispecific antibody (Ab) designed to concurrently target IL4R and IL31R. Our innovative design involved sequence optimization of VL-VH and the introduction of disulfide bond (VH44-VL100) within the IL31Rα Ab scFv region to stabilize the scFv structure. Our bispecific Ab efficiently inhibited the IL4/IL13/IL31 signaling pathways in vitro and reduced serum immunoglobulin E and IL31 levels in vivo. Consequently, this intervention led to improved inflammation profiles and notable amelioration of AD symptoms. This research highlighted a novel approach to AD therapy by employing bispecific Ab targeting IL4Rα and IL31Rα with potent efficacy.
Fruit softening: evidence for rhamnogalacturonan lyase action in vivo in ripe fruit cell walls
Authors: Al-Hinai Thurayya Z S , Mackay C Logan , Fry Stephen C 
Published: January 2024

Abstract

Background and Aims
The softening of ripening fruit involves partial depolymerization of cell-wall pectin by three types of reaction: enzymic hydrolysis, enzymic elimination (lyase-catalysed) and non-enzymic oxidative scission. Two known lyase activities are pectate lyase and rhamnogalacturonan lyase (RGL), potentially causing mid-chain cleavage of homogalacturonan and rhamnogalacturonan-I (RG-I) domains of pectin respectively. However, the important biological question of whether RGL exhibits action in vivo had not been tested.

Methods
We developed a method for specifically and sensitively detecting in-vivo RGL products, based on Driselase digestion of cell walls and detection of a characteristic unsaturated ‘fingerprint’ product (tetrasaccharide) of RGL action.

Key Results
In model experiments, potato RG-I that had been partially cleaved in vitro by commercial RGL was digested by Driselase, releasing an unsaturated tetrasaccharide (‘ΔUA-Rha-GalA-Rha’), taken as diagnostic of RGL action. This highly acidic fingerprint compound was separated from monosaccharides (galacturonate, galactose, rhamnose, etc.) by electrophoresis at pH 2, then separated from ΔUA-GalA (the fingerprint of pectate lyase action) by thin-layer chromatography. The ‘ΔUA-Rha-GalA-Rha’ was confirmed as 4-deoxy-β-l-threo-hex-4-enopyranuronosyl-(1→2)-l-rhamnosyl-(1→4)-d-galacturonosyl-(1→2)-l-rhamnose by mass spectrometry and acid hydrolysis. Driselase digestion of cell walls from diverse ripe fruits [date, sea buckthorn, cranberry, yew (arils), mango, plum, blackberry, apple, pear and strawberry] yielded the same fingerprint compound, demonstrating that RGL had been acting in vivo in these fruits prior to harvest. The ‘fingerprint’ : (galacturonate + rhamnose) ratio in digests from ripe dates was approximately 1 : 72 (mol/mol), indicating that ~1.4 % of the backbone Rha→GalA bonds in endogenous RG-I had been cleaved by in-vivo RGL action.

Conclusions
The results provide the first demonstration that RGL, previously known from studies of fruit gene expression, proteomic studies and in-vitro enzyme activity, exhibits enzyme action in the walls of soft fruits and may thus be proposed to contribute to fruit softening.
Associations between Informant-Reported Cognitive Complaint and Longitudinal Cognitive Decline in Subjective Cognitive Decline A 7-Year Longitudinal Study
Authors: Liu Yuanyuan , Su Ning , Li Wei , Hong Bo , Yan Feng , Wang Jinghua , Li Xia , Chen Jianhua , Xiao Shifu , Yue Ling 
Published: January 2024

Abstract

Objective
This study aimed to determine the predictive values of informant-reported memory decline (IMD) among subjective cognitive decline (SCD) older adults from a 7-year community-based cohort study.

Method
Ninety SCD participants were included. Demographic data and neuropsychological test scores at both baseline and 7-year follow-up were collected. Differences between SCD with IMD (+IMD) and SCD without IMD (−IMD) were compared. Logistic regression models were used to determine whether baseline IMD could predict diagnostic outcomes at 7-year follow-up.

Results
Forty-one percent of SCD adults had IMD. At baseline, the +IMD group showed more depressive symptoms (p = 0.016) than the −IMD group. Furthermore, the Beijing-version Montreal Cognitive Assessment (MoCA), Digit Span Test-Forward, Visual Matching and Reasoning, and Wechsler Adult Intelligence Scale-RC Picture Completion (WAIS-PC) scores in the +IMD group were significantly lower than those in the −IMD group. Fifty-four percent of +IMD participants converted to mild cognitive impairment (MCI) or dementia at follow-up, and 22.6% of the −IMD participants converted to MCI. Follow-up Mini-Mental State Examination, MoCA, and Verbal Fluency Test scores of the +IMD group were significantly lower than those in the −IMD group. The +IMD group was more likely to progress to cognitive impairment at 7-year follow-up (OR = 3.361, p = 0.028).

Conclusions
SCD participants with +IMD may have poorer cognition and are more likely to convert to cognitive impairment over time. Our long-term follow-up study confirmed the importance of informants’ perceptions of SCD, which can help clinicians identify individuals at risk of cognitive decline.
DeepRank-GNN-esm: a graph neural network for scoring protein–protein models using protein language model
Authors: Xu Xiaotong , Bonvin Alexandre M J J 
Published: January 2024

Abstract

Motivation
Protein–Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability.

Results
We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes).

Availability and implementation
DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.
SciDataFlow: a tool for improving the flow of data through science
Authors: Buffalo Vince 
Published: January 2024

Abstract

Motivation
Managing data and code in open scientific research is complicated by two key problems: large datasets often cannot be stored alongside code in repository platforms like GitHub, and iterative analysis can lead to unnoticed changes to data, increasing the risk that analyses are based on older versions of data.

Results
SciDataFlow is a fast, concurrent command-line tool paired with a simple Data Manifest specification that streamlines tracking data changes, uploading data to remote repositories, and pulling in all data necessary to reproduce a computational analysis.

Availability and implementation
SciDataFlow is available at https://github.com/vsbuffalo/scidataflow.
CircSI-SSL: circRNA-binding site identification based on self-supervised learning
Authors: Cao Chao , Wang Chunyu , Yang Shuhong , Zou Quan 
Published: January 2024

Abstract

Motivation
In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain.

Results
To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value.

Availability and implementation
The source code and data are available at https://github.com/cc646201081/CircSI-SSL.
FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data
Authors: Koutrouli Mikaela , Nastou Katerina , Piera Líndez Pau , Bouwmeester Robbin , Rasmussen Simon , Martens Lennart , Jensen Lars Juhl 
Published: January 2024

Abstract

Motivation
Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.

Results
To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

Availability and implementation
Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI &lt;pip install favapy&gt; as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.
The role of epicardial fat radiomic profiles for atrial fibrillation identification and recurrence risk with coronary CT angiography
Authors: Wu Jingping , Li Yuwei , Wu Dan , Schoepf Uwe-Joseph , Zhao Pengfei , Goller Markus , Li Junhuan , Tian Jinwen , Shen Mingzhi , Cao Kunlin , Yang Li , Zhang Fan 
Published: January 2024

Abstract

Objectives
Fat radiomic profile (FRP) was a promising imaging biomarker for identifying increased cardiac risk. We hypothesize FRP can be extended to fat regions around pulmonary veins (PV), left atrium (LA), and left atrial appendage (LAA) to investigate their usefulness in identifying atrial fibrillation (AF) and the risk of AF recurrence.

Methods
We analysed 300 individuals and grouped patients according to the occurrence and types of AF. We used receiver operating characteristic and survival curves analyses to evaluate the value of imaging biomarkers, including fat attenuation index (FAI) and FRP, in distinguishing AF from sinus rhythm and predicting post-ablation recurrence.

Results
FRPs from AF-relevant fat regions showed significant performance in distinguishing AF and non-AF with higher AUC values than FAI (peri-PV: FRP = 0.961 vs FAI = 0.579, peri-LA: FRP = 0.923 vs FAI = 0.575, peri-LAA: FRP = 0.900 vs FAI = 0.665). FRPs from peri-PV, peri-LA, and peri-LAA were able to differentiate persistent and paroxysmal AF with AUC values of 0.804, 0.819, and 0.694. FRP from these regions improved AF recurrence prediction with an AUC of 0.929, 0.732, and 0.794. Patients with FRP cut-off values of ≥0.16, 0.38, and 0.26 had a 7.22-, 5.15-, and 4.25-fold higher risk of post-procedure recurrence, respectively.

Conclusions
FRP demonstrated potential in identifying AF, distinguishing AF types, and predicting AF recurrence risk after ablation. FRP from peri-PV fat depot exhibited a strong correlation with AF. Therefore, evaluating epicardial fat using FRP was a promising approach to enhance AF clinical management.

Advances in knowledge
The role of epicardial adipose tissue (EAT) in AF had been confirmed, we focussed on the relationship between EAT around pulmonary arteries and LAA in AF which was still unknown. Meanwhile, we used the FRP to excavate more information of EAT in AF.