bluuebunny/crossref_metadata_2025_split huggingface dataset:
An ultra-thin organosiloxane film with excellent corrosion resistance based on self-crosslinking of multiple polymerization sites prepared by PECVD on copper alloy
Authors: Huang Meiru , Shen Luli , Yin Yuying , Liu Luqi , Zhou Guiping , Wang Gang , Zeng Zhixiang , Ma Fuliang 
Published: December 2024

Abstract
Intelligent, integrated, and environmentally sustainable electronic products have become a major trend, making the protection of electronic products a top priority. Low surface energy organosiloxane polymer films are very attractive for electronic protection, but their high porosity is not conducive to the protection of electronic devices. To solve this problem, an ultra-thin 2,4,6,8-tetravinyl-2,4,6,8-tetramethylcyclotetrasiloxane polymer film (p-V4D4) with very low porosity was deposited on a copper alloy by PECVD self-crosslinking. Compared with triethoxyvinylsilane polymer film (p-VTES) and divinyltetramethyldisiloxane polymer film (p-DVTMS), the p-V4D4 has more polymerization sites, higher cross-linking density, and lower porosity. Benefiting from the synergistic effect of good hydrophobicity and low porosity structure, the p-V4D4 offers 99.99% corrosion suppression efficiency, 48 h of salt spray corrosion resistance, and up to 23 days of immersion in 3.5 wt% NaCl. Moreover, the dense structure gives it good mechanical stability and low dielectric properties. These findings provide a new pathway for synthesizing films with efficient anti-corrosion properties and have potential application prospects in electronic protection and microelectronics.
Mechanical characterization and thermal investigation of nylon polymer matrix composites used in automotive power train systems
Authors: Shanmugam Thillikkani , D S Manoj Abraham , N Suresh , Obili Sathish 
Published: December 2024

Abstract
Failures may result from a variety of mechanical issues, including the improper composition of the components. This study synthesizes specific typical varying materials, including nylon 6/6 and glass fibers, with unique volume proportions to improve abrasion resistance and extend the lifespan of equipment. The research demonstrated that polymer gears have a lengthier longevity than gears that are entirely composed of nylon. The specimen samples undergo a variety of evaluations, including tensile, compressive, flexural, impact, Thermo Gravimetric analysis, and wear assessments. Nevertheless, the rate of depreciation remains constant. An experiment has been conducted that involves a composite of 90% nylon and 10% nylon with 10% glass fiber. Research indicates that the combination of 10% Nylon 6/6 produces optimistic outcomes. The research indicates that a composition that comprises 30% glass fiber and 70% Nylon 6/6 offers exceptional strength, wear resistance, and reduced wear.
Preliminary study on degradation mechanisms of plasma-treated DR1 by atomistic simulations
Authors: NABIYEVA Namunakhon , AKRAMOV Tohir , ABDUVOKHIDOV Davronjon , ZHANG 张 Yuantao 远涛 , YUSUPOV Maksudbek , RAZZOKOV Jamoliddin 
Published: December 2024

Abstract
Cold atmospheric plasma (CAP) has emerged as a promising technology for the degradation of organic dyes, but the underlying mechanisms at the molecular level remain poorly understood. Using density-functional tight-binding (DFTB)-based quantum chemical molecular dynamics at 300 K, we have performed numerical simulations to investigate the degradation mechanism of Disperse Red 1 (DR) interacting with CAP-generated oxygen radicals. One hundred direct-dynamics trajectories were calculated for up to 100 ps simulation time, after which hydrogen-abstraction, benzene ring-opening/expanding, formaldehyde formation and modification in the chromophoric azo group which can lead to color-losing were observed. The latter was obtained with yields of around 6% at the given temperature. These findings not only enhance our understanding of CAP treatment processes but also have implications for the development of optimized purification systems for sustainable wastewater treatment. This study underscores the utility of DFTB simulations in unraveling complex chemical processes and guiding the design of advanced treatment strategies in the context of CAP technology.
Effects of soil bund and landscape positions on selected soil physico-chemical properties at Anole watershed, Rift Valley Basin, Ethiopia
Authors: Mathewos Markos , Lukas Mesfin 
Published: December 2024

Abstract
Land degradation in the form of soil erosion and fertility reduction is a major environmental problem in Ethiopia. Various efforts to control soil erosion through the introduction of soil and water conservation measures have been underway for nearly five decades in Ethiopia. The purpose of this research was to assess the effects of soil bunds on selected soil physical and chemical properties in the Anole watershed of Rift Valley of Ethiopia. A reconnaissance survey was conducted to identify a representative soil sampling site. 18 composite soil samples were collected from soil bund and no conservation practice (control) with three replications at three slope positions from 0 to 30 cm depth, whereas, 18 undisturbed soil samples were collected for bulk density analysis from both land uses. The analysis was carried out using analysis of variance (ANOVA). Clay, sand, BD, CEC, TN, and OM showed significant variation (P < 0.05) between the soil bund and control plots. Whereas, for slope positions, sand, clay, BD, CEC, TN, and available phosphorus in the Anole watershed were found to be statistically significant (P < 0.05). In steep, middle, and lower slopes, contents of OC and exchangeable potassium were greater in fields with soil bunds than without. Lower slope fields showed greater clay, OM, and nutrients than steep slopes. Because of the implementation of soil bund in the study watershed, the majority of soil’s physical and chemical characteristics have shown improvement. Proper implementations of soil bunds in the study area have a pronounced positive impact and should be practiced and applied in adjacent watersheds and similar agroecologies in the Rift Valley of Ethiopia.
Neuromorphic, physics-informed spiking neural network for molecular dynamics
Authors: Pham Vuong Van , Muther Temoor , Kalantari Dahaghi Amirmasoud 
Published: December 2024

Abstract
Molecular dynamics (MD) simulations are used across many fields from chemical science to engineering. In recent years, Scientific Machine Learning (Sci-ML) in MD attracted significant attention and has become a new direction of scientific research. However, effectively integrating Sci-ML with MD simulations remains challenging. Compliance with the physical principles, comparable performance to a numerical method, and integration of start-of-the-art ML architectures are top-concerned examples of those gaps. This work addresses these challenges by introducing, for the first time, the neuromorphic physics-informed spiking neural network (NP-SNN) architecture to solve Newton’s equations of motion for MD systems. Unlike conventional Sci-ML methods that heavily rely on prior training data, NP-SNN performs without needing pre-existing data by embedding MD fundamentals directly into its learning process. It also leverages the enhanced representation of real biological neural systems through spiking neural network integration with molecular dynamic physical principles, offering greater efficiency compared to conventional AI algorithms. NP-SNN integrates three core components: (1) embedding MD principles directly into the training, (2) employing best practices for training physics-informed ML systems, and (3) utilizing a highly advanced and efficient SNN architecture. By integrating these core components, this proposed architecture proves its efficacy through testing across various molecular dynamics systems. In contrast to traditional MD numerical methods, NP-SNN is trained and deployed within a continuous time framework, effectively mitigating common issues related to time step stability. The results indicate that NP-SNN provides a robust Sci-ML framework that can make accurate predictions across diverse scientific molecular applications. This architecture accelerates and enhances molecular simulations, facilitating deeper insights into interactions and system dynamics at the molecular level. The proposed NP-SNN paves the way for foundational advancements across various domains of chemical and material sciences especially in energy, environment, and sustainability fields.
Machine and deep learning performance in out-of-distribution regressions
Authors: Shmuel Assaf , Glickman Oren , Lazebnik Teddy 
Published: December 2024

Abstract
Machine learning (ML) and deep learning (DL) models are gaining popularity due to their effectiveness in many computational tasks. These models are based on an intuitive, but frequently unsatisfied, assumption that the data used to train these models is well-representing the task at hand. This gives rise to the out-of-distribution (OOD) challenge which can cause an unexpected drop in the data-driven model’s performance. In this study, we evaluate the performance of various ML and DL models in in-distribution (ID) versus OOD prediction. While the degradation in OOD performance is well acknowledged, to the best of our knowledge, this is one of the first studies to quantify it for various models on a large benchmark n = 15 real-world regression datasets. We extensively (

n
>
40

000

runs) compare the ID versus OOD performance of XGBoost, random forest, K-nearest-neighbors, support vector machine, and linear regression models, as well as AutoML models (Tree-based Pipeline Optimization Tool and AutoKeras). In addition, to tackle this challenge, we propose to integrate a symbolic regression (SR) as a feature engineering method model with an ML or DL model to improve its performance for OOD samples. Our results show that the incorporation of SR-derived features significantly enhances the predictive capabilities of both ML and DL models with 3.70% and 10.20%, on average, of the OOD samples, respectively, without reducing ID performance and in fact improving it to a slightly lower extent. As such, this method can help produce more generalized and robust data-driven models.
Climate change impacts to extreme weather events associated with insured losses in New Zealand: a review
Authors: Aldridge Joanna , Bell Rob 
Published: December 2024

Abstract
In 2023, New Zealand experienced two consecutive weather-related events that exceeded previous insured losses by more than a factor of ten: the Auckland Anniversary Day floods and ex-Tropical Cyclone Gabrielle. Further, climate reporting for financial services becomes mandatory in this jurisdiction in 2024, yet established catastrophe models are not available for a range of perils in New Zealand. Insurers hence need to better understand weather-related catastrophes in New Zealand and the impact of climate change in this island nation exposed to strong marine influences and weather events of both tropical and temperate origin. This comprehensive review seeks to integrate and interpret the findings from a wide range of scientific literature into a cohesive summary useful for insurers evaluating climate risk in New Zealand. This review summarises the climate risk facing New Zealand, focussing on extreme events including heavy rainfall, floods, coastal hazards driven by weather systems on a range of spatiotemporal scales: atmospheric rivers, ex- and extra-tropical cyclones and severe convective storms, as well as wildfire weather. Potential changes to natural climate variability are also considered. The review shows that extreme rainfall over a range of durations, but particularly shorter durations, is projected to increase, and riverine and coastal flooding will also increase, although potential impacts are less well understood. Extreme weather systems such as ex-tropical and extra-tropical cyclones may be supported by warmer sea surface temperatures and the poleward shift in subtropical weather systems, although quantitative studies on their changing frequency and severity are not yet available. Key knowledge gaps in understanding sources of extreme rainfall, ex-tropical cyclones and other low-pressure systems and severe convective storms are identified. Further, focus areas for climate-related risk reduction that insurers could seek to promote to help protect the New Zealand community are discussed.
Antibiotic prescribing for care-home residents: a population-based, cross-classified multilevel analysis in Scotland, UK
Authors: De Souza Nicosha , Guthrie Bruce , Grant Suzanne , Lorencatto Fabiana , Dickson Jane , Herbec Aleksandra , Hughes Carmel , Sneddon Jacqueline , Donnan Peter T , Marwick Charis A 
Published: December 2024

Abstract

Background
There is wide variation in antibiotic prescribing across care-homes for older people, with implications for resident outcomes and antimicrobial resistance.

Objective
To quantify variation in antibiotic prescribing and associations with resident, care-home and general practice characteristics.

Design
Population-based analyses using administrative data.

Setting and subjects
148 care-homes in two Scottish regions, with 6633 residents registered with 139 general practices.

Methods
Prescriptions for any antibiotic and for broad-spectrum antibiotics between 1 April 2016 and 31 March 2017 were analysed using cross-classified multilevel negative binomial regression.

Results
For any antibiotics, the mean prescription rate was 6.61 (SD 3.06) per 1000 resident bed-days (RBD). In multivariate analysis, prescribing was associated with resident age [incidence rate ratio (IRR) 1.30 [95% confidence interval 1.19 to 1.41] for 90+ versus &lt;80 years old] and comorbidity (1.88 [1.71 to 2.06] for Charlson Comorbidity Index 3+ versus 0), and the care-home’s sampling rate for microbiological culture (1.53 [1.28 to 1.84] for &gt;7 versus &lt;3.5 samples per 1000 RBD), with residual unexplained variation between care-homes (median IRR 1.29 [1.23 to 1.36]) and general practices (1.11 [1.05 to 1.18]). For broad-spectrum antibiotics, the mean rate was 0.98 (0.92) per 1000 RBD. Broad-spectrum prescribing was also associated with resident age, sex, comorbidity and sampling rate, with larger residual unexplained variation between care-homes (1.56 [1.36 to 1.77]) and general practices (1.51 [1.31 to 1.72]).

Conclusion
Variation in prescribing was influenced by resident case-mix, but there is significant unexplained variation between care-homes and between general practices, indicating a need for antibiotic stewardship to target both.
Trop-2 and Ephrin B2 expression in urothelial carcinoma with divergent differentiation and aggressive urothelial carcinoma subtypes
Authors: Case Katherine B , Martini Dylan J , Dababneh Melad N , Bidot Samuel , Nazha Bassel , Brown Jacqueline , Joshi Shreyas , Narayan Vikram , Parihar Vaunita , Saeed Faisal , Bilen Mehmet Asim , Harik Lara R 
Published: December 2024

Abstract

Objectives
Urothelial carcinomas (UCs) encompass a heterogeneous group of tumors. Several histopathologic features are associated with poor clinical outcomes and limited treatment options. With new rising therapeutic modalities, we aimed to determine the pattern of expression of Trop-2 and ephrin B2 in UC with aggressive subtype histology and/or divergent differentiation (SH/DD).

Methods
We performed a retrospective analysis of 113 UC samples with SH/DD at our institution from 2011 to 2021. Immunohistochemical staining for Trop-2 and ephrin B2 expression was performed on all cases. Expression was determined by the percentage of samples with a moderate or strong H-score.

Results
Our results show Trop-2 expression was the highest in squamous cell carcinoma and UC with squamous differentiation, adenocarcinoma and UC with glandular differentiation, and plasmacytoid subtype, while ephrin B2 expression was highest in adenocarcinoma, UC with glandular differentiation, and small cell carcinoma.

Conclusions
Expression of Trop-2 and ephrin B2 may demonstrate therapeutic possibilities for patients with SH/DD, who usually have limited treatment options, particularly in small cell carcinoma, in which few targets have been identified. Clinical trials to investigate the efficacy of these novel treatments are warranted.
A lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules
Authors: Roy Subhankar , Kumar Maity Dilip , Mukhopadhyay Anirban 
Published: December 2024

Abstract
Deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence compressors for novel species frequently face challenges when processing wide-scale raw, FASTA, or multi-FASTA structured data. For years, molecular sequence databases have favored the widely used general-purpose Gzip and Zstd compressors. The absence of sequence-specific characteristics in these encoders results in subpar performance, and their use depends on time-consuming parameter adjustments. To address these limitations, in this article, we propose a reference-free, lossless sequence compressor called GraSS (Grammatical, Statistical, and Substitution Rule-Based). GraSS compresses sequences more effectively by taking advantage of certain characteristics seen in DNA and RNA sequences. It supports various formats, including raw, FASTA, and multi-FASTA, commonly found in GenBank DNA and RNA files. We evaluate GraSS’s performance using ten benchmark DNA sequences with reduced number of repeats, two highly repetitive RNA sequences, and fifteen raw DNA sequences. Test results indicate that the weighted average compression ratios (WACR) for DNA and RNA sequences are 4.5 and 19.6, respectively. Additionally, the entire DNA sequence corpus has a total compression time (TCT) of 246.8 seconds (s). These results demonstrate that the proposed compression method performs better than several advanced algorithms specifically designed to handle various levels of sequence redundancy. The decompression times, memory usage, and CPU usage are also very competitive. Contact: anirban@klyuniv.ac.in
Higher order interaction analysis quantifies coordination in the epigenome revealing novel biological relationships in Kabuki syndrome
Authors: Cuvertino Sara , Garner Terence , Martirosian Evgenii , Walusimbi Bridgious , Kimber Susan J , Banka Siddharth , Stevens Adam 
Published: December 2024

Abstract
Complex direct and indirect relationships between multiple variables, termed higher order interactions (HOIs), are characteristics of all natural systems. Traditional differential and network analyses fail to account for the omic datasets richness and miss HOIs. We investigated peripheral blood DNA methylation data from Kabuki syndrome type 1 (KS1) and control individuals, identified 2,002 differentially methylated points (DMPs), and inferred 17 differentially methylated regions, which represent only 189 DMPs. We applied hypergraph models to measure HOIs on all the CpGs and revealed differences in the coordination of DMPs with lower entropy and higher coordination of the peripheral epigenome in KS1 implying reduced network complexity. Hypergraphs also capture epigenomic trans-relationships, and identify biologically relevant pathways that escape the standard analyses. These findings construct the basis of a suitable model for the analysis of organization in the epigenome in rare diseases, which can be applied to investigate mechanism in big data.
GAADE: identification spatially variable genes based on adaptive graph attention network
Authors: Zhang Tianjiao , Sun Hao , Wu Zhenao , Zhao Zhongqian , Zhao Xingjie , Zhang Hongfei , Gao Bo , Wang Guohua 
Published: December 2024

Abstract
The rapid advancement of spatial transcriptomics (ST) sequencing technology has made it possible to capture gene expression with spatial coordinate information at the cellular level. Although many methods in ST data analysis can detect spatially variable genes (SVGs), these methods often fail to identify genes with explicit spatial expression patterns due to the lack of consideration for spatial domains. Considering spatial domains is crucial for identifying SVGs as it focuses the analysis of gene expression changes on biologically relevant regions, aiding in the more accurate identification of SVGs associated with specific cell types. Existing methods for identifying SVGs based on spatial domains predefine spot similarity before training, which prevents adaptive learning and limits generalizability across different tissues or samples. This limitation may also lead to inaccurate identification of specific genes at boundary regions. To address these issues, we present GAADE, an unsupervised neural network architecture based on graph-structured data representation learning. GAADE stacks encoder/decoder layers and integrates a self-attention mechanism to reconstruct node attributes and graph structure, effectively capturing spatial domain structures of different sections. Consequently, we confine the identification of SVGs within spatial domains. By performing differential expression analysis on spots within the target spatial domain and their multi-order neighbors, GAADE detects genes with enriched expression patterns within defined domains. Comparative evaluations with five other popular methods on ST datasets across four different species, regions and tissues demonstrate that GAADE exhibits superior performance in detecting SVGs and capturing the extent of spatial gene expression variation.
A comparative analysis of gene expression profiling by statistical and machine learning approaches
Authors: Bontonou Myriam , Haget Anaïs , Boulougouri Maria , Audit Benjamin , Borgnat Pierre , Arbona Jean-Michel 
Published: December 2024

Abstract

Motivation
Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretations are lists of genes ranked by their importance for the predictions, with top-ranked genes likely linked to the phenotype. In this article, we discuss the limitations of such approaches using integrated gradient, an explainability method developed for neural networks, as an example.

Results
Experiments are performed on RNA sequencing data from public cancer databases. A collection of ML models, including multilayer perceptrons and graph neural networks, are trained to classify samples by cancer type. Gene rankings from integrated gradients are compared to genes highlighted by statistical feature selection methods such as DESeq2 and other learning methods measuring global feature contribution. Experiments show that a small set of top-ranked genes is sufficient to achieve good classification. However, similar performance is possible with lower-ranked genes, although larger sets are required. Moreover, significant differences in top-ranked genes, especially between statistical and learning methods, prevent a comprehensive biological understanding. In conclusion, while these methods identify pathology-specific biomarkers, the completeness of gene sets selected by explainability techniques for understanding biological processes remains uncertain.

Availability and implementation
Python code and datasets are available at https://github.com/mbonto/XAI_in_genomics.