bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emergent Wigner phases in moiré superlattice from deep learning
Authors: Li Xiang , Qian Yubing , Ren Weiluo , Xu Yang , Chen Ji 
Published: September 2024

Abstract

Moiré superlattice designed in stacked van der Waals material provides a dynamic platform for hosting exotic and emergent condensed matter phenomena. However, the relevance of strong correlation effects and the large size of moiré unit cells pose significant challenges for traditional computational techniques. To overcome these challenges, we develop an unsupervised deep learning approach to uncover electronic phases emerging from moiré systems based on variational optimization of neural network many-body wavefunction. Our approach has identified diverse quantum states, including novel phases such as generalized Wigner crystals, Wigner molecular crystals, and previously unreported Wigner covalent crystals. These discoveries provide insights into recent experimental studies and suggest new phases for future exploration. They also highlight the crucial role of spin polarization in determining Wigner phases. More importantly, our proposed deep learning approach is proven general and efficient, offering a powerful framework for studying moiré physics.
Characterization and antimicrobial resistance of Staphylococcus hyicus from swine exudative epidermitis in South Korea
Authors: Yun Chi Sun , Kang Su-Min , Kwon Dong Hyeon , Lee Sanghyub , Jeon Gyu-Tae , Kang Hye Jeong , Kim Jongho , Byun Jae-Won , Ku Bok-Kyung , Hwang Mi-Hye , Kim Ha-Young 
Published: September 2024

Abstract

Background: Staphylococcus hyicus causes porcine exudative epidermitis, predominantly affecting suckling and weaned piglets. This bacterium produces various exfoliative toxins (ExhA, ExhB, ExhC, ExhD, SHETA, and SHETB), which are responsible for the clinical manifestations of exudative epidermitis. However, treatment failure is common due to frequent antimicrobial resistance in porcine strains. Therefore, this study aimed to identify the genes encoding exfoliative toxins and assess the antimicrobial resistance profiles of S. hyicus. A total of 17 S. hyicus isolates were collected from piglets with skin lesions from 2014 to 2021. All strains were subjected to species-specific polymerase chain reaction targeting sodA to confirm the presence of S. hyicus, and polymerase chain reaction amplification of exfoliative toxin genes (exhA, exhB, exhC, exhD, sheta, and shetb) was performed to differentiate toxigenic strains. Pulsed-field gel electrophoresis analysis and minimum inhibitory concentration tests using broth microdilution were conducted to further analyze the strains.
Results: Exfoliative toxin genes were detected in 52.9% (n = 9) of the S. hyicus isolates, with notable detection of exhB (17.6%), exhC(17.6%), exhD (11.8%), exhA (5.9%), sheta (0%), and shetb(0%). Pulsed-field gel electrophoresis analysis categorized the isolates into 11 pulsotypes with 70% similarity. Among 18 tested antimicrobials, all isolates exhibited 100% susceptibility to ceftiofur and sulfonamides and high susceptibility rates to neomycin, tilmicosin, and tetracyclines. Whereas the susceptibility rate of spectinomycin was 0% in all isolates, multidrug resistance was observed in 82.4% of the isolates, and in all toxigenic strains.
Conclusions: These findings provide crucial insights for monitoring and devising effective treatment strategies for managing exudative epidermitis in pigs caused by S. hyicus.
Antimicrobial peptide production with Corynebacterium glutamicum on lignocellulosic side streams
Authors: Waldschitz Daniel , Neudert Mark-Richard , Kitzmueller Jakob , Bong Jae Hwi , Bus Yannick , Karner Eva Maria , Sinner Peter , Spadiut Oliver 
Published: September 2024

Abstract

Background:Biorefineries usually focus on the production of low-value commodities, such as bioethanol, platform chemicals or single cell protein.Shifting production to bioactive compounds, such as antimicrobial peptides, could provide an opportunity to increase the economic viability of biorefineries.
Results: Recombinant production of the antimicrobial peptide pediocin PA-1 in Corynebacterium glutamicum was transferred from yeast extract based media to minimal media based on lignocellulosic spent sulfite liquor.Induced batch, fed batch, and extended batch process modes were compared for highest pediocin PA-1 production.
Conclusion: For pediocin PA-1 production on lignocellulosic residues, extended batch cultivation was identified as the optimal process mode, producing up to $\simeq$ 104 mg/L active pediocin PA-1.Moreover, the production of pediocin PA-1 on this sustainable second generation resource exceeded its state-of-the-art production on yeast extract based media $\simeq$ 1.5-fold.
Strategies for Carbon Reduction in China's Aluminum Industry: An LMDI-based Decomposition and Sensitivity Analysis Framework
Authors: Chang Jing , Hu Guangwen , Gu Yifan , Wu Yufeng 
Published: September 2024

Abstract

In this study, we harnessed Material Flow Analysis (MFA) to meticulously quantify the substance flows across the mining, smelting, electrolysis, Processing and manufacturing, Recycling management, and regeneration stages of China's aluminum industry from 1990 to 2022. Concurrently, we dissected the carbon emissions of primary and secondary aluminum products employing the Logarithmic Mean Divisia Index (LMDI) approach. The LMDI framework facilitated the decomposition of emissions into five endogenous variables: emission factors, energy structure, energy intensity, product structure, and total production. Our analysis foregrounds the principal driving forces shaping the carbon footprint of the aluminum sector and assesses the nuanced impacts of long-term and short-term policy shifts under various baseline years. Key findings illuminate that: (1)The carbon emission growth in China's aluminum industry has partially decoupled from production.(2)The pivotal elements of the industry's low-carbon transition hinge on technological advancements and enhanced recycling rates of waste products.(3)Enhanced energy efficiency emerges as a critical determinant in curtailing carbon emissions.(4)The industry exhibits robust adaptability to policy shifts.(5)Furthermore, the cumulative and synergistic effects of policies are instrumental in propelling energy conservation, emission reduction, and the cyclical utilization of resources. This research offers not only pivotal insights for the sustainable evolution and carbon mitigation of China's aluminum sector but also serves as a methodological exemplar for CO2 studies in other high-emission industries.
Evidence of an emerging triple-reassortant H3N3 avian influenza virus in China
Authors: He Lei , Zhang Yuhao , Si Kaixin , Yu Chuan , Shang Ke , Yu Zuhua , Wei Ying , Sarker Subir , Chen Songbiao 
Published: September 2024

Abstract

The H3 subtype of avian influenza virus (AIV) stands out as one of the most prevalent subtypes, posing a significant threat to public health, and a novel triple-reassortant H3N3 AIV, designated A/chicken/China/16/2023 (H3N3), was isolated from a chicken in northern China. The complete genome of the isolate was determined using next-generation sequencing, and the AIV-like particles were confirmed via transmission electron microscopy. Phylogenetic analyses revealed that eight genes of the H3N3 isolate clustered within the Eurasian lineage of AIVs, indicating the novel H3N3 isolate has various constellations and was generated by complex reassortment events involving H3N8, H9N2, and H10N3 subtype influenza viruses. Strikingly, the HA gene of the H3N3 isolate exhibited the closest evolutionary relationships to a human-derived H3N8 influenza virus, posing a potential threat to public health. Additionally, the presence of Q226 and T228 in the HA protein suggests the H3N3 virus preferentially binds to α-2,3-linked sialic acid receptors. While the HA cleavage site motif (PEKQTR/GIF) and the absence of E627K and D701N mutations in PB2 protein classify the virus as a characteristic low pathogenicity AIV. However, several mutations in internal genes raise concerns about potential increases in viral resistance, virulence, and transmission in mammalian hosts. Overall, this study provides valuable insights into the molecular and genetic characterization of the emerging triple-reassortant H3N3 AIVs, and continued surveillance of domestic poultry is essential for monitoring the H3N3 subtype evolution and potential spread.
Bioefficacy of Sycanus reclinatus Stal Dohrn, (Hemiptera: Reduviidae) on the Euproctis fraterna Moore (Lepidoptera:Lymantriidae)
Authors: Muthupandi M , J Merline Shyla. , A Manimaran 
Published: September 2024

Abstract

Sycanus reclinatus Dohrn, (Hemiptera: Reduviidae) is a reduviid predator of many agricultural crop pests. A laboratory experiment was conducted to evaluate the bio efficacy of this predator using functional response as a tool. Sycanus reclinatus Dohrn third, fourth, and fifth instar nymphs and adults on Euproctis fraterna Moore were evaluated. The predatory rate gradually increased from the third instar nymph to adults. S. reclinatus killed and consumed more prey in high densities (4.5, 4.6, 6.0 and 4.5 prey/ predator). The rate of discovery gradually increased when the predator grew older. Handling time also increased when the predator grew older. S. reclinatus showed an exponential (Type II) functional response when E. fraterna was exposed to it. Type II functional response is more pronounced in pests. Such different patterns show that the predator needs to adapt distinct strategies according to the kind of prey available in the field. The biological control potential of S. reclinatus concerning the pest E. fraterna shows that the, searching time, handling time, and number of prey killed depended upon the prey densities and predator life stages. Hence, the predator S. reclinatus can be utilized to control and manage the selected lepidopteran pest.
Research on Optimization Grading Method of Manufactured sand Concrete Based on Particle Shape Image Representation
Authors: Pang Shanshan , Yang Yahan , Tang Wenhui , Wang Bingshu , Yu Zeming 
Published: September 2024

Abstract

It is crucial to optimize the proportioning parameters of manufactured sand concrete by combining particle shape images. This article adopts a research method combining theoretical analysis and physical experiments, using DIP method to obtain quantitative image characterization parameters of mechanism sand particle shape,study the mechanical performance laws of manufactured sand concrete affected by different characterization parameters, and obtain reasonable quantitative parameters of particle shape characterization and optimized mix proportion of manufactured sand concrete. The research results indicate that: (1) The DIP particle shape image method is used to accurately obtain the random parameters of dispersed manufactured sand, and the roundness, edge angle, and particle size ratio are used to characterize the particle shape distribution characteristics more accurately. Small particle size manufactured sand exhibits more uniform particle shape images. (2) Establishing an optimization model for compressive performance with water cement ratio, fly ash mineral powder admixture, and sand ratio as influencing factors, analyzing the changes in mechanical properties of Manufactured Sand concrete under the influence of influencing factors.(3) Comparative analysis shows that the water cement ratio has the most significant impact on the compressive strength of manufactured sand concrete, while the effect of fly ash and mineral powder admixture and sand ratio is relatively small. The optimal ratio is a water cement ratio of 0.32, mineral powder content of 20%, and sand ratio of 45%. The research results provide effective data support for the quantitative application of manufactured sand production.
Performance of soil total nitrogen monitoring model using optimal preprocessing combinations of in-situ and indoor spectra
Authors: Dai Yuanshui , Kong Yacong , Li Gang , Lv Xin 
Published: September 2024

Abstract

To explore the performance of in-situ spectral monitoring of soil total nitrogen, this study focused on cotton fields with different soil textures and sampled soil from 0–60 cm depth. Three different preprocessing combinations were applied to the indoor and in-situ spectra, and four modeling methods including the Generalized Regression Neural Network (GRNN), Random Forest Regression (RFR), Support Vector Machine Regression (SVR), and Ordinary Least Squares Regression were optimized using the Northern Goshawk Optimization (NGO) algorithm. The goal was to establish and select the best model for monitoring soil total nitrogen content in each soil layer. The results showed that: (1) Compared with no preprocessing, different preprocessing combinations improved the model accuracy by 0.19–0.49. The optimal preprocessing combination for the surface soil was First Derivative (FD) - Standard Normal Variate (SNV) - Z-score - Savitzky-Golay (SG), and for the medium and deep soil was FD - SNV - Continuum Removal (CR) - SG. (2) The optimized NGO-GRNN model outperformed the GRNN model, with an improvement of 60%, 12%, and 19% in R2 for the shallow, medium, and deep soil layers, respectively. (3) The model constructed using indoor spectra performed better than the in-situ spectra in monitoring soil total nitrogen content. However, the in-situ spectra-based models for different soil layers had an R2 greater than 0.6, indicating good monitoring performance and eliminating the laborious steps of indoor spectral processing. This study provides theoretical and technical support for rapid acquisition of nutrient information in various soil layers of cotton fields using in-situ spectral monitoring, demonstrating feasibility and robustness.
Unlocking disease associations during prefrontal cortex development with scRNAseq
Authors: Farley Kathryn Olivia , Chen Kevin G. , Lassmann Timo 
Published: September 2024

Abstract

Background
The brain consists of tissue comprising billions of neurons intricately connected through trillions of synapses. Throughout human development, from conception to adulthood, cells in the brain undergo significant changes, assembling functional circuitry over time orchestrated by finely tuned programs of gene expression. Understanding the spatiotemporal signalling that governs brain development and the impact of gene mutations on developmental programs remains a significant challenge. Here we identify the role of genetic variants in brain development to advance the development of therapeutic options.
Results
This computational study leverages developmental prefrontal cortex single-cell transcriptomic data to associate specific cell types with disease states. Using un-supervised techniques, we identify temporal gene expression patterns and gene co-expression networks enriched for genes associated with neurological disorders. By systematically applying these approaches we identify key cell types and developmental stages associated with disease emergence and progression
Conclusions
Our approach identifies important cell types and developmental stages relevant to diseases directly from single cell data. By pinpointing cells, genes, and their neighbourhoods, our approach has the potential to contribute to the discovery of new targets and treatment avenues. We hope our work will advance the management of brain-related disorders.
Assessing multi-hazard risk in Gulmidarbar Rural Municipality, Gulmi, Nepal
Authors: Tandan Sundar , Adhikari Basanta Raj , Karna Akhilesh Kumar 
Published: September 2024

Abstract

The occurrences of different kinds of natural hazards in the Nepal Himalaya is increasing in recent years. The complex interaction between these hazards is poorly understood. The multi-hazard mapping underscores the pivotal role of hazard maps in disaster risk reduction, advocating for creating a multi-hazard map despite associated challenges in the Gulmidarbar Rural Municipality. The Individual hazard maps for landslides, forest fires, floods, and earthquakes were generated. Rigorous validation processes, including field verification, were done to ensure the accuracy of individual hazard maps before integration into the comprehensive multi-hazard map using the Analytical Hierarchal Process (AHP). The vulnerability was calculated at the lowest administrative levels incorporating physical and socio-economic factors. The multi-hazard map is prepared by combining the ward-level hazard and vulnerability. The analysis shows that Ward No.4 has high risk followed by Ward No.1. This analysis is important to the mayor and other policymakers in decision-making, emergency response, and urban planning, aiming to develop robust strategies that minimize losses and enhance resilience in the studied region
From Feasibility to Insight: Piloting Feature Extraction from FHIR Cohorts to Advance Clinical Research
Authors: Rosenau Lorenz , Gruendner Julian , Behrend Paul , Triefenbach Lucas , Kurscheidt Maximilian , Majeed Raphael W. , Prokosch Hans-Ulrich , Ingenerf Josef 
Published: September 2024

Abstract

Background Interoperability between healthcare institutions and the standardized sharing of health data pose ongoing challenges. The Medical Informatics Initiative (MII) and the German Portal for Medical Research Data (FDPG) leverage the Fast Healthcare Interoperability Resources (FHIR) standard to address these issues. However, their capability for standardized and automated data extraction still needs to be added.Objective This research explores enhancing the FDPG's functionalities beyond its current scope of distributed feasibility studies (e.g., for cohort size estimations) within the existing MII framework. The focus is on extracting a subset of data represented in FHIR for specific cohorts aiming to uncover actionable insights from the health data repositories, thus extending the portal's utility beyond feasibility analyses.Methods We developed a prototype focusing on designing a user interface and implementing a local data extraction process. Based on a detailed comparison of existing data extraction tooling, we decided on the Pathling Server, chosen for the alignment of its capabilities with the problem space of data extraction and feasibility and potential as an all-in-one server solution for the FDPG architecture.Results We implemented a prototype that showcases the possibility of expanding the FDPG's feature set for local data extraction at clinical sites. Further, we were able to showcase its feasibility in providing researchers with means to extract CSV-formatted reports on specified cohorts based on a synthetic data set.Conclusion While a range of considerations are still required for extending the FDPG to support data extraction in a federated network, our work provides valuable insight. Namely, the value of providing an abstraction layer for researchers with an implicit translation to FHIR Path expressions and the benefit of a local CSV extraction. The approach of using Pathling requires staging project-specific data due to performance constraints. This poses privacy risks and should, therefore, be revisited. By presenting an early prototype, we hope to gather additional feedback from different stakeholders in the MII, including but not limited to clinical researchers, data stewards, and data privacy specialists.
The Genetic Landscape of Canine Tumors: Insights from the Canine Cancer Genome Atlas (CCGA)
Authors: Aresu Luca , Mazzone Eugenio , Marconato Laura , Fanelli Antonella , Cascione Luciano , Bertoni Francesco 
Published: September 2024

Abstract

The Canine Cancer Genome Atlas (https://caninecancergenomeatlas.org/) represents a groundbreaking advance in comparative oncology, offering an unprecedented wealth of genetic data across ten canine cancer types. This invaluable resource, encompassing 770 cases, reveals over 34,000 genetic variants, providing a comprehensive map of canine cancer genetics. By bridging the gap between canine and human oncology, the Canine Cancer Genome Atlas (CCGA) reveals shared oncogenic pathways and potential therapeutic targets. Key findings parallel human cancer genetics, with mutations in TP53, PIK3CA, and PTEN emerging as prominent features. The atlas also elucidates copy number alterations and mutational signatures associated with DNA repair deficiencies and carcinogen exposure. Through its user-friendly web platform, the CCGA equips researchers with state-of-the-art visualization and analysis tools, fostering collaboration and accelerating scientific discoveries. As the CCGA expands to incorporate multi-omics data, it promises to deepen understanding of cancer's molecular complexities. This indispensable resource not only advances veterinary oncology but also holds the potential to transform human cancer treatment, exemplifying the profound impact of comparative genomics in the battle against cancer.
compGWAS: a new GWAS tool allows revelation of the genetic architecture and risk stratification for the versatile pathogen Streptococcus pyogenes
Authors: Wang Peng-Ying , Liang Zhong , Chen Zhi-Song , Bao Yun-Juan , Castellino Francis J. 
Published: September 2024

Abstract

Background
Gene inactivations caused by loss-of-function mutations and regulatory changes caused by insertions/deletions (InDels) are common genetic factors closely related to phenotypic diversity or pathogenic versatility of many bacterial species. However, these genetic factors were usually ignored by the computational approach of genome-wide association studies (GWAS). It prevents the full understanding of the contributions of genetic variants to phenotypic diversity or the roles in shaping genetic architecture of bacterial species of diverse phenotypes. Group A Streptococcus pyogenes (GAS) is one of the most versatile pathogens causing a variety of primary diseases, as well as disease progressions, complications, and sequelae and is a promising species to do investigations in this regard.
Methods
By using GAS as a paradigm, we developed a new GWAS tool, compGWAS, to comprehensively identify phenotype-associated genetic variants that include not only SNPs, but also InDels and gene inactivations. The genetic architecture of GAS phenotypes was revealed by considering all these types of variants. A GWAS polygenic score (GPS) model was developed through integration of all types of associated variants for phenotype stratification.
Results
By leveraging this newly developed tool, we constructed a relationship network between 1,361 variants linked with 783 genes and eight GAS phenotypes. The network shows a high level of polygenicity of the GAS phenotypes (ranging from 6 to 148 genes) and pleiotropicity of the causal genes (as many as eight phenotypes). Further investigation revealed a unique genetic architecture of GAS phenotypes as a combination of many low-effect common variants and a small proportion of high-effect low-frequency variants with gene inactivations being predominant. By adding gene inactivations and InDels, the proportion of explained phenotypic variance increased by 7%-16%, resulting in a total explained variance as high as 50%. The high explained variance allowed us to construct a GPS model with high discriminatory capabilities in GAS phenotype stratification with the AUC > 80% in the validation dataset.
Conclusions
Our work provides a novel tool and analysis framework for investigating phenotypic effects and genetic characteristics of InDels and gene inactivations previously ignored. Our study has implications for understanding genetic architecture of versatile pathogens like GAS.