bluuebunny/crossref_metadata_2025_split huggingface dataset:
Butter
Authors: Kelly Alan , Fox Patrick , Cogan Tim 
Published: December 2024

Abstract
One of the oldest dairy products in many countries is butter—e.g., its production in Ireland has been traced back to several centuries bc—because it is a simple and effective way of preserving the key energy-rich component of milk—fat—for storage and consumption in times of scarcity. This chapter describes the principles of making butter (separation of cream, by gravity or mechanically, churning, separation of butter grains from buttermilk, and salting if applied, before molding and storage), the factors affecting spreadability, one of its most important properties for consumers, and the production of lactic butter, a variety common in continental European countries. The chapter also considers the emergence of alternative products to butter, such as spreads in which some of the milk fat is replaced by softer and perhaps healthier plant fats and oils, as well as the key principles of formulating and producing such products.
Concentrated and Dried Dairy Products
Authors: Kelly Alan , Fox Patrick , Cogan Tim 
Published: December 2024

Abstract
One of the most effective ways to extend the shelf-life of milk is to remove some or all of the water; dried forms of milk such as skimmed milk and whole milk powders are major commodities traded internationally. This chapter explains how these are produced, with the help of simple diagrams. It describes the steps in the process, typically evaporation to concentrate the milk solids followed by drying, originally in a process called roller-drying, but today, in most cases, through a highly efficient process called spray-drying. It explains the importance of ensuring that any powder can later easily be returned to a fluid state by mixing with water, as well as the factors affecting this. Finally, it explores the products that are made by concentrating milk without drying (evaporated or condensed milk and sweetened condensed milk), including their production and properties.
Ice Cream
Authors: Kelly Alan , Fox Patrick , Cogan Tim 
Published: December 2024

Abstract
This chapter considers ice cream, one of the most popular dairy products in many countries, which has a long history stretching back to Marco Polo. It outlines the production of high-quality ice cream, which depends on a combination of factors, including the careful selection of ingredients (cream, sugar, emulsifiers, stabilizers, and flavors) and appropriate processing, including heating, homogenization, and freezing. It then describes the influence of each of these factors on the properties of ice cream, especially the role of sugar in controlling the freezing properties of water to ensure that the final product does not become too hard. Finally, the chapter reviews the role of stabilizers in maintaining the quality of ice cream during prolonged frozen storage.
Challenges in the Modern Dairy Sector
Authors: Kelly Alan , Fox Patrick , Cogan Tim 
Published: December 2024

Abstract
Despite the very long history of dairy products, and their high level of consumption across the world today, the future of dairy is perhaps under more threat now than at any other point in history, with emerging concerns about health, ethical and environmental impacts of dairy production, and the rise of veganism and the associated desire for dairy alternatives. Some of these concerns are explored in this chapter, alongside some trends in the way in which dairy products are produced, as influenced by factors such as consumer desires for less processed foods, to try and end the book with a sense of the future of the dairy sector.
Impact of frailty, malnutrition and socioeconomic status on perioperative outcomes
Authors: Stretton Brandon , Booth Andrew E C , Kovoor Joshua , Gupta Aashray , Edwards Suzanne , Hugh Tom , Maddison John , Talley Nicholas J , Plummer Mark , Meyer Emily , Horowitz Michael , Barreto Savio , Padbury Robert , Bacchi Stephen , Maddern Guy , Boyd Mark 
Published: December 2024

Abstract

Background
Frailty, malnutrition and low socioeconomic status may mutually perpetuate each other in a self-reinforcing and interdependent manner. The intertwined nature of these factors may be overlooked when investigating impacts on perioperative outcomes. This study aimed to investigate the impact of frailty, malnutrition and socioeconomic status on perioperative outcomes.

Methods
A multicentre cohort study involving six Australian tertiary hospitals was undertaken. All consecutive surgical patients who underwent an operation were included. Frailty was defined by the Hospital Frailty Risk Score, malnutrition by the Malnutrition Universal Screening Tool (MUST) and low socioeconomic status by the Index of Relative Socioeconomic Disadvantage. Linear mixed-effects and binary logistic generalised estimated equation models were performed for the outcomes: inpatient mortality, length of stay, 30-day readmission and re-operation.

Results
A total of 21 976 patients were included. After controlling for confounders, malnutrition and socioeconomic status, patients at high risk of frailty have a mean hospital length of stay 3.46 times longer (mean ratio = 3.46; 95% confidence interval (CI): 3.20, 3.73; P value &lt; .001), odds of 30-day readmission 2.4 times higher (odds ratio = 2.40; 95% CI: 2.19, 2.63; P value &lt; .001) and odds of in-hospital mortality 12.89 times greater than patients with low risk of frailty (odds ratio = 12.89; 95% CI: 4.51, 36.69; P value &lt; .001). Elevated MUST scores were also significantly associated with worse outcomes, but to a lesser extent. Socioeconomic status had no association with outcomes.

Conclusion
Perioperative risk evaluation should consider both frailty and malnutrition as separate, significant risk factors. Despite strong causal links with frailty and malnutrition, socioeconomic disadvantage is not associated with worse postoperative outcomes. Additional studies regarding the prospective identification of these patients with implementation of strategies to mitigate frailty and malnutrition and assessment of perioperative risk are required.
Body size and occupational physical activity during the life course and gastrointestinal cancers
Authors: Sassano Michele , Nalini Mahdi , Seyyedsalehi Monireh Sadat , Roshandel Gholamreza , Poustchi Hossein , Khoshnia Masoud , Kamangar Farin , Dawsey Sanford M , Abnet Christian , Malekzadeh Reza , Boffetta Paolo , Etemadi Arash 
Published: December 2024

Abstract
Evidence on the associations of body size and occupational physical activity (PA) during the life course with gastrointestinal cancers is inconclusive.
We analyzed data from the Golestan Cohort Study, a population-based prospective cohort study from Iran, with 50,045 individuals aged 40-75 years enrolled during 2004-2008 and followed through April 2023. Body size during adolescence, early adulthood, and at cohort baseline was assessed using validated pictograms.
Large adolescent body size was associated with total gastrointestinal cancers (hazard ratio, HR: 1.09; 95% confidence interval, CI: 1.02-1.16) and liver cancer (HR: 1.49; 95% CI: 1.11-2.01). Large early-adulthood body size was associated with liver cancer (HR: 1.85; 95% CI: 1.35-2.52).
Compared with normal weight during all stages, overweight or obesity during adolescence only or early adulthood only were associated with stomach cancer, esophageal squamous cell carcinoma, and liver cancer.
Lower levels of adolescent occupational PA were associated with colon cancer (HR: 1.39; 95% CI: 1.10-1.75), while lower levels at cohort baseline were positively associated with colon cancer and inversely associated with rectal cancer.
Our findings suggest that timing of obesity during the life course might differently affect risk of gastrointestinal cancers, while occupational PA was found to be associated with colon cancer only
Family criminal legal system exposure and early adolescents’ pubertal development: The mediating role of family strain
Authors: Del Toro Juan , Roettger Michael , Jackson Dylan B , Wilson Sylia 
Published: December 2024

Abstract
Pubertal trends, wherein adolescents today are experiencing puberty earlier than prior generations, have coincided with the expansion of the criminal legal system, which is disproportionately impacting communities of color. However, whether pubertal development and criminal legal system exposure among adolescents are inter-related is unknown. We tested whether family members’ criminal legal system exposure predicted adolescents’ pubertal development, whether family strain explained the relation between criminal legal system exposure and pubertal development, and whether race/ethnicity moderated our results. We used three yearly waves of longitudinal data among a national sample of 9,518 adolescents. Results illustrated that 40% of Black, 20% of Latinx, 16% of Other, and 10% of White adolescents experienced one or more family criminal legal system exposures. In structural equation models within a case-crossover design controlling for measured confounders and unmeasured confounders that do not change over time, including neighborhood-level socioeconomic status and crime, family criminal legal system exposure predicted adolescents’ advanced pubertal development, and family strain explained this relation between family criminal legal system exposure and pubertal development. The United States’ approach to law and order has public health implications that may be perpetuating health inequities, as accelerated pubertal development can have downstream consequences across the life course.
Root growth control by negative regulation of MYB50 under ABA signaling in Arabidopsis
Authors: Mase Kosuke , Kamiya Yukino , Sakaoka Satomi , Morikami Atsushi , Tsukagoshi Hironaka 
Published: December 2024

Abstract
Plant growth is finely tuned by environmental changes, with abscisic acid (ABA) playing a key role in balancing stress tolerance and growth regulation. The target genes of MYB50, which regulate root growth, include genes that respond to ABA; however, the precise role of MYB50 in ABA signaling remains unclear. Therefore, this study aimed to elucidate the function of MYB50 under ABA signaling. Our experiments demonstrated that ABA treatment reduced MYB50 expression and promoted the degradation of MYB50 protein. This degradation alleviates the inhibitory effects of MYB50 on root growth. Furthermore, ABA differentially regulates MYB50 compared with ABI5, another key transcription factor involved in root growth under ABA signaling, suggesting that ABA uses distinct regulatory pathways for root growth. Our study suggests that ABA controls root growth by modulating MYB50 at both the transcriptional and post-translational levels, thus ensuring balanced root development in response to ABA.
Screening of novel lactic acid bacteria with high induction of immunoglobulin A production, dendritic cell activation, and interleukin-12 production
Authors: Tsuruno Keigo , Mawatari Takashi , Tanaka-Azuma Yukimasa , Yamatsu Atsushi , Tanabe Soichi 
Published: December 2024

Abstract
In this study, we screened 308 lactic acid bacteria strains for high immunoglobulin A (IgA) production, dendritic cell activation, and interleukin-12 (IL-12) production using human-derived cells. Among them, Lactobacillus helveticus GCL1815 demonstrated superior performance in all aspects, indicating its remarkable potential for immunomodulatory functions in both innate and adaptive immunity.
Effects of experimental nest treatment with herbs on ectoparasites and body condition of nestlings
Authors: Glądalski Michał , Norte Ana Cláudia , Bartos Maciej , Demeško Iwona , Kaliński Adam , Markowski Marcin , Skwarska Joanna , Wawrzyniak Jarosław , Zieliński Piotr , Bańbura Jerzy 
Published: December 2024

Abstract
Nest fumigation behavior involves the incorporation of fresh green plant fragments that contain ectoparasite-repellent volatile compounds into birds’ nests. This behavior is relatively rare among bird species, and there is ongoing debate about whether it benefits parental breeding success. In this study, we experimentally tested whether the inclusion of aromatic-herbal plant fragments in the nests of great tits Parus major affects the physiological condition of nestlings, as indicated by blood levels of hematocrit, hemoglobin, glucose, and body condition indices, such as weight and wing length. We divided the nests into 2 groups, adding aromatic herbs to the test group’s nests and non-aromatic plants to the control group. After the nestlings fledged, all nest materials were collected to extract, identify, and count arthropod ectoparasites. Nestlings in nests supplemented with aromatic plant fragments had elevated levels of hematocrit and hemoglobin, indicating improved physiological condition compared to the control group. Ectoparasites were present in both groups, although ticks (Ixodidae) occurred less frequently in nests with aromatic plants. The experimental treatment did not affect fledging success. Further experimental studies are needed to explore the effects of incorporating aromatic plant fragments into tit nests within the frameworks of both the nest protection hypothesis and the drug hypothesis.
BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees
Authors: Ormond Cathal , Ryan Niamh M , Cap Mathieu , Byerley William , Corvin Aiden , Heron Elizabeth A 
Published: December 2024

Abstract
Next-generation sequencing is widely applied to the investigation of pedigree data for gene discovery. However, identifying plausible disease-causing variants within a robust statistical framework is challenging. Here, we introduce BICEP: a Bayesian inference tool for rare variant causality evaluation in pedigree-based cohorts. BICEP calculates the posterior odds that a genomic variant is causal for a phenotype based on the variant cosegregation as well as a priori evidence such as deleteriousness and functional consequence. BICEP can correctly identify causal variants for phenotypes with both Mendelian and complex genetic architectures, outperforming existing methodologies. Additionally, BICEP can correctly down-weight common variants that are unlikely to be involved in phenotypic liability in the context of a pedigree, even if they have reasonable cosegregation patterns. The output metrics from BICEP allow for the quantitative comparison of variant causality within and across pedigrees, which is not possible with existing approaches.
Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN
Authors: Xue Shuailin , Zhu Fangfang , Chen Jinyu , Min Wenwen 
Published: December 2024

Abstract
Spatial transcriptomics (ST technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization, and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer and Graph Convolutional Network that integrates histological images, spot-based ST data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately and aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations. Our source code and all used datasets are available at https://github.com/wenwenmin/scstGCN and https://zenodo.org/records/12800375.
Multi-kernel feature extraction with dynamic fusion and downsampled residual feature embedding for predicting rice RNA N6-methyladenine sites
Authors: Liu Mengya , Sun Zhan-Li , Zeng Zhigang , Lam Kin-Man 
Published: December 2024

Abstract
RNA N$^{6}$-methyladenosine (m$^{6}$A) is a critical epigenetic modification closely related to rice growth, development, and stress response. m$^{6}$A accurate identification, directly related to precision rice breeding and improvement, is fundamental to revealing phenotype regulatory and molecular mechanisms. Faced on rice m$^{6}$A variable-length sequence, to input into the model, the maximum length padding and label encoding usually adapt to obtain the max-length padded sequence for prediction. Although this can retain complete sequence information, resulting in sparse information and invalid padding, reducing feature extraction accuracy. Simultaneously, existing rice-specific m$^{6}$A prediction methods are still at an early stage. To address these issues, we develop a new end-to-end deep learning framework, MFDm$^{6}$ARice, for predicting rice m$^{6}$A sites. In particular, to alleviate sparseness, we construct a multi-kernel feature fusion module to mine essential information in max-length padded sequences by multi-kernel feature extraction function and effectively transfer information through global–local dynamic fusion function. Concurrently, considering the complexity and computational efficiency of high-dimensional features caused by invalid padding, we design a downsampling residual feature embedding module to optimize feature space compression and achieve accurate feature expression and efficient computational performance. Experiments show that MFDm$^{6}$ARice outperforms comparison methods in cross-validation, same- and cross-species independent test sets, demonstrating good robustness and generalization. The application on maize m$^{6}$A indicates the MFDm$^{6}$ARice’s scalability. Further investigations have shown that combining different kernel features, focusing on global channel-local spatial, and employing reasonable downsampling and residual connections can improve feature representation and extraction, ensure effective information transfer, and significantly enhance model performance.
Polyphonia: detecting inter-sample contamination in viral genomic sequencing data
Authors: Krasilnikova Lydia A , Tomkins-Tinch Christopher H , Gayton Alton C , Schaffner Stephen F , Dobbins Sabrina T , Gladden-Young Adrianne , Siddle Katherine J , Park Daniel J , Sabeti Pardis C 
Published: December 2024

Abstract

Summary
In viral genomic research and surveillance, inter-sample contamination can affect variant detection, analysis of within-host evolution, outbreak reconstruction, and detection of superinfections and recombination events. While sample barcoding methods exist to track inter-sample contamination, they are not always used and can only detect contamination in the experimental pipeline from the point they are added. The underlying genomic information in a sample, however, carries information about inter-sample contamination occurring at any stage. Here, we present Polyphonia, a tool for detecting inter-sample contamination directly from deep sequencing data without the need for additional controls, using intrahost variant frequencies. We apply Polyphonia to 1102 SARS-CoV-2 samples sequenced at the Broad Institute and already tracked using molecular barcoding for comparison.

Availability and implementation
Polyphonia is available as a standalone Docker image and is also included as part of viral-ngs, available in Dockstore. Full documentation, source code, and instructions for use are available at https://github.com/broadinstitute/polyphonia.
HBFormer: a single-stream framework based on hybrid attention mechanism for identification of human-virus protein–protein interactions
Authors: Zhang Liyuan , Wang Sicong , Wang Yadong , Zhao Tianyi 
Published: December 2024

Abstract

Motivation
Exploring human-virus protein–protein interactions (PPIs) is crucial for unraveling the underlying pathogenic mechanisms of viruses. Limitations in the coverage and scalability of high-throughput approaches have impeded the identification of certain key interactions. Current popular computational methods adopt a two-stream pipeline to identify PPIs, which can only achieve relation modeling of protein pairs at the classification phase. However, the fitting capacity of the classifier is insufficient to comprehensively mine the complex interaction patterns between protein pairs.

Results
In this study, we propose a pioneering single-stream framework HBFormer that combines hybrid attention mechanism and multimodal feature fusion strategy for identifying human-virus PPIs. The Transformer architecture based on hybrid attention can bridge the bidirectional information flows between human protein and viral protein, thus unifying joint feature learning and relation modeling of protein pairs. The experimental results demonstrate that HBFormer not only achieves superior performance on multiple human-virus PPI datasets but also outperforms 5 other state-of-the-art human-virus PPI identification methods. Moreover, ablation studies and scalability experiments further validate the effectiveness of our single-stream framework.

Availability and implementation
Codes and datasets are available at https://github.com/RmQ5v/HBFormer.
DPAM-AI: a domain parser for AlphaFold models powered by artificial intelligence
Authors: Durham Jesse , Zhang Jing , Schaeffer Richard D , Cong Qian 
Published: December 2024

Abstract

Motivation
Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering nearly the entire protein universe. Segmenting these models into domains and classifying them into an evolutionary hierarchy hold tremendous potential for unraveling essential insights into protein function.

Results
We introduce DPAM-AI, a Domain Parser for AlphaFold Models based on Artificial Intelligence. DPAM-AI utilizes a convolutional neural network trained with previously classified domains in the Evolutionary Classification Of protein Domains (ECOD) database. DPAM-AI integrates inter-residue distances, predicted aligned errors, and sequence and structural alignments to previously classified domains detected via sequence (HHsuite) and structural (Dali) similarity searches. DPAM-AI has demonstrated its power through rigorous tests, excelling in several benchmark sets compared to its predecessor, DPAM, and other recently published domain parsers, Merizo and Chainsaw. We applied DPAM-AI to representative AFDB models for proteins classified in Pfam. We obtained representative 3D structures for 18 487 (89%) of the 20 795 Pfam families. The remaining families either (i) belong to viral proteins that were excluded from AFDB or (ii) do not adopt globular 3D structures. Our structure-aware domain delineation uncovered a considerable fraction (15%) of Pfam domains containing multiple structural and evolutionary units and refined the boundaries for over half.

Availability and implementation
Pfam and corresponding DPAM-AI domains are at http://prodata.swmed.edu/DPAM-pfam/. Our code is deposited at https://github.com/Jsauce5p/DPAM/tree/dpam_ai, and updates will be released through https://github.com/CongLabCode/DPAM.