bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research Design
Authors: Pflücke Felix 
Published: July 2024

Abstract
This chapter outlines the research design of the monograph’s empirical investigation. It introduces the hypotheses and expected findings of the empirical inquiry and critically examines previous studies. It then discusses how it translates consumer rights and the factors potentially affecting compliance into dependent and independent variables. The chapter also deals with how it generated the data for the case study. In the next step, the chapter outlines the techniques for the data analysis, including the measurement of the levels of compliance, the statistical tools used, and the visualization of the findings. Finally, the chapter concludes by reflecting upon some of the methodological challenges of the empirical inquiry.
Shakti Chattopadhyay
Authors: Roy Sumana 
Published: July 2024

Abstract
Very few poets in the world wrote as obsessively about plant life as Shakti Chattopadhyay. Entering his writing through his biography, his life in Baharu and his moving to Calcutta, his nomadic education and professional life, his constant centrifugal desire to travel to forests in India, this chapter reads his poems closely to understand his world of philosophical botany, his obsession with roots, the autumnal forest, the privileging of leaves over flowers, the figures of the woodcutter and the gardener, the desire to enter a tree and live there, and how a tree’s giving is different from the human’s.
Satyajit Ray
Authors: Roy Sumana 
Published: July 2024

Abstract
Beginning with Upendrakishore Ray Chowdhury and Sukumar Ray, Satyajit Ray’s grandfather and father, and their stories and poems about fantastic and nonsense plant kingdoms, how that impacted children’s literature in Bengal, this chapter moves to the filmmaker’s memoir, particularly his childhood, his acquaintance with the trees of Darbhanga, the note that Rabindranath is said to have written in the little boy’s notebook. It must have been all of these that produced the fantastic nerve in Satyajit Ray’s stories about plant life—man-eating trees, murderous flowers, plants that can cure depression, the green man, and, of course, the Professor Shanku stories.
CloudATAC: a cloud-based framework for ATAC-Seq data analysis
Authors: Veerappa Avinash M , Rowley M Jordan , Maggio Angela , Beaudry Laura , Hawkins Dale , Kim Allen , Sethi Sahil , Sorgen Paul L , Guda Chittibabu 
Published: July 2024

Abstract
Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis.
This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
A cloud-based learning module for biomarker discovery
Authors: Hemme Christopher L , Beaudry Laura , Yosufzai Zelaikha , Kim Allen , Pan Daniel , Campbell Ross , Price Marcia , Cho Bongsup P 
Published: July 2024

Abstract
This manuscript describes the development of a resource module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning” https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84).
This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
Identifying and training deep learning neural networks on biomedical-related datasets
Authors: Woessner Alan E , Anjum Usman , Salman Hadi , Lear Jacob , Turner Jeffrey T , Campbell Ross , Beaudry Laura , Zhan Justin , Cornett Lawrence E , Gauch Susan , Quinn Kyle P 
Published: July 2024

Abstract
This manuscript describes the development of a resources module that is part of a learning platform named ‘NIGMS Sandbox for Cloud-based Learning’ https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks.
This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction
Authors: Fang Yitian , Luo Mingshuang , Ren Zhixiang , Wei Leyi , Wei Dong-Qing 
Published: July 2024

Abstract
Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP.
Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs
Authors: Tan Xiaorong , Liu Qianhui , Fang Yanpeng , Yang Sen , Chen Fei , Wang Jianmin , Ouyang Defang , Dong Jie , Zeng Wenbin 
Published: July 2024

Abstract
Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.
Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics
Authors: Jiang Rui , Huang Wentao , Qiu Xinqi , Chen Jianyi , Luo Ruibang , Zeng Ruijie , Tong Shuangshuang , Lyu Yanlin , Sun Panpan , Lian Qizhou , Leung Felix W , Liu Yufeng , Sha Weihong , Chen Hao 
Published: July 2024

Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which current treatments are limited and drug development costs are prohibitive. Identifying drug targets for ASD is crucial for the development of targeted therapies. Summary-level data of expression quantitative trait loci obtained from GTEx, protein quantitative trait loci data from the ROSMAP project, and two ASD genome-wide association studies datasets were utilized for discovery and replication. We conducted a combined analysis using Mendelian randomization (MR), transcriptome-wide association studies, Bayesian colocalization, and summary-data-based MR to identify potential therapeutic targets associated with ASD and examine whether there are shared causal variants among them. Furthermore, pathway and drug enrichment analyses were performed to further explore the underlying mechanisms and summarize the current status of pharmacological targets for developing drugs to treat ASD. The protein–protein interaction (PPI) network and mouse knockout models were performed to estimate the effect of therapeutic targets. A total of 17 genes revealed causal associations with ASD and were identified as potential targets for ASD patients. Cathepsin B (CTSB) [odd ratio (OR) = 2.66 95, confidence interval (CI): 1.28–5.52, P = 8.84 × 10−3], gamma-aminobutyric acid type B receptor subunit 1 (GABBR1) (OR = 1.99, 95CI: 1.06–3.75, P = 3.24 × 10−2), and formin like 1 (FMNL1) (OR = 0.15, 95CI: 0.04–0.58, P = 5.59 × 10−3) were replicated in the proteome-wide MR analyses. In Drugbank, two potential therapeutic drugs, Acamprosate (GABBR1 inhibitor) and Bryostatin 1 (CASP8 inhibitor), were inferred as potential influencers of autism. Knockout mouse models suggested the involvement of the CASP8, GABBR1, and PLEKHM1 genes in neurological processes. Our findings suggest 17 candidate therapeutic targets for ASD and provide novel drug targets for therapy development and critical drug repurposing opportunities.
Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types
Authors: Wang Sheng , Stroup Emily K , Wang Ting-You , Yang Rendong , Ji Zhe 
Published: July 2024

Abstract
Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells’ transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein–protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells’ intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.
The activity of thermostable NiO/CeO2 heterointerface structure toward low-temperature catalytic CO–NO reaction
Authors: Moushumy Zannatul Mumtarin , Yoshida Hiroshi , Tokusada Kaori , Kuraoka Ai , Sakamoto Sota , Tsushida Masayuki , Machida Masato 
Published: July 2024

Abstract
The surface grafting of NiO onto CeO2 nanocrystallites generates heterointerface structures, providing efficient active sites for CO–NO reactions toward forming N2 and CO2. In this study, we investigated the effects of high-temperature thermal aging on the activity and nanostructure of the NiO/CeO2 catalyst. After thermal aging at 900 °C, the catalyst retained a high catalytic activity, whereas the reference catalysts lost theirs owing to considerable solid-state reactions and sintering. The as-prepared fresh NiO/CeO2 catalyst (calcined at 600 °C) contained high dispersions of NiO species in CeO2 crystallites. Conversely, the thermally aged catalyst comprised grown NiO and CeO2 crystallites were allowed to contact intimately to form thermostable interfaces, where the perimeter in the vicinity provided the Ni2+-incorporated CeO2 surface for removing and filling the oxygen species in the catalytic cycle toward facilitating CO–NO reactions. Based on in situ Fourier transform infrared and parallel isotopic reaction analyses, we confirmed the following as possible pathways: (i) the removal of the surface oxygen by the adsorbed CO to form an oxygen vacancy (VO), (ii) the interaction between the adsorbed NO with VO, and (iii) the N–O bond cleavage and the reaction with CO to form isocyanate, followed by the reaction with NO to produce N2.
A Bargaining Theory of US–China Economic Rivalry: Differentiating the Trade and Technology Wars
Authors: Wu Cathy Xuauxuan 
Published: July 2024

Abstract
This article examines the outbreak and persistence of US–China economic war, which comprises both the trade war, featured with retaliatory tariffs, and the technology war, featured with restrictions on Chinese access to US technologies. Building on the analytical framework of bargaining and war, I argue that different components of the economic war emerged from distinct causes. The outbreak of the trade war was primarily driven by the information problem, characterized by mutual uncertainty and the lack of effective communications. The technology war was largely a result of the commitment problem driven by the existing power’s concern regarding potential future changes in the balance of power. After examining the initiation stage during the Trump era, I further analyze how the economic war has unfolded during the Biden administration. While the prospect of a new trade war seems unlikely as mutual uncertainty diminishes, existing tariffs remain as the commitment problem on trade issues has become more critical. The preventive technology war is expected to persist, reflecting Washington’s ongoing concerns over China’s growing leadership in technology.
Temporal trends in mortality, heart failure hospitalization, and stroke in heart failure patients with and without atrial fibrillation: a nationwide study from 1997 to 2018 on 152 059 patients
Authors: Austreim Marte , Nouhravesh Nina , Malik Mariam E , Abassi Noor , Zahir Deewa , Garred Caroline Hartwell , Andersen Camilla F , Hansen Morten Lock , Olesen Jonas Bjerring , Fosbøl Emil , Østergaard Lauge , Køber Lars , Schou Morten 
Published: July 2024

Abstract

Aims
We aimed to investigate temporal trends in all-cause mortality, heart failure (HF) hospitalization, and stroke from 1997 to 2018 in patients diagnosed with both HF and atrial fibrillation (AF).

Methods and results
From Danish nationwide registers, we identified 152 059 patients with new-onset HF between 1997 and 2018. Patients were grouped according to year of new-onset HF and AF-status: Prevalent AF (n = 34 734), New-onset AF (n = 12 691), and No AF (n = 104 634). Median age decreased from 76 to 73 years between 1997 and 2018. The proportion of patients with prevalent or new-onset AF increased from 24.7% (n = 9256) to 35.8% (n = 14 970). Five-year risk of all-cause mortality went from 69.1% [confidence interval (CI): 67.9–70.2%] to 51.3% (CI: 49.9–52.7%), 62.3% (CI: 60.5–64.4%) to 43.0% (CI: 40.5–45.5%), and 61.9% (CI: 61.3–62.4%) to 36.7% (CI: 35.9–37.6%) for the Prevalent AF, New-onset AF, and No AF-group, respectively. Minimal changes were observed in the risk of HF-hospitalization. Five-year stroke risk decreased from 8.5% (CI: 7.8–9.1%) to 5.0% (CI: 4.4–5.5%) for the prevalent AF group, 8.2% (CI: 7.2–9.2%) to 4.6% (CI: 3.7–5.5%) for new-onset AF, and 6.3% (CI: 6.1–6.6%) to 4.9% (CI: 4.6–5.3%) for the No AF group. Simultaneously, anticoagulant therapy increased for patients with prevalent (from 42.7 to 93.1%) and new-onset AF (from 41.9 to 92.5%).

Conclusion
From 1997 to 2018, we observed an increase in patients with HF and co-existing AF. Mortality decreased for all patients, regardless of AF-status. Anticoagulation therapy increased, and stroke risk for patients with AF was reduced to a similar level as patients without AF in 2013–2018.
Microbial remineralization processes during postspring-bloom with excess phosphate available in the northern Baltic Sea
Authors: Vanharanta Mari , Santoro Mariano , Villena-Alemany Cristian , Piiparinen Jonna , Piwosz Kasia , Grossart Hans-Peter , Labrenz Matthias , Spilling Kristian 
Published: July 2024

Abstract
The phosphorus (P) concentration is increasing in parts of the Baltic Sea following the spring bloom. The fate of this excess P-pool is an open question, and here we investigate the role of microbial degradation processes in the excess P assimilation phase. During a 17-day-long mesocosm experiment in the southwest Finnish archipelago, we examined nitrogen, phosphorus, and carbon acquiring extracellular enzyme activities in three size fractions (&lt;0.2, 0.2–3, and &gt;3 µm), bacterial abundance, production, community composition, and its predicted metabolic functions. The mesocosms received carbon (C) and nitrogen (N) amendments individually and in combination (NC) to distinguish between heterotrophic and autotrophic processes. Alkaline phosphatase activity occurred mainly in the dissolved form and likely contributed to the excess phosphate conditions together with grazing. At the beginning of the experiment, peptidolytic and glycolytic enzymes were mostly produced by free-living bacteria. However, by the end of the experiment, the NC-treatment induced a shift in peptidolytic and glycolytic activities and degradation of phosphomonoesters toward the particle-associated fraction, likely as a consequence of higher substrate availability. This would potentially promote retention of nutrients in the surface as opposed to sedimentation, but direct sedimentation measurements are needed to verify this hypothesis.