bluuebunny/crossref_metadata_2025_split huggingface dataset:
Revolutionary Emotions
Authors: Toska Silvana 
Published: September 2024

Abstract
Revolutionary Emotions: The Emotional Roots of Revolutionary Waves proposes a novel emotional theory of revolutionary diffusion and success and argues that emotional mobilization is at the root of both. For centuries, revolutionaries have spoken of the emotional arousal—the anger, the hope, and the jettisoning of fear—that motivated their participation in revolt. Studies of revolutions, however, rarely give these emotional narratives the power that actors themselves recount. This book argues that revolutionary waves, including the 1848 revolts and the Arab Uprisings, cannot be explained without the emotional arousal that motivated potential revolutionaries to imitate neighboring revolts. The shared identity of revolutionaries across borders leads to a shared emotional arousal and adoption of protest frames and methods. Absent this shared identity, revolutions are unlikely to cause sufficient emotional resonance to spur imitators across borders. In addition to a shared identity, previous history of protests makes diffusion more likely, as seasoned activists capitalize on this emotional jolt to mobilize against their regimes. The emotional power of the crowds is tangible—if difficult to measure—and influences the decisions of key elite players, including the military, on whether to continue supporting the regime. It is emotional arousal that both conditions the regional diffusion of revolt and determines the strength of mobilization and potential for revolutionary success. By grounding theory in the emotional narratives of revolutionaries and by breaking down the various dichotomies that plague the study of revolutions—structure versus agency, domestic versus international—Revolutionary Emotions provides the first coherent theory of revolutionary diffusion and success.
Introduction
Authors: Toska Silvana 
Published: September 2024

Abstract
This introduction examines the question of why some revolts diffuse across borders and why some of them succeed in becoming revolutions, a puzzle without a clear answer in the existing literature. The chapter then explores the role of emotions in revolutionary diffusion; provides an analysis of existing explanations on revolts, revolutions, emotions, rationality, social movements, military behavior in revolutionary settings, and revolutionary diffusion, and establishes the necessity for a coherent theory; it provides a brief overview of the emotional theory of the diffusion of revolt and revolutionary success; it establishes the theory’s strengths and limitations; it elaborates on the interview-based research and cross-national analysis that test this theory; and it provides an overview of the rest of the book.
Theory
Authors: Toska Silvana 
Published: September 2024

Abstract
This chapter builds on theories of the causes of revolution and studies of diffusion to devise an emotional theory of the spread and success of revolt. Because emotions are rarely theorized, it first defines emotions, explains which emotions are mobilizing and which ones have paralyzing tendencies, and describes how we can study them during episodes of contention. It further examines the relationship between emotions, cognitive liberation, and identity and shows how, because of its ability to engender an emotional reaction, identity determines the limits of where revolutionary movements are likely to spread. The final section of the theory outlines the domestic conditions that allow these movements to spread and succeed: namely, that preexisting opportunity structures are essential to mobilization, and that the relationship between protesters and the military and the military’s evaluation of the emotional strength of the protesters greatly affect the success of revolts.
Why Revolts Spread and Often Succeed
Authors: Toska Silvana 
Published: September 2024

Abstract
This chapter presents a cross-national study that first tests whether diffusion follows on from a revolution in an identity neighbor and then, for revolts between 1945 and 2013, measures the effects of revolt size and nature on the likelihood of success. It tests the relationship between neighborhood revolt and domestic opportunity structures on the probability of revolt in the home country. The chapter shows how a revolt in an identity neighbor greatly increases the likelihood that revolt will spread, especially so when the home country has preexisting opportunity structures. It also tests the relationship between the size and nature of the opposition on the probability of revolt success. This cross-national analysis shows a clear relationship between an identity neighbor revolt and spread of revolt, and the size and nature of protests on their success.
learnMSA2: deep protein multiple alignments with large language and hidden Markov models
Authors: Becker Felix , Stanke Mario 
Published: September 2024

Abstract

Motivation
For the alignment of large numbers of protein sequences, tools are predominant that decide to align two residues using only simple prior knowledge, e.g. amino acid substitution matrices, and using only part of the available data. The accuracy of state-of-the-art programs declines with decreasing sequence identity and when increasingly large numbers of sequences are aligned. Recently, transformer-based deep-learning models started to harness the vast amount of protein sequence data, resulting in powerful pretrained language models with the main purpose of generating high-dimensional numerical representations, embeddings, for individual sites that agglomerate evolutionary, structural, and biophysical information.

Results
We extend the traditional profile hidden Markov model so that it takes as inputs unaligned protein sequences and the corresponding embeddings. We fit the model with gradient descent using our existing differentiable hidden Markov layer. All sequences and their embeddings are jointly aligned to a model of the protein family. We report that our upgraded HMM-based aligner, learnMSA2, combined with the ProtT5-XL protein language model aligns on average almost 6% points more columns correctly than the best amino acid-based competitor and scales well with sequence number. The relative advantage of learnMSA2 over other programs tends to be greater when the sequence identity is lower and when the number of sequences is larger. Our results strengthen the evidence on the rich information contained in protein language models’ embeddings and their potential downstream impact on the field of bioinformatics.
Availability and implementation: https://github.com/Gaius-Augustus/learnMSA, PyPI and Bioconda, evaluation: https://github.com/felbecker/snakeMSA
Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Authors: Song Tianci , Cosatto Eric , Wang Gaoyuan , Kuang Rui , Gerstein Mark , Min Martin Renqiang , Warrell Jonathan 
Published: September 2024

Abstract

Motivation
Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.

Results
Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest.

Availability and implementation
https://github.com/song0309/asGNN/
CIBRA identifies genomic alterations with a system-wide impact on tumor biology
Authors: Lakbir Soufyan , Buranelli Caterina , Meijer Gerrit A , Heringa Jaap , Fijneman Remond J A , Abeln Sanne 
Published: September 2024

Abstract

Motivation
Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g. genomics), and another defining a system-wide expression response (e.g. transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation.

Results
We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes (0.79 AUC). Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely under-reported. Additionally, CIBRA can identify impact with only 10 cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response.

Availability and implementation
The R package CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA.
Metagenomic functional profiling: to sketch or not to sketch?
Authors: Hera Mahmudur Rahman , Liu Shaopeng , Wei Wei , Rodriguez Judith S , Ma Chunyu , Koslicki David 
Published: September 2024

Abstract

Motivation
Functional profiling of metagenomic samples is essential to decipher the functional capabilities of microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general, k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented in sourmash, a publicly available software), a k-mer-sketching algorithm, to obtain functional profiles of metagenome samples.

Results
We show how pieces of the sourmash software (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample. We named our pipeline fmh-funprofiler. We report that the functional profiles obtained using this pipeline demonstrate comparable completeness and better purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. We also report that fmh-funprofiler is 39–99× faster in wall-clock time, and consumes up to 40–55× less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets.

Availability and implementation
This fast and lightweight metagenomic functional profiler is freely available and can be accessed here: https://github.com/KoslickiLab/fmh-funprofiler. All scripts of the analyses we present in this manuscript can be found on GitHub.
A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data
Authors: Jiang Qi , Wan Lin 
Published: September 2024

Abstract
Motivation: Learning cellular dynamics through reconstruction of the underlying cellular potential energy landscape (aka Waddington landscape) from time-series single-cell RNA sequencing (scRNA-seq) data is a current challenge. Prevailing data-driven computational methods can be hampered by the lack of physical principles to guide learning from complex data, resulting in reduced prediction accuracy and interpretability when applied to infer cell population dynamics.
Results: Here, we propose PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework that combines the Hamilton–Jacobi (HJ) equation and neural SDE to learn cellular dynamics. Grounded in potential energy theory of biological systems, PI-SDE integrates the principle of least action by enforcing the HJ equation when reconstructing cellular potential energy function. This approach not only facilitates accurate predictions, but also improves interpretability, especially in the reconstructed potential energy landscape. Through benchmarking on two real scRNA-seq datasets, we demonstrate the importance of incorporating the HJ regularization term in dynamic inference, especially in predicting gene expression at held-out time points. Meanwhile, the learned potential energy landscape provides biologically interpretable insights into the process of cell differentiation. Our framework enhances model performance, while maintaining robustness and stability.
Availability: PI-SDE software is available at https://github.com/QiJiang-QJ/PI-SDE.
GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation
Authors: Yan Huiying , Wang Shaojun , Liu Hancheng , Mamitsuka Hiroshi , Zhu Shanfeng 
Published: September 2024

Abstract

Summary
The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023–2024.

Availability and implementation
GORetriever is publicly available at https://github.com/ZhuLab-Fudan/GORetriever.
A hybrid constrained continuous optimization approach for optimal causal discovery from biological data
Authors: Zhu Yuehua , Benos Panayiotis V , Chikina Maria 
Published: September 2024

Abstract

Motivation
Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods.

Results
We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm’s strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics.

Availability and implementation
https://github.com/zhu-yh1/PC-NOTEARS.
A distribution-free and analytic method for power and sample size calculation in single-cell differential expression
Authors: Hsu Chih-Yuan , Liu Qi , Shyr Yu 
Published: September 2024

Abstract

Motivation
Differential expression analysis in single-cell transcriptomics unveils cell type-specific responses to various treatments or biological conditions. To ensure the robustness and reliability of the analysis, it is essential to have a solid experimental design with ample statistical power and sample size. However, existing methods for power and sample size calculation often assume a specific distribution for single-cell transcriptomics data, potentially deviating from the true data distribution. Moreover, they commonly overlook cell–cell correlations within individual samples, posing challenges in accurately representing biological phenomena. Additionally, due to the complexity of deriving an analytic formula, most methods employ time-consuming simulation-based strategies.

Results
We propose an analytic-based method named scPS for calculating power and sample sizes based on generalized estimating equations. scPS stands out by making no assumptions about the data distribution and considering cell–cell correlations within individual samples. scPS is a rapid and powerful approach for designing experiments in single-cell differential expression analysis.

Availability and implementation
scPS is freely available at https://github.com/cyhsuTN/scPS and Zenodo https://zenodo.org/records/13375996.
The evolution of sensory systems after signal change in threespine stickleback
Authors: Firneno Thomas J , Welsh Gabrielle T , Gumm Jennifer M , Larson Erica L , Tinghitella Robin M 
Published: September 2024

Abstract
Sensory drive can lead to the evolution of signals that are optimized to the environment in which they are perceived. However, when environmental conditions change, the interactions between signal, environment, and receiver may also shift, leading to the evolution of a new signal optimum or more categorical shifts in sexual signals (gains or losses). We evaluated how visual systems have evolved following a change in environment and male signal, and whether visual system divergence contributes to reproductive isolation between ancestral and derived types in red and black morphs of Pacific Northwest freshwater threespine stickleback. We found that opsin sequence was tuned to enhance the perceived contrast of black fish on a red-shifted light background, whereas opsin expression was not. Further, we found no evidence for homotypic preferences or assortative mating between colour morphs; males of both morphs were equally successful in no-choice mating contexts, perhaps because black males are more vigorous courters. Together, our results suggest that habitat transitions in black stickleback have led to a shift in sensory-drive dynamics with some aspects of the visual system and behaviour evolving in response to other factors (foraging or predation) or lagging behind the evolution of opsin sequences in red-shifted environments.
Using mindfulness-based intervention to promote executive function in young children: a multivariable and multiscale sample entropy study
Authors: Xie Sha , Lu Shuqi , Lu Jiahao , Gong Chaohui , Chang Chunqi 
Published: September 2024

Abstract
Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps &lt; 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = −3.674, P &lt; 0.001 for cognitive shifting; Z = 2.594, P &lt; 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children’s brain development are also addressed.
Preliminary studies on ion-pair extractions of Zr, Hf, Nb, and Ta using extractants having tertiary N atom from H2SO4 and HF
Authors: Sasaki Yuji , Kaneko Masashi , Matsumiya Masahiko 
Published: September 2024

Abstract
Ion-pair extraction enables the recovery of anionic metal ions, such as pertechnetate and chlorinated rhodium ions, by protonated extractants with tertiary amino N atom in their structures. We expand this technique for other anionic species dissolved in different mineral acids. Extractions of Zr, Hf, Nb, and Ta, metal anions present in sulfuric and hydrofluoric acids (H2SO4 and HF), are examined. Zr and Hf in H2SO4, and Zr, Hf, Nb, and Ta in HF can be extracted by ion-pair extraction using NTAamide (nitrilotriacetamide), methylimino-N, N’-dioctylacetamide (MIDOA), and trioctylamine (TOA). Basic information about their extraction behavior was obtained through this study.