bluuebunny/crossref_metadata_2025_split huggingface dataset:
Organizing and Resisting Status Competition
Authors: Beaumont Paul David 
Published: May 2024

Abstract
This chapter explores how international organizations can exert influence when lacking material carrots or sticks. Investigating Norway’s response to the OECD’s Programme for International Student Assessment (PISA) ranking, the chapter argues that international rankings operate as “status Esperanto” that allow “foreign” theories of status to bypass the obstacles that usually hinder international agreement about the rules of international status competitions. However, tracing 19 years of the education policy highlights a more critical “reactivity,” hitherto overlooked by prior research. The process of competing in the PISA rankings led several domestic groups to question the rules of the game and develop rival theories of educational status that spread to mainstream politics. This emergent resistance to the competition has undermined the potential for PISA to legitimate future policy reforms and highlights the importance of treating status competition as a discursively mediated process that is always susceptible to contestation from below.
Conclusion
Authors: Beaumont Paul David 
Published: May 2024

Abstract
Reflecting upon the preceding chapters, the conclusion answers the following questions: How does the book contribute to the study of status in world politics? What kind of research agenda does a theory-of-status framework enable that was hitherto foreclosed? Among the avenues opened up, the conclusion argues that a TIS approach provides a systematic means of analyzing change in international status hierarchies, by providing a framework for exploring the emergence, spread, contestation, and potential withering of specific theories of international status and examining these theories’ political consequences. The conclusion also responds to skeptics that may question whether the TIS approach refers to “real” status at all. It concludes with a counterintuitive policy implication of the book’s substance: that status ambiguity among states and publics is a social good to be cherished because it can mitigate zero-sum competitions.
Failed hereditary succession in comparative perspective: The case of Senegal (2000–2024)
Authors: Brossier Marie 
Published: May 2024

Abstract
Contrary to enduring theoretical expectations on neopatrimonialism, family successions are rare in sub-Saharan Africa. This article demonstrates that family successions are difficult to set up and might fail when rulers attempt to implement them. Building on the scholarship on political dynasties and family successions in broader comparative politics, I demonstrate that the study of failed attempts helps unveil the specific mechanisms of such failure. While scholarship documents how formal rules (such as term limits) constrain the ruler’s succession agenda, I contend that other types of constraints -party politics, opposition coalition, and public opinion-might also strongly impact it but have remained underexamined. The Senegal case study helps uncover these constraints. The article begins by emphasizing the theoretical importance and empirical challenges of studying non-cases of family successions and, more specifically, failed attempts. Then, the article examines the Senegalese failed hereditary succession between former President Abdoulaye Wade and his son Karim. Through a longitudinal single-country case study (2000–2024), this article employs process-tracing to uncover the three main interrelated mechanisms, which led to this failure: Popular resentment towards the succession attempt, a succession crisis due to the ruler’s not leaving power, and elite defection leading to party split. In mutually reinforcing each other, these dynamics converged to block the transfer of power from the ruler to his son. Therefore, this single case study of a failed attempt enhances our empirical and theoretical understanding of what drives variation in the success or failure of family succession. I argue that the role of actors (party elites and voters) in the succession process and how they engage with the rules of the game (mainly over party leadership selection and elections) impact the succession outcome.
New horizon in improving ageing with improvisational theatre
Authors: Keisari Shoshi , Krueger Kristin R , Ben-David Boaz M , Hainselin Mathieu 
Published: May 2024

Abstract
Cognitive decline, mental health and mindset factors can all affect the autonomy and well-being of older adults. As the number of older adults across the globe increases, interventions to improve well-being are urgently needed. Improvisational theatre (improv) and improv-based interventions are well-suited to address this need.
Studies have shown that participation in improv-based interventions has a positive impact on mental health indicators, including depressive symptoms, well-being and social connectedness, as well as cognitive skills such as attention and memory. In addition, improv-based interventions have been beneficial for people with dementia, improving positive affect, self-esteem and communication.
In this article, we describe improvisational theatre, or improv, and the reasons it has emerged from a form of spontaneous theatre that involves playfulness and creativity to an important tool to effect behavioural change in individuals and groups. We then review the literature on the effects of improv in ageing populations, with a focus on social, emotional and cognitive functioning. Finally, we make recommendations on designing improv-based interventions so that future research, using rigorous quantitative methods, larger sample sizes and randomised controlled trials, can expand the use of improv in addressing important factors related to autonomy and well-being in older adults.
‘I think we could probably do more’: an interview study to explore community pharmacists’ experiences and perspectives of frailty and optimising medicines use in frail older adults
Authors: Faulkner Lucy , Hughes Carmel M , Barry Heather E 
Published: May 2024

Abstract

Background
Community pharmacists potentially have an important role to play in identification of frailty and delivery of interventions to optimise medicines use for frail older adults. However, little is known about their knowledge or views about this role.

Aim
To explore community pharmacists’ knowledge of frailty and assessment, experiences and contact with frail older adults, and perceptions of their role in optimising medicines use for this population.

Methods
Semi-structured interviews conducted between March and December 2020 with 15 community pharmacists in Northern Ireland. Interviews were transcribed verbatim and analysed thematically.

Results
Three broad themes were generated from the data. The first, ‘awareness and understanding of frailty’, highlighted gaps in community pharmacists’ knowledge regarding presentation and identification of frailty and their reluctance to broach potentially challenging conversations with frail older patients. Within the second theme, ‘problem-solving and supporting medication use’, community pharmacists felt a large part of their role was to resolve medicines-related issues for frail older adults through collaboration with other primary healthcare professionals but feedback on the outcome was often not provided upon issue resolution. The third theme, ‘seizing opportunities in primary care to enhance pharmaceutical care provision for frail older adults’, identified areas for further development of the community pharmacist role.

Conclusions
This study has provided an understanding of the views and experiences of community pharmacists about frailty. Community pharmacists’ knowledge deficits about frailty must be addressed and their communication skills enhanced so they may confidently initiate conversations about frailty and medicines use with older adults.
Mix-Key: graph mixup with key structures for molecular property prediction
Authors: Jiang Tianyi , Wang Zeyu , Yu Wenchao , Wang Jinhuan , Yu Shanqing , Bao Xiaoze , Wei Bin , Xuan Qi 
Published: May 2024

Abstract
Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.
Evaluating large language models for annotating proteins
Authors: Vitale Rosario , Bugnon Leandro A , Fenoy Emilio Luis , Milone Diego H , Stegmayer Georgina 
Published: May 2024

Abstract
In UniProtKB, up to date, there are more than 251 million proteins deposited. However, only 0.25% have been annotated with one of the more than 15000 possible Pfam family domains. The current annotation protocol integrates knowledge from manually curated family domains, obtained using sequence alignments and hidden Markov models. This approach has been successful for automatically growing the Pfam annotations, however at a low rate in comparison to protein discovery. Just a few years ago, deep learning models were proposed for automatic Pfam annotation. However, these models demand a considerable amount of training data, which can be a challenge with poorly populated families. To address this issue, we propose and evaluate here a novel protocol based on transfer learningṪhis requires the use of protein large language models (LLMs), trained with self-supervision on big unnanotated datasets in order to obtain sequence embeddings. Then, the embeddings can be used with supervised learning on a small and annotated dataset for a specialized task. In this protocol we have evaluated several cutting-edge protein LLMs together with machine learning architectures to improve the actual prediction of protein domain annotations. Results are significatively better than state-of-the-art for protein families classification, reducing the prediction error by an impressive 60% compared to standard methods. We explain how LLMs embeddings can be used for protein annotation in a concrete and easy way, and provide the pipeline in a github repo. Full source code and data are available at https://github.com/sinc-lab/llm4pfam
Revealing cell–cell communication pathways with their spatially coupled gene programs
Authors: Zhu Junchao , Dai Hao , Chen Luonan 
Published: May 2024

Abstract
Inference of cell–cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand–receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.
Single-cell RNA sequencing data imputation using bi-level feature propagation
Authors: Lee Junseok , Yun Sukwon , Kim Yeongmin , Chen Tianlong , Kellis Manolis , Park Chanyoung 
Published: May 2024

Abstract
Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell–cell or gene–gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene–gene and cell–cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints
Authors: Zhang Chenhao , Zhang Chengyun , Shang Tianfeng , Zhu Ning , Wu Xinyi , Duan Hongliang 
Published: May 2024

Abstract
In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target–cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure–activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.
Molecular property prediction based on graph structure learning
Authors: Zhao Bangyi , Xu Weixia , Guan Jihong , Zhou Shuigeng 
Published: May 2024

Abstract

Motivation
Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in improving prediction performance. However, current models often ignore relationships between molecules, which could be also helpful for MPP.

Results
For this sake, in this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. Then, with molecular fingerprints, we construct a molecule similarity graph (MSG). Following that, we conduct GSL on the MSG, i.e. molecule-level GSL, to get the final molecular embeddings, which are the results of fuzing both GNN encoded molecular representations and the relationships among molecules. That is, combining both intra-molecule and inter-molecule information. Finally, we use these molecular embeddings to perform MPP. Extensive experiments on 10 various benchmark datasets show that our method could achieve state-of-the-art performance in most cases, especially on classification tasks. Further visualization studies also demonstrate the good molecular representations of our method.

Availability and implementation
Source code is available at https://github.com/zby961104/GSL-MPP.
Causal inference for time-to-event data with a cured subpopulation
Authors: Wang Yi , Deng Yuhao , Zhou Xiao-Hua 
Published: May 2024

ABSTRACT
When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.
Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components
Authors: Xie Shanghong , Zeng Donglin , Wang Yuanjia 
Published: May 2024

ABSTRACT
Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.
Evolutionarily conserved 12-oxophytodienoate reductase trans-lncRNA pair affects disease resistance in tea (Camellia sinensis) via the jasmonic acid signaling pathway
Authors: Jiang Ting , Jiao Tianming , Hu Yingbang , Li Tongtong , Liu Cheng , Liu Yajun , Jiang Xiaolan , Xia Tao , Gao Li-Ping 
Published: May 2024

Abstract
Long non-coding RNAs (lncRNAs) have gathered significant attention due to their pivotal role in plant growth, development, and biotic and abiotic stress resistance. Despite this, there is still little understanding regarding the functions of lncRNA in these domains in the tea plant (Camellia sinensis), mainly attributable to the insufficiencies in gene manipulation techniques for tea plants. In this study, we designed a novel strategy to identify evolutionarily conserved trans-lncRNA (ECT-lncRNA) pairs in plants. We used highly consistent base sequences in the exon-overlapping region between trans-lncRNAs and their target gene transcripts. Based on this method, we successfully screened 24 ECT-lncRNA pairs from at least two or more plant species. In tea, as observed in model plants such as Arabidopsis, alfalfa, potatoes, and rice, there exists a trans-lncRNA capable of forming an ECT-lncRNA pair with transcripts of the 12-oxophytodienoate reductase (OPR) family, denoted as the OPRL/OPR pair. Considering evolutionary perspectives, the OPRL gene cluster in each species likely originates from a replication event of the OPR gene cluster. Gene manipulation and gene expression analysis revealed that CsOPRL influences disease resistance by regulating CsOPR expression in tea plants. Furthermore, the knockout of StOPRL1 in Solanum tuberosum led to aberrant growth characteristics and strong resistance to fungal infection. This study provides insights into a strategy for the screening and functional verification of ECT-lncRNA pairs.