bluuebunny/crossref_metadata_2025 huggingface dataset:
Stephen, the King: A Rock Opera in the Late Communist Period and Questions of National Identity
Authors: Berend Nora
Published: May 2024

Demonstrating how myth laundered into history continued to be reshaped in the twentieth century and beyond, this chapter takes up the story of Stephen and Koppány represented in a rock opera in 1983. Backed by a historical advisor, the creators of the rock opera offered a heavily mythical story that was understood as a real reconstruction of historical events. It gained immediate popularity, seen by many as expressing opposition to the late communist regime. Yet it also gained the authorities’ approval, whether as a safety valve or an apotheosis of the regime. Read by many as the story of the 1956 revolution, Stephen the King also reflected the struggles around national identity in the last years of the communist regime, and, through yearly performances and reinterpretations, beyond. The rock opera has been immensely successful in propagating its vision of Hungarian history as a true reconstruction, and sparked further elaborations of mythical history, catapulting Koppány into the role of national hero.
The Hungarian Crown
Authors: Berend Nora
Published: May 2024

The most intriguingly complex as well as the most noxious legends linked to Stephen by posterity are concentrated around his alleged crown. Several independent myths originating in different centuries can be disentangled: the story about the papal sending of Stephen’s crown; speculations about the origins of the extant medieval crown that used to be attributed to Stephen; the development and meaning of the ‘doctrine of the Holy Crown’; and the symbolism with which the existing crown has been invested in modern times. At their most misleading and most potent, however, the legends intertwine to the point of becoming indistinguishable, and reinforce each other, turning the physical object into the carrier of legendary power and sacrality. The chapter dissects the rise of each myth in the medieval and modern periods, and investigates the reasons for the renewed political use of the crown in the twentieth and twenty-first centuries. Drawing on European parallels, the chapter both offers a re-evaluation of the extant medieval crown and the ‘doctrine of the Holy Crown’ and reconstructs for the first time how the crown came to be seen as ‘holy’. Ultimately, the chapter demonstrates that it is only the modern and contemporary history of the crown that is unusual.
Introduction
Authors: Beaumont Paul David
Published: May 2024

This chapter introduces the puzzle animating the book that the recent wave of status research has left in its wake: Why do states appear to chase status so ferociously when the status rewards appear so ephemeral and perhaps illusionary? To address this puzzle, the chapter makes the theoretical case that states have varying degrees of leeway to develop and maintain hierarchical constructions of the world that are neither shared nor recognized by international audiences yet remain salient and have political effects domestically. Consequently, governments can benefit from status-seeking in terms of legitimacy, without being beholden to international recognition. This has been overlooked because prior works tend to bracket the domestic audience, overstate the degree of intersubjective agreement about international status, and thereby understate the degree of interpretative agency located within domestic discourses. This discussion lays the groundwork for studying the theories of status used by actors within domestic politics.
Organizing and Resisting Status Competition
Authors: Beaumont Paul David
Published: May 2024

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
CRISPR/Cas9 genome editing of potato StDMR6-1 results in plants less affected by different stress conditions
Authors: Karlsson Milla , Kieu Nam Phuong , Lenman Marit , Marttila Salla , Resjö Svante , Zahid Muhammad Awais , Andreasson Erik
Published: May 2024

Potato is the third most important food crop, but cultivation is challenged by numerous diseases and adverse abiotic conditions. To combat diseases, frequent fungicide application is common. Knocking out susceptibility genes by genome editing could be a durable option to increase resistance. DMR6 has been described as a susceptibility gene in several crops, based on data that indicates increased resistance upon interruption of the gene function. In potato, Stdmr6-1 mutants have been described to have increased resistance against the late blight pathogen Phytophthora infestans in controlled conditions. Here, we present field evaluations of CRISPR/Cas9 mutants, in a location with a complex population of P. infestans, during four consecutive years that indicate increased resistance to late blight without any trade-off in terms of yield penalty or tuber quality. Furthermore, studies of potato tubers from the field trials indicated increased resistance to common scab, and the mutant lines exhibit increased resistance to early blight pathogen Alternaria solani in controlled conditions. Early blight and common scab are problematic targets in potato resistance breeding, as resistance genes are very scarce. The described broad-spectrum resistance of Stdmr6-1 mutants may further extend to some abiotic stress conditions. In controlled experiments of either drought simulation or salinity, Stdmr6-1 mutant plants are less affected than the background cultivar. Together, these results demonstrate the prospect of the Stdmr6-1 mutants as a useful tool in future sustainable potato cultivation without any apparent trade-offs.
Covariate shift in nonparametric regression with Markovian design
Authors: Trottner Lukas
Published: May 2024

Covariate shift in regression problems and the associated distribution mismatch between training and test data is a commonly encountered phenomenon in machine learning. In this paper, we extend recent results on nonparametric convergence rates for i.i.d. data to Markovian dependence structures. We demonstrate that under Hölder smoothness assumptions on the regression function, convergence rates for the generalization risk of a Nadaraya–Watson kernel estimator are determined by the similarity between the invariant distributions associated to source and target Markov chains. The similarity is explicitly captured in terms of a bandwidth-dependent similarity measure recently introduced in Pathak, Ma and Wainwright [ICML, 2022]. Precise convergence rates are derived for the particular cases of finite Markov chains and spectral gap Markov chains for which the similarity measure between their invariant distributions grows polynomially with decreasing bandwidth. For the latter, we extend the notion of a distribution transfer exponent from Kpotufe and Martinet [Ann. Stat., 49(6), 2021] to kernel transfer exponents of uniformly ergodic Markov chains in order to generate a rich class of Markov kernel pairs for which convergence guarantees for the covariate shift problem can be formulated.
The underestimated fraction: diversity, challenges and novel insights into unicellular cyanobionts of lichens
Authors: Jung Patrick , Briegel-Williams Laura , Büdel Burkhard , Schultz Matthias , Nürnberg Dennis J , Grube Martin , D’Agostino Paul M , Kaštovský Jan , Mareš Jan , Lorenz Maike , González Manuel Luis Gil , Forno Manuela Dal , Westberg Martin , Chrismas Nathan , Pietrasiak Nicole , Whelan Paul , Dvořák Petr , Košuthová Alica , Gkelis Spyros , Bauersachs Thorsten , Schiefelbein Ulf , Giao Võ Thị Phi , Lakatos Michael ,   INCb,   International Network for research on unicellular CyanoBionts from lichens
Published: May 2024

Lichens are remarkable and classic examples of symbiotic organisms that have fascinated scientists for centuries. Yet, it has only been for a couple of decades that significant advances have focused on the diversity of their green algal and/or cyanobacterial photobionts. Cyanolichens, which contain cyanobacteria as their photosynthetic partner, include up to 10% of all known lichens and, as such, studies on their cyanobionts are much rarer compared to their green algal counterparts. For the unicellular cyanobionts, i.e. cyanobacteria that do not form filaments, these studies are even scarcer. Nonetheless, these currently include at least 10 different genera in the cosmopolitan lichen order Lichinales. An international consortium (International Network of CyanoBionts; INCb) will tackle this lack of knowledge. In this article, we discuss the status of current unicellular cyanobiont research, compare the taxonomic resolution of photobionts from cyanolichens with those of green algal lichens (chlorolichens), and give a roadmap of research on how to recondition the underestimated fraction of symbiotic unicellular cyanobacteria in lichens.
Use of the FebriDx® host-response point-of-care test may reduce antibiotic use for respiratory tract infections in primary care: a mixed-methods feasibility study
Authors: Wilcox Christopher R , Odeh Nour , Clark Tristan W , Muller Ingrid , Becque Taeko , Todd Alexander , Islam Nazrul , Little Paul , Davies Firoza , McGavin John , Francis Nick
Published: May 2024

Introduction

FebriDx® is a CE-marked, single-use point-of-care test with markers for bacterial [C-reactive protein (CRP)] and viral [myxovirus resistance protein A (MxA)] infection, using finger-prick blood samples. Results are available after 10–12 min. We explored the usability and potential impact of FebriDx® in reducing antibiotic prescriptions for lower respiratory tract infection (LRTI) in primary care, and the feasibility of conducting a randomized controlled trial (RCT).

Methods

Patients (aged ≥1 year) with LRTI deemed likely to receive antibiotic prescription were recruited at nine general practices and underwent FebriDx® testing. Data collection included FebriDx® results, antibiotic prescribing plan (before and after testing) and re-consultation rates. Staff completed System Usability Scale questionnaires.

Results

From 31 January 2023 to 9 June 2023, 162 participants participated (median age 57 years), with a median symptom duration of 7 days (IQR 5–14). A valid FebriDx® result was obtained in 97% (157/162). Of 155 patients with available results, 103 (66%) had no detectable CRP or MxA, 28 (18%) had CRP only, 5 (3%) had MxA only, and 19 (12%) had both CRP and MxA. The clinicians’ stated management plan was to prescribe antibiotics for 86% (134/155) before testing and 45% (69/155) after testing, meaning a 41% (95% CI: 31%, 51%) difference after testing, without evidence of increased re-consultation rates. Ease-of-use questionnaires showed ‘good’ user-friendliness.

Conclusions

Use of FebriDx® to guide antibiotic prescribing for LRTI in primary care was associated with a substantial reduction in prescribing intentions. These results support a fully powered RCT to confirm its impact and safety.
RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition
Authors: Li Mingchen , Zhou Huixue , Yang Han , Zhang Rui
Published: May 2024

Objectives

This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework.

Materials and Methods

Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance.

Results

Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance.

Discussion

This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study.

Conclusion

The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.
What We Argue About When We Argue About Death
Authors: Aas Sean
Published: May 2024

The literature on the determination of death has often if not always assumed that the concept of human death should be defined in terms of the end of the human organism. I argue that this broadly biological conceptualization of human death cannot constitute a basis for agreement in a pluralistic society characterized by a variety of reasonable views on the nature of our existence as embodied beings. Rather, following Robert Veatch, I suggest that we must define death in moralized terms, as the loss of an especially significant sort of moral standing. Departing from Veatch, however, I argue that we should not understand death in terms of the loss of all moral status whatsoever. Rather, I argue, what we should argue about, when we argue about death, is when and why people lose their rights-claims to the protection and promotion of their basic bodily functioning.
Protopanaxadiol prevents cisplatin-induced acute kidney injury by regulating ferroptosis
Authors: Song Zeyu , Li Zhenyuan , Pan Tao , Liu Teng , Gong Baifang , Wang Zhixia , Liu Ke , Fan Huaying
Published: May 2024

Objectives

Acute kidney injury (AKI) caused by cisplatin (CDDP) is a complex, critical illness with no effective or specific treatment. The purpose of the study was to assess the protective effect of protopanaxadiol (PPD) on the kidneys in CDDP-induced AKI models and its possible mechanisms.

Methods

In vitro, the protection of PPD was assessed in HK-2. KM mice were injected with CDDP to induce AKI models in vivo. The determination of blood urea nitrogen and serum creatinine (SCr) was performed, and pathological changes were examined by histopathological examination. Immunostaining and western blot analyses were used to analyze the expression levels of proteins.

Results

PPD can increase the viability of HK-2 cells damaged by CDDP, improve cell morphology, and alleviate the symptoms of AKI in mice. In addition, PPD can down-regulate the protein expression of TRF and up-regulate the protein expression of Ferritin heavy chain, Glutathione peroxidase 4, and ferroptosis suppressor protein 1 reduce the iron content in cells and kidney tissues, and restore the antioxidant defense system.

Conclusion

PPD has an inhibitory effect on cisplatin-induced nephrotoxicity, which may be related to the inhibition of ferroptosis by regulating iron metabolism and lipid peroxidation.
Estimating disease transmission in a closed population under repeated testing
Authors: Wascher Matthew , Schnell Patrick M , KhudaBukhsh Wasiur R , Quam Mikkel B M , Tien Joesph H , Rempała Grzegorz A
Published: May 2024

The article presents a novel statistical framework for COVID-19 transmission monitoring and control, which was developed and deployed at The Ohio State University main campus in Columbus during the Autumn term of 2020. Our approach effectively handles prevalence data with interval censoring and explicitly incorporates changes in transmission dynamics and human behaviour. To illustrate the methodology’s usefulness, we apply it to both synthetic and actual student SARS-CoV-2 testing data collected at the OSU Columbus campus in late 2020.
Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli
Authors: Hinz Aaron , Amado André , Kassen Rees , Bank Claudia , Wong Alex
Published: May 2024

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.
mtDNA-Server 2: advancing mitochondrial DNA analysis through highly parallelized data processing and interactive analytics
Authors: Weissensteiner Hansi , Forer Lukas , Kronenberg Florian , Schönherr Sebastian
Published: May 2024

Over the past decade, mtDNA-Server established itself as one of the most widely used variant calling web-services for human mitochondrial genomes. The service accepts sequencing data in BAM format and returns an annotated variant analysis report for both homoplasmic and heteroplasmic variants. In this work we present mtDNA-Server 2, which includes several new features highly requested by the community. Most importantly, it includes (a) the integration of a novel variant calling mode that accurately call insertions, deletions and single nucleotide variants at once, (b) the integration of additional quality control and input validation modules, (c) a method to estimate the required coverage to minimize false positives and (d) an interactive analytics dashboard. Furthermore, we migrated the complete analysis workflow to the Nextflow workflow manager for improved parallelization, reproducibility and local execution. Recognizing the importance of insertions and deletions as well as offering novel quality control, validation and reporting features, mtDNA-Server 2 provides researchers and clinicians a new state-of-the-art analysis platform for interpreting mitochondrial genomes. mtDNA-Server 2 is available via mitoverse, our analysis platform that offers a centralized place for mtDNA analysis in the cloud. The web-service, source code and its documentation are freely accessible at https://mitoverse.i-med.ac.at.
UPF1 helicase orchestrates mutually exclusive interactions with the SMG6 endonuclease and UPF2
Authors: Langer Lukas M , Kurscheidt Katharina , Basquin Jérôme , Bonneau Fabien , Iermak Iuliia , Basquin Claire , Conti Elena
Published: May 2024

Nonsense-mediated mRNA decay (NMD) is a conserved co-translational mRNA surveillance and turnover pathway across eukaryotes. NMD has a central role in degrading defective mRNAs and also regulates the stability of a significant portion of the transcriptome. The pathway is organized around UPF1, an RNA helicase that can interact with several NMD-specific factors. In human cells, degradation of the targeted mRNAs begins with a cleavage event that requires the recruitment of the SMG6 endonuclease to UPF1. Previous studies have identified functional links between SMG6 and UPF1, but the underlying molecular mechanisms have remained elusive. Here, we used mass spectrometry, structural biology and biochemical approaches to identify and characterize a conserved short linear motif in SMG6 that interacts with the cysteine/histidine-rich (CH) domain of UPF1. Unexpectedly, we found that the UPF1–SMG6 interaction is precluded when the UPF1 CH domain is engaged with another NMD factor, UPF2. Based on cryo-EM data, we propose that the formation of distinct SMG6-containing and UPF2-containing NMD complexes may be dictated by different conformational states connected to the RNA-binding status of UPF1. Our findings rationalize a key event in metazoan NMD and advance our understanding of mechanisms regulating activity and guiding substrate recognition by the SMG6 endonuclease.
GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs
Authors: Gou Yujie , Liu Dan , Chen Miaomiao , Wei Yuxiang , Huang Xinhe , Han Cheng , Feng Zihao , Zhang Chi , Lu Teng , Peng Di , Xue Yu
Published: May 2024

Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
SHAPEwarp-web: sequence-agnostic search for structurally homologous RNA regions across databases of chemical probing data
Authors: Scholten Niek R , Haandrikman Dennis , Tolhuis Joshua O , Morandi Edoardo , Incarnato Danny
Published: May 2024

RNA molecules perform a variety of functions in cells, many of which rely on their secondary and tertiary structures. Chemical probing methods coupled with high-throughput sequencing have significantly accelerated the mapping of RNA structures, and increasingly large datasets of transcriptome-wide RNA chemical probing data are becoming available. Analogously to what has been done for decades in the protein world, this RNA structural information can be leveraged to aid the discovery of structural similarity to a known RNA (or RNA family), which, in turn, can inform about the function of transcripts. We have previously developed SHAPEwarp, a sequence-agnostic method for the search of structurally homologous RNA segments in a database of reactivity profiles derived from chemical probing experiments. In its original implementation, however, SHAPEwarp required substantial computational resources, even for moderately sized databases, as well as significant Linux command line know-how. To address these limitations, we introduce here SHAPEwarp-web, a user-friendly web interface to rapidly query large databases of RNA chemical probing data for structurally similar RNAs. Aside from featuring a completely rewritten core, which speeds up by orders of magnitude the search inside large databases, the web server hosts several high-quality chemical probing databases across multiple species. SHAPEwarp-web is available from https://shapewarp.incarnatolab.com.
Analysis of swimming trajectories in Daphnia similis as an environmental impact assessment tool
Authors: Lopes Rubens M , Akari Marcia , De La Cruz Leandro T , Uttieri Marco
Published: May 2024

Toxicity tests with daphnids are important tools in the environmental impact assessment of aquatic ecosystems. The aim of this study was to characterize the swimming paths of Daphnia similis in response to two concentrations of the reference substance potassium chloride (KCl), as an additional expedite tool in evaluating the effects of contaminants. Bidimensional trajectory coordinates were obtained with image acquisition and processing methods and used to calculate the following metrics of D. similis behavior: mean swimming speed, hop rate, fractal dimension, and vertical occupancy. The swimming speed, hop rate and fractal dimension for the control group were significantly different from the KCl treatment at the higher concentration. Conversely, the vertical occupancy did not differ between the experimental conditions. The results of this investigation validate the use of behavioral analysis as a rapid and reliable approach to evaluate water contamination, aiding in early warning detection of water quality issues. The method can be further refined through the integration of machine learning applications in video tracking.