bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phylogenetic position of Ligusticopsis (Apiaceae, Apioideae): evidence from molecular data and carpological characters
Authors: Li Zi-Xuan , Guo Xian-Lin , Price Megan , Zhou Song-Dong , He Xing-Jin 
Published: March 2022

Abstract
Ligusticopsis (Apiaceae, Apioideae) is now considered to have an East-Asia and Sino–Himalaya distribution. The genus was not recognized as a natural and separate genus and was treated as a synonym of Ligusticum both in Flora Reipublicae Popularis Sinicae and Flora of China since first established, though Pimenov et al. have made many taxonomic revisions to Ligusticopsis, phylogenetic relationships between Ligusticopsis and Ligusticum have been in dispute. Thirty-four plastomes and 35 nrITS from Apioideae were analysed by RAxML and MrBayes to reconstruct the phylogenetic relationships, along with carpology of 10 species and comparative analyses of 17 plastomes to investigate the evidence supporting the independence of Ligusticopsis. As a result, nine species suggested to be Ligusticopsis formed a highly supported monophyletic branch (Subclade A) inside Selineae both in maximum likelihood and Bayesian inference; the results of the comparative analyses further supported the monophyly of Subclade A, mainly in the location of genes at the IRa/LSC boundary, the sequence diversity exhibited by various genes (e.g. trnH-GUG–psbA and ycf2) and same codon biases in terminator TAA (relative synonymous codon usage = 1.75). Species in Subclade A also had shared characters in mericarps, combined with other characters of the plant, ‘base clothed in fibrous remnant sheaths, pinnate bracts, pinnate bracteoles longer than rays of umbellule, mericarps strongly compressed dorsally, median and lateral ribs filiform or keeled, marginal ribs winged, and numerous vittae in commissure and each furrow’ should be the most important and diagnostic characters of Ligusticopsis. Our phylogenetic trees and other analyses supported the previous taxonomic treatments of Pimenov et al. that Ligusticopsis should be a natural and separate genus rather than a synonym of Ligusticum.
BioDKG–DDI: predicting drug–drug interactions based on drug knowledge graph fusing biochemical information
Authors: Ren Zhong-Hao , Yu Chang-Qing , Li Li-Ping , You Zhu-Hong , Guan Yong-Jian , Wang Xin-Fei , Pan Jie 
Published: March 2022

AbstractThe way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactions among drugs, predicting potential adverse drug–drug interactions (DDIs) accurately is promotive to prevent unanticipated interactions, which may cause significant harm to patients. Currently, numerous computational studies are focusing on potential DDIs prediction on account of traditional experiments in wet lab being time-consuming, labor-consuming, costly and inaccurate. These approaches performed well; however, many approaches did not consider multi-scale features and have the limitation that they cannot predict interactions among novel drugs. In this paper, we proposed a model of BioDKG–DDI, which integrates multi-feature with biochemical information to predict potential DDIs through an attention machine with superior performance. Molecular structure features, representation of drug global association using drug knowledge graph (DKG) and drug functional similarity features are fused by attention machine and predicted through deep neural network. A novel negative selecting method is proposed to certify the robustness and stability of our method. Then, three datasets with different sizes are used to test BioDKG–DDI. Furthermore, the comparison experiments and case studies can demonstrate the reliability of our method. Upon our finding, BioDKG–DDI is a robust, yet simple method and can be used as a benefic supplement to the experimental process.
Integrated multi-cohorts for characterizing the immunogenomic landscape and predicting drug response in triple-negative breast cancer
Authors: Su Dongqing , Chi Meng , Wang Shiyuan , Di Shuhong , Zhang Haoxing , Lu Qianzi , Yu Yao , Xiong Yuqiang , Wei Haodong , Lv Yingli , Zuo Yongchun , Yang Lei 
Published: March 2022

AbstractTriple-negative breast cancer (TNBC) is the breast cancer subtype with the highest fatality rate, and it seriously threatens women’s health. Recent studies found that the level of immune cell infiltration in TNBC was associated with tumor progression and prognosis. However, due to practical constraints, most of these TNBC immune infiltration studies only used a small number of patient samples and a few immune cell types. Therefore, it is necessary to integrate more TNBC patient samples and immune cell types to comprehensively study immune infiltration in TNBC to contribute to the prognosis and treatment of patients. In this study, 12 TNBC datasets were integrated and an extensive collection of 182 gene sets with immune-related signatures were included to comprehensively investigate tumor immune microenvironment of TNBC. A single sample gene set enrichment analysis was performed to calculate the infiltration score of each immune-related signature in each patient, and an immune-related risk scoring model for TNBC was constructed to accurately assess patient prognosis. Significant differences were found in immunogenomic landscape between different immune risk subtypes. In addition, the immunotherapy response and chemotherapy drug sensitivity of patients with different immune risk subtypes were also analyzed. The results showed that there were significant differences in these characteristics. Finally, a prediction model for immune risk subtypes of TNBC patients was constructed to accurately predict patients with unknown subtypes. Based on the aforementioned findings, we believed that the immune-related risk score constructed in this study can assist in providing personalized medicine to TNBC patients.
ProSAP: a GUI software tool for statistical analysis and assessment of thermal stability data
Authors: Ji Hongchao , Lu Xue , Zheng Zhenxiang , Sun Siyuan , Tan Chris Soon Heng 
Published: March 2022

Abstract
The Cellular Thermal Shift Assay (CETSA) plays an important role in drug-target identification, and statistical analysis is a crucial step significantly affecting conclusion. We put forward ProSAP (Protein Stability Analysis Pod), an open-source, cross-platform and user-friendly software tool, which provides multiple methods for thermal proteome profiling (TPP) analysis, nonparametric analysis (NPA), proteome integral solubility alteration and isothermal shift assay (iTSA). For testing the performance of ProSAP, we processed several datasets and compare the performance of different algorithms. Overall, TPP analysis is more accurate with fewer false positive targets, but NPA methods are flexible and free from parameters. For iTSA, edgeR and DESeq2 identify more true targets than t-test and Limma, but when it comes to ranking, the four methods show not much difference. ProSAP software is available at https://github.com/hcji/ProSAP and https://zenodo.org/record/5763315.
An efficient curriculum learning-based strategy for molecular graph learning
Authors: Gu Yaowen , Zheng Si , Xu Zidu , Yin Qijin , Li Liang , Li Jiao 
Published: March 2022

AbstractComputational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG’s encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning.Availability: The source code is available in https://github.com/gu-yaowen/CurrMG.
DeepTTA: a transformer-based model for predicting cancer drug response
Authors: Jiang Likun , Jiang Changzhi , Yu Xinyu , Fu Rao , Jin Shuting , Liu Xiangrong 
Published: March 2022

Abstract
Identifying new lead molecules to treat cancer requires more than a decade of dedicated effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is generally validated by in vitro cellular experiments. Therefore, accurate prediction of cancer drug response is a critical and challenging task for anti-cancer drugs design and precision medicine. With the development of pharmacogenomics, the combination of efficient drug feature extraction methods and omics data has made it possible to use computational models to assist in drug response prediction. In this study, we propose DeepTTA, a novel end-to-end deep learning model that utilizes transformer for drug representation learning and a multilayer neural network for transcriptomic data prediction of the anti-cancer drug responses. Specifically, DeepTTA uses transcriptomic gene expression data and chemical substructures of drugs for drug response prediction. Compared to existing methods, DeepTTA achieved higher performance in terms of root mean square error, Pearson correlation coefficient and Spearman’s rank correlation coefficient on multiple test sets. Moreover, we discovered that anti-cancer drugs bortezomib and dactinomycin provide a potential therapeutic option with multiple clinical indications. With its excellent performance, DeepTTA is expected to be an effective method in cancer drug design.
Exploring the Effects of Geopolitical Shifts on Global Wildlife Trade
Authors: Ribeiro Joana , Bingre Pedro , Strubbe Diederik , Santana Joana , Capinha César , Araújo Miguel B , Reino Luís 
Published: March 2022

AbstractInternational wildlife trade is a major driver of species extinction and biological invasions. Anticipating environmental risks requires inferences about trade patterns, which are shaped by geopolitics. Although the future cannot be predicted, scenarios can help deal with the uncertainty of future geopolitical dynamics. We propose a framework for generating and analyzing scenarios based on four geopolitical storylines, distinguished by combinations of international trade barrier strength and domestic law enforcement degree across countries supplying and demanding wildlife. We then use historical data on bird trade to classify countries into geopolitical profiles and confirm that trade barriers and law enforcement allow predicting bird trade patterns, supporting our scenarios’ plausibility and enabling projections for future global bird trade. Our framework can be used to examine the consequences of geopolitical changes for wildlife trade and to advise policy and legislation. Reducing demand for wildlife and ameliorating global inequality are key for curbing trade related risks.
Fighting a Fire versus Waiting for the Wave: Useful and Not-So-Useful Analogies in Times of SARS-CoV-2
Authors: Archer Louise C , Standley Claire J , Molnár Péter K 
Published: March 2022

Abstract
As SARS-CoV-2 has swept the planet, intermittent lockdowns have become a regular feature to control transmission. References to so-called recurring waves of infections remain pervasive among news headlines, political messaging, and public health sources. We explore the power of analogies to facilitate understanding of biological models and processes by reviewing strengths and limitations of analogies used throughout the COVID-19 pandemic. We consider how, when analogies fall short, their ability to persuade can mislead public perception, even if unintentionally. Although waves can convey patterns of disease outbreak, we suggest process-based analogies might be more effective communication tools, given that they can be easily mapped to underlying epidemiological concepts and extended to include complex dynamics. Although no single analogy perfectly captures disease dynamics, fire is particularly suitable for visualizing epidemiological models, underscoring the importance and reasoning behind control strategies and potentially conveying a sense of urgency that can galvanize individual and collective action.
Distinct brainstem to spinal cord noradrenergic pathways inversely regulate spinal neuronal activity
Authors: Kucharczyk Mateusz W , Di Domenico Francesca , Bannister Kirsty 
Published: March 2022

Abstract
Brainstem to spinal cord noradrenergic pathways include a locus coeruleus origin projection and diffuse noxious inhibitory controls. While both pathways are traditionally viewed as exerting an inhibitory effect on spinal neuronal activity, the locus coeruleus was previously shown to have a facilitatory influence on thermal nocioception according to the subpopulation of coerulean neurons activated. Coupled with knowledge of its functional modular organisation and the fact that diffuse noxious inhibitory controls are not expressed in varied animal models of chronicity, we hypothesized a regulatory role for the locus coeruleus on non-coerulean, discrete noradrenergic cell group(s).
We implemented locus coeruleus targeting strategies by microinjecting canine adenovirus encoding for channelrhodopsin-2 under a noradrenaline-specific promoter in the spinal cord (retrogradely labelling a coeruleospinal module) or the locus coeruleus itself (labelling the entire coerulean module). Coeruleospinal module optoactivation abolished diffuse noxious inhibitory controls (two-way ANOVA, P &lt; 0.0001), which were still expressed following locus coeruleus neuronal ablation.
We propose that the cerulean system interacts with, but does not directly govern, diffuse noxious inhibitory controls. This mechanism may underlie the role of the locus coeruleus as a ‘chronic pain generator’. Pinpointing the functionality of discrete top-down pathways is crucial for understanding sensorimotor modulation in health and disease.
Different computational relations in language are captured by distinct brain systems
Authors: Fu Ze , Wang Xiaosha , Wang Xiaoying , Yang Huichao , Wang Jiahuan , Wei Tao , Liao Xuhong , Liu Zhiyuan , Chen Huimin , Bi Yanchao 
Published: March 2022

AbstractA critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.
Molnupiravir and Nirmatrelvir-Ritonavir: Oral Coronavirus Disease 2019 Antiviral Drugs
Authors: Saravolatz Louis D , Depcinski Shawn , Sharma Mamta 
Published: March 2022

Abstract
At a crucial time with rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant globally, the United States Food and Drug Administration has issued an emergency use authorization for 2 oral antivirals, molnupiravir (in persons aged ≥18 years) and nirmatrelvir-ritonavir (Paxlovid) (in persons aged ≥12 years weighing ≥40 kg), for the outpatient treatment of patients with mild to moderate coronavirus disease 2019 (COVID–19) who are at risk for progression. Molnupiravir is a nucleoside analogue, whereas nirmatrelvir is a SARS-CoV-2 main protease inhibitor, and ritonavir is a human immunodeficiency virus type 1 protease inhibitor. Drug interactions are a major concern for nirmatrelvir-ritonavir. Nirmatrelvir-ritonavir demonstrated a greater risk reduction in hospitalization and death than molnupiravir compared to placebo. Both drugs need to be started within 5 days of symptoms onset and given for 5 days’ duration. This article reviews the 2 oral COVID-19 antiviral drugs including the mechanisms of action, antiviral activity, pharmacokinetics, drug interactions, clinical experience including trials, adverse events, recommended indications, and formulary considerations.
Mental Health, Work Presenteeism, and Exercise in Inflammatory Bowel Disease
Authors: Sciberras Martina , Karmiris Konstantinos , Nascimento Catarina , Tabone Trevor , Nikolaou Penelope , Theodoropoulou Angeliki , Mula Abigail , Goren Idan , Yanai Henit , Amir Hadar , Mantzaris Gerassimnos J , Georgiadi Tereza , Foteinogiannopoulou Kalliopi , Koutroubakis Ioannis , Allocca Mariangela , Fiorino Gionata , Furfaro Federica , Katsanos Konstantinos , Fousekis Fotios , Michalopoulos George , Camilleri Liberato , Torres Joana , Ellul Pierre 
Published: March 2022

Abstract

Background
Chronic diseases, such as IBD, can lead to anxiety and depression which can have a significant impact on productivity at work [presenteeism]. The aim of this study was to assess the prevalence of depression/anxiety, presenteeism and exercise levels among IBD patients.

Methods
This was a multicentre study whereby adult IBD patients, in clinical remission, were asked to answer a questionnaire anonymously. Hospital Anxiety and Depression Score [HADS], Stanford Presenteeism Scale [SPS-6] and Godin Exercise Score were also collected.

Results
A total of 585 patients were recruited. The majority had Crohn’s disease [CD, 62.2%] and were male [53.0%], with a median age of 39 years [IQR 30-49]. A psychiatric diagnosis was present in 10.8% of patients prior to their IBD diagnosis. A further 14.2% of patients were psychiatrically diagnosed after IBD diagnosis, this being commoner in CD patients [41.6% of CD, p &lt;0.01]. A raised HADS-Anxiety or a HADS-Depression score ≥8 was present in 46.1% of patients, with 27.4% having a score ≥11. Low presenteeism at work was present in 34.0%. Patients diagnosed with depression/anxiety had a more sedentary lifestyle [p &lt;0.01], lower presenteeism at work [p &lt;0.01] and a higher rate of unemployment [p &lt;0.01].

Conclusions
A significant percentage of IBD patients in remission suffer from anxiety and/or depression. Risk factors for these are CD, female gender, use of biologic medications, long-standing and/or perianal disease. Depression/anxiety was associated with a sedentary lifestyle, lower presenteeism at work and unemployment. Validated screening tools and appropriate referrals to psychologists and/or psychiatrists should be employed within IBD clinics.
Risk of depression after coronary artery bypass grafting: a SWEDEHEART population-based cohort study
Authors: Stenman Malin , Jeppsson Anders , Pivodic Aldina , Sartipy Ulrik , Nielsen Susanne J 
Published: March 2022

AbstractAimsA diagnosis of depression in patients with coronary heart disease is associated with worse outcomes. This study examined the long-term risk for new onset of depression after coronary artery bypass grafting (CABG) compared to an age- and sex-matched control group from the general population.Methods and resultsIn total, 125 418 primary isolated CABG patients and 495 371 matched controls were included from 1992 to 2017. The SWEDEHEART registry and three other national registers were used to acquire information about baseline characteristics and depression. The adjusted risk of depression was estimated by using Cox regression models adjusted for patient characteristics, and socioeconomic variables, described by hazard ratios (HR) and 95% confidence intervals (CI). In total, 6202 (4.9%) CABG patients and 17 854 (3.6%) controls developed depression. The cumulative incidence of depression was higher in CABG patients than in the control population [6.1%, 95% CI 6.0–6.3 vs. 4.7% (4.7–4.8), P &lt; 0.0001]. Overall, the CABG group had a marginally increased adjusted risk of depression compared to controls [adjusted HR (aHR): 1.05 (1.01–1.09), P = 0.0091]. In age-specific analyses, the increased risk compared to controls was only present in patients &lt;65 years [aHR: 1.19 (1.11–1.27), P &lt;0.0001] and was only evident during the first 5 years after surgery.ConclusionPatients who underwent CABG had a higher risk of new onset of depression compared to sex- and age-matched controls in the general population. The risk of depression was especially pronounced in younger patients during the first 5 years after surgery.
A forgotten pioneer: Eugene Marteney and the American renaissance of hautboy-making, 1962–1969
Authors: Howe Robert 
Published: March 2022

Abstract
Coming from a background in science and philosophy, the American electrical engineer Eugene Marteney (1934–2008) stumbled into hautboy-making in 1961–2, inspired by the playing of Josef Marx. Marteney learned the techniques of woodwind manufacture from Friedrich von Huene, who ultimately referred potential buyers to him. Selling hautboys after 1964, he did everything himself, from measuring antique specimens to designing and making the instruments and their reeds. He made connections with Marx, von Huene, Bruce Haynes, Paul Hailperin and, through him, with Michel Piguet and Bernhard Schermer, which would set off modern hautboy-making at original pitches. Marteney did this all half a decade before anyone else. In so doing he influenced all of the people we remember today as modern hautboy pioneers, laying the foundations for the Baroque revival of the 1970s and 80s.
In 1969, as Schermer and Haynes started making quality hautboys at original pitches, Marteney stopped doing so. His work was then forgotten until 2010. This article explores the hautboy-making of this remarkable man and shows how he influenced more well-known figures.