bluuebunny/crossref_metadata_2025_split huggingface dataset:
Legal Personhood in Normative Reasoning
Authors: Kurki Visa A.J. 
Published: September 2019

Abstract
This concluding chapter considers the role of legal personhood in normative argumentation, distinguishing normative questions that are ‘internal’ to law from ‘external’ normative questions. Particular focus is placed on the different normative claims regarding the legal status of animals. The chapter shows how the Bundle Theory can structure the most central accounts, including the widely-adopted ‘welfarist approach’, the ‘habeas corpus approach’ of the Nonhuman Rights Project, as well as the ‘abolitionist approach’ of Gary Francione. The import of the term ‘person’ in legal materials is discussed. The chapter ends with a conclusion that ties together the book as a whole.
Smell in Eighteenth-Century England
Authors: Tullett William 
Published: September 2019

Abstract
In England during the period between the 1670s and the 1820s a transformation took place in how smell and the senses were viewed. This book traces that transformation. The role of smell in creating medical and scientific knowledge came under intense scrutiny and the equation of smell with disease was actively questioned. Yet a new interest in smell’s emotive and idiosyncratic dimensions offered odours a new power in the sociable spaces of eighteenth-century England. Using a wide range of sources from diaries, letters, and sanitary records to satirical prints, consumer objects, and magazines, William Tullett traces how individuals and communities perceived the smells around them. From paint and perfume to onions and farts, this book highlights the smells that were good for eighteenth-century writers to think with. In doing so, the study challenges a popular, influential, and often cited narrative. Smell in Eighteenth-Century England is not a tale of the medicalization and deodorization of English olfactory culture. Instead, the book demonstrates that it was a new recognition of smell’s asocial-sociability, its capacity to create atmospheres of uncomfortable intimacy, that transformed the relationship between the senses and society. To trace this shift, the book also breaks new methodological ground. Smell in Eighteenth-Century England makes the case for new ways of thinking about the history of the senses, experience, and the body. Understanding the way past peoples perceived their world involves tracing processes of habituation, sensitization, and attention. These processes help explain which odours entered the archive and why they did so. They force us to recognise that the past was, for those who lived there, not just a place of unmitigated stench
The Five Nations model for prison health surveillance: lessons from practice across the UK and Republic of Ireland
Authors: Perrett S , Plugge E , Conaglen P , O’Moore E , Sturup-Toft S 
Published: September 2019

ABSTRACT

Background
Prison populations experience an increased burden of physical, mental and social health needs compared to the community, further impacted by the prison environment. Surveillance systems to monitor health and well-being trends in prisons are lacking, presenting a challenge to services planners, and policy makers who often lack evidence to inform decisions.

Method
The Five Nations Health and Justice Collaboration, a body of experts on prison health across the UK and Republic of Ireland (ROI), met to share and discuss challenges and opportunities to developing robust prison health surveillance systems that could inform local provision, guide national policy and enable cross-border comparisons.

Results
Challenges to robust prison health surveillance systems were shared across the UK and ROI. Methods of surveillance differed across nations and included performance indicators and outcome measures as part of local or national programs. All nations had strong public health infectious disease notification systems.

Conclusions
The Five Nations Health and Justice Collaboration is proposing a new model for prison health surveillance, based on established guidelines for public health surveillance but with additional features that recognize the uniqueness of the prison environment and need for a whole prison approach, built on collaboration and sharing of data between health and justice sectors.
Exploring the barriers and facilitators for the use of digital health technologies for the management of COPD: a qualitative study of clinician perceptions
Authors: Slevin P , Kessie T , Cullen J , Butler M W , Donnelly S C , Caulfield B 
Published: September 2019

Abstract

Background
Digital health technology (DHT) promises to support patients and healthcare professionals (HCPs) to optimize the management of chronic obstructive pulmonary disease (COPD). However, there is a lack of evidence demonstrating the effectiveness of DHT for the management of COPD. One reason for this is the lack of user-involvement in the development of DHT interventions in COPD meaning their needs and preferences are rarely accounted for in the design phase. Although HCP adoption issues have been identified in relation to DHT, little is known about the challenges perceived by HCPs providing care to COPD patients. Therefore, this study aims to qualitatively explore the barriers and facilitators HCPs perceive for the use of DHT in the management of COPD.

Methods
Participants (n = 32) were recruited using snowball sampling from two university hospitals and several general practitioner clinics. A semi-structured interview was conducted with each participant. NVivo 12 software was used to complete thematic analysis on the data.

Results
Themes identified include: data quality; evidence-based care; resource constraints; and digital literacy presented as barriers; and facilitators include the following themes: digital health training and education; improving HCP digital literacy; and Personalized prescribing. Patient-centered approaches, such as pulmonary rehabilitation and shared decision-making were suggested as implementation strategies to ease the adoption of digital health for the management of COPD.

Conclusion
These findings contribute new insights about the needs and preferences of HCPs working in COPD regarding DHT. The findings can be used to help mitigate user-experience issues by informing the design of person-centered implementation and adoption strategies for future digital health interventions in COPD.
Chronic Nucleoside Reverse Transcriptase Inhibitors Disrupt Mitochondrial Homeostasis and Promote Premature Endothelial Senescence
Authors: Chen Yi-Fan , Stampley James E , Irving Brian A , Dugas Tammy R 
Published: September 2019

AbstractCombination antiretroviral therapy (cART) has improved the life expectancy of HIV patients, thus increasing the number of people living with HIV (PLWH). However, cardiovascular diseases (CVD) are now one of the most prevalent causes of death among PLWH. Nucleoside reverse transcriptase inhibitors (NRTIs) are the backbone of cART, and the emtricitabine (FTC) and tenofovir disoproxil fumarate (TDF) coformulation is commonly used. In prior studies, acute NRTI treatment-induced endothelial dysfunction, increased reactive oxygen species production, and mitophagic activity, suggesting that mitochondrial dysfunction may be critical to NRTI-induced endothelial dysfunction. Mitochondrial dysfunction plays a causal role in endothelial senescence, whereas premature endothelial senescence can promote the development of CVD. We hypothesize that for chronic NRTI treatment, a disruption in mitochondrial homeostasis leads to premature endothelial senescence and predisposes PLWH to CVD. We used human aortic endothelial cells (HAEC) and HIV-1 transgenic (Tg26) mice to test the interrelationship between mitochondrial and vascular dysfunction after chronic NRTI treatment in vitro and in vivo. Mitochondrial DNA copy number was decreased in late-passage HAEC treated with NRTIs, and senescence-associated β-galactosidase accumulation was elevated. In late-passage HAEC, NRTIs decreased the activity of Parkin-mediated mitophagy. In Tg26 mice treated with FTC, plasma nitrite levels were decreased. Endothelium-dependent vasodilation in NRTI-treated Tg26 mice was also reduced. Our work suggests that long-term use of NRTI may disrupt mitochondrial homeostasis, induce premature endothelial senescence, and impair vascular function.
EventEpi–A Natural Language Processing Framework for Event-Based Surveillance
Authors: Abbood Auss , Ullrich Alexander , Busche Rüdiger , Ghozzi Stéphane 
Published: September 2019

AbstractAccording to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of epidemiologists sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural-language-processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We trained a naive Bayes classifier to find the single most likely one using RKI’s EBS database as labels. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using document and word embeddings. Two of the tested algorithms stood out: The multilayer perceptron performed best overall, with a precision of 0.19, recall of 0.50, specificity of 0.89, F1 of 0.28, and the highest tested index balanced accuracy of 0.46. The support-vector machine, on the other hand, had the highest recall (0.88) which can be of higher interest for epidemiologists. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code is publicly available at https://github.com/aauss/EventEpi.
Network-based clustering for drug sensitivity prediction in cancer cell lines
Authors: Pouryahya Maryam , Oh Jung Hun , Mathews James C. , Belkhatir Zehor , Moosmüller Caroline , Deasy Joseph O. , Tannenbaum Allen R. 
Published: September 2019

AbstractThe study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We show that prior clustering of the heterogenous cell lines and structurally diverse drugs significantly improves the accuracy of the prediction. In addition, it facilities the interpretability of the results and identification of molecular biomarkers which are significant for both clustering of the cell lines and predicting the drug response.
Evaluating Representations for Gene Ontology Terms
Authors: Duong Dat , Uppunda Ankith , Gai Lisa , Ju Chelsea , Zhang James , Chen Muhao , Eskin Eleazar , Li Jingyi Jessica , Chang Kai-Wei 
Published: September 2019

AbstractProtein functions can be described by the Gene Ontology (GO) terms, allowing us to compare the functions of two proteins by measuring the similarity of the terms assigned to them. Recent works have applied neural network models to derive the vector representations for GO terms and compute similarity scores for these terms by comparing their vector embeddings. There are two typical ways to embed GO terms into vectors; a model can either embed the definitions of the terms or the topology of the terms in the ontology. In this paper, we design three tasks to critically evaluate the GO embeddings of two recent neural network models, and further introduce additional models for embedding GO terms, adapted from three popular neural network frameworks: Graph Convolution Network (GCN), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT), which have not yet been explored in previous works. Task 1 studies edge cases where the GO embeddings may not provide meaningful similarity scores for GO terms. We find that all neural network based methods fail to produce high similarity scores for related terms when these terms have low Information Content values. Task 2 is a canonical task which estimates how well GO embeddings can compare functions of two orthologous genes or two interacting proteins. The best neural network methods for this task are those that embed GO terms using their definitions, and the differences among such methods are small. Task 3 evaluates how GO embeddings affect the performance of GO annotation methods, which predict whether a protein should be labeled by certain GO terms. When the annotation datasets contain many samples for each GO label, GO embeddings do not improve the classification accuracy. Machine learning GO annotation methods often remove rare GO labels from the training datasets so that the model parameters can be efficiently trained. We evaluate whether GO embeddings can improve prediction of rare labels unseen in the training datasets, and find that GO embeddings based on the BERT framework achieve the best results in this setting. We present our embedding methods and three evaluation tasks as the basis for future research on this topic.
The investigation of the role of VHL-HIF signaling in DNA repair and apoptosis in zebrafish
Authors: Kim HR , Santhakumar K , Markham E , Baldera D , Bryant HE , El-Khamisy SF , van Eeden FJ 
Published: September 2019

AbstractpVHL is a tumor suppressor. The lack of its function leads to various tumors, among which ccRCC (clear cell renal cell carcinoma) has the most serious outcome due to its resistance to chemotherapies and radiotherapies. Although HIF promotes the progression of ccRCC, the precise mechanism by which the loss of VHL leads to tumor initiation remains unclear. We exploited two zebrafish vhl mutants, vhl and vll, and Tg(phd3::GFP)i144 fish to identify crucial functions of Vhl in tumor initiation. Through the mutant analysis, we found that the role of pVHL in DNA repair is conserved in zebrafish Vll. Interestingly, we also discovered that Hif activation strongly suppressed genotoxic stress induced DNA repair defects and apoptosis in vll and brca2 mutants and in the embryos lacking ATM activity. These results suggest the potential of HIF as a clinical modulator that can protect cells from accumulating DNA damage and apoptosis which can lead to cancers and neurodegenerative disorders.
scHiCTools: a computational toolbox for analyzing single-cell Hi-C data
Authors: Li Xinjun , Feng Fan , Leung Wai Yan , Liu Jie 
Published: September 2019

AbstractSingle-cell Hi-C (scHi-C) sequencing technologies allow us to investigate three-dimensional chromatin organization at the single-cell level. However, we still need computational tools to deal with the sparsity of the contact maps from single cells and embed single cells in a lower-dimensional Euclidean space. This embedding helps us understand relationships between the cells in different dimensions such as cell-cycle dynamics and cell differentiation. Here, we present an open-source computational toolbox, scHiCTools, for analyzing single cell Hi-C data. The toolbox takes singlecell Hi-C data files as input, and projects single cells in a lower-dimensional Euclidean space. The toolbox includes three commonly used methods for smoothing scHi-C data (linear convolution, random walk, and network enhancing), three projection methods for embedding single cells (fastHiCRep, Selfish, and InnerProduct), three clustering methods for clustering cells (k-means, spectral clustering, and HiCluster) and a build-in function to visualize the cells embedding in a two-dimensional or three-dimensional plot. We benchmark the embedding performance and run time of these methods on a number of scHi-C datasets, and provide some suggestions for practice use. scHiCTools, based on Python3, can run on different platforms, including Linux, macOS, and Windows. Our software package is available at https://github.com/liu-bioinfo-lab/scHiCTools.
MYC-associated factor MAX is an essential regulator of the clock core network
Authors: Blaževitš Olga , Bolshette Nityanand , Vecchio Donatella , Guijarro Ana , Croci Ottavio , Campaner Stefano , Grimaldi Benedetto 
Published: September 2019

SummaryThe circadian transcriptional network is based on a competition between transcriptional activator and repressor complexes regulating the rhythmic expression of clock-controlled genes. We show here that the MYC-Associated factor X, MAX, plays a repressive role in this network and operates through its MYC-independent binding to E-box-containing regulatory regions within the promoters of circadian BMAL1 targets. This clock function of MAX is essential for maintaining a proper circadian rhythm but separated by the role of MAX as a partner of MYC in controlling cell proliferation. We also identified MAX Network Transcriptional repressor, MNT, as a fundamental partner of MAX-mediated circadian regulation. Collectively, our data indicate that MAX is an integral part of the core molecular clock and keeps the balance between positive and negative elements of the molecular clock machinery. Accordingly, alteration of MAX transcriptional complexes may contribute to circadian dysfunction in pathological contexts.
“Green pointillism”: detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras
Authors: Delpierre Nicolas , Soudani Kamel , Berveiller Daniel , Dufrêne Eric , Hmimina Gabriel , Vincent Gaëlle 
Published: September 2019

AbstractPhenological cameras have been used over a decade for identifying plant phenological markers (budburst, leaf senescence) and more generally the greenness dynamics of forest canopies. The analysis is usually carried out over the full camera field of view, with no particular analysis of the variability of phenological markers among trees.Here we show that images produced by phenological cameras can be used to quantify the within-population variability of budburst (WPVbb) in temperate deciduous forests. Using 7 site-years of image analyses, we report a strong correlation (r²=0.97) between the WPVbb determined with a phenological camera and its quantification through ground observation.We show that WPVbb varies strongly (by a factor of 4) from year to year in a given population, and that those variations are linked with temperature conditions during the budburst period, with colder springs associated to a higher differentiation of budburst (higher WPVbb) among trees.Deploying our approach at the continental scale, i.e. throughout phenological cameras networks, would improve the understanding of the spatial (across populations) and temporal (across years) variations of WPVbb, which have strong implications on forest functioning, tree fitness and phenological modelling.
H. pyloriinfection inhibits autophagy to aggravate DNA damage by p62-mediated Rad51 ubiquitination
Authors: Xie Chuan , Li Nianshuang , Wang Huan , He Cong , Hu Yi , Peng Chao , Ouyang Yaobin , Wang Dejie , Xie Yong , Chen Jiang , Shu Xu , Zhu Yin , Lu Nonghua 
Published: September 2019

AbstractHelicobacter pylori(H. pylori) infection is the strongest known risk factor for gastric carcinogenesis. DNA damage response (DDR) and autophagy play key roles in tumorigenic transformation. However, it remains unclear howH. pyloriinfection modulate DNA damage and autophagy. Here we report thatH. pyloriinfection promotes DNA damage via suppression of Rad51 expression through inhibition of autophagy and accumulation of p62 in gastric carcinogenesis. We find thatH. pyloriinfection caused alteration of DDR pathway and autophagy in gastric cells and Mongolian gerbils in a CagA-dependent manner. Moreover, loss of autophagy led to promotion of DNA damage inH. pylori-infected cells. Furthermore, knockdown of autophagic substrate p62 upregulated Rad51 expression, and p62 promoted ubiquitination of Rad51 via the direct interaction of the UBA domain with Rad51. Finally,H. pyloriinfection was associated with elevated levels of p62 in gastric intestinal metaplasia and decreased levels of Rad51 in dysplasia compared to theirH. pylori-counterparts. Our findings provide a novel mechanism into the linkage ofH. pyloriinfection, autophagy, DNA damage and gastric tumorigenesis.
Ultrasensitive Molecular Imaging of Mucosal Inflammation Using Leucocyte-Mimicking Particles Targeted to MAdCAM-1
Authors: Fournier Antoine P. , de Lizarrondo Sara Martinez , Rateau Adrien , Gerard-Brisou Axel , Waldner Maximilian J. , Neurath Markus F. , Vivien Denis , Docagne Fabian , Gauberti Maxime 
Published: September 2019

ABSTRACTMucosal tissues line the digestive, respiratory, urinary, mammary and reproductive tracts and play critical roles in health and disease as the primary barrier between the external world and the inner body. Clinical evaluation of mucosal tissues is currently performed using endoscopy, such as ileocolonoscopy for the intestinal mucosa, that causes significant patient discomfort and can lead to organ damage. Here, we developed a new contrast agent for molecular magnetic resonance imaging (MRI) that is targeted to mucosal vascular addressin cell adhesion molecule 1 (MAdCAM-1), an adhesion molecule overexpressed by inflamed mucosal tissues. We investigated the diagnostic performance of molecular MRI of MAdCAM-1 to detect mucosal inflammation in several models of acute and chronic intestinal inflammation in mice. We demonstrated that molecular MRI of MAdCAM-1 reveals disease activity and can evaluate the response to inflammatory treatments along the whole intestinal mucosa in clinically relevant models of inflammatory bowel diseases. We also provide evidence that this new technique can detect low, subclinical levels of mucosal inflammation. Molecular MRI of MAdCAM-1 has thus potential applications in early diagnosis, longitudinal follow-up and therapeutic response monitoring in diseases affecting mucosal tissues, such as inflammatory bowel diseases.One Sentence SummaryMolecular magnetic resonance imaging allows non-invasive evaluation of mucosal inflammation in clinically relevant experimental models.