bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Post-Thrombotic Syndrome with Ultrasonographic Follow-Up after Deep Vein Thrombosis: A Systematic Review and Meta-Analysis
Authors: Dronkers C. E. A. , Mol G. C. , Maraziti G. , van de Ree M. A. , Huisman M. V. , Becattini C. , Klok F. A. 
Published: July 2018

Background Post-thrombotic syndrome (PTS) is a common and potential severe complication of deep venous thrombosis (DVT). Elastic compression stocking therapy may prevent PTS if worn on a daily basis, but stockings are cumbersome to apply and uncomfortable to wear. Hence, identification of predictors of PTS may help physicians to select patients at high risk of PTS.
Aims This article identifies ultrasonography (US) parameters assessed during or after treatment of DVT of the leg, that predict PTS.
Methods This is a systematic review and meta-analysis study. Databases were searched for prospective studies including consecutive patients with DVT who received standardized treatment, had an US during follow-up assessing findings consistent with vascular damage after DVT and had a follow-up period of at least 6 months for the occurrence of PTS assessed by a standardized protocol.
Results The literature search revealed 1,156 studies of which 1,068 were irrelevant after title and abstract screening by three independent reviewers. After full-text screening, 12 relevant studies were included, with a total of 2,684 analysed patients. Two US parameters proved to be predictive of PTS: residual vein thrombosis, for a pooled odds ratio (OR) of 2.17 (95% confidence interval [CI], 1.79–2.63) and venous reflux at the popliteal level, for a pooled OR of 1.34 (95% CI, 1.03–1.75).
Conclusion The US features reflux and residual thrombosis measured at least 6 weeks after DVT predict PTS. Whether these features may be used to identify patients who may benefit from compression therapy remains to be assessed in further studies.
Fentanyl Delays the Platelet Inhibition Effects of Oral Ticagrelor: Full Report of the PACIFY Randomized Clinical Trial
Authors: Ibrahim Khalil , Shah Rohan , Goli Rakesh , Kickler Thomas , Clarke William , Hasan Rani , Blumenthal Roger , Thiemann David , Resar Jon , Schulman Steven , McEvoy John 
Published: July 2018

Morphine delays oral P2Y12 platelet inhibitor absorption and is associated with adverse outcomes after myocardial infarction. Consequently, many physicians and first responders are now considering fentanyl as an alternative. We conducted a single-centre trial randomizing cardiac patients undergoing coronary angiography to intravenous fentanyl or not. All participants received local anaesthetic and intravenous midazolam. Those requiring percutaneous coronary intervention (PCI) with stenting received 180 mg oral ticagrelor intra-procedurally. The primary outcome was area under the ticagrelor plasma concentration–time curve (AUC0–24 hours). The secondary outcomes were platelet function assessed at 2 hours after loading, measured by P2Y12 reaction units (PRUs) and light transmission platelet aggregometry. Troponin-I was measured post-PCI using a high-sensitivity troponin-I assay (hs-TnI). All participants completed a survey of pain and anxiety. Of the 212 randomized, 70 patients required coronary stenting and were loaded with ticagrelor. Two participants in the no-fentanyl arm crossed over to receive fentanyl for pain. In as-treated analyses, ticagrelor concentrations were higher in the no-fentanyl arm (AUC0–24 hours 70% larger, p = 0.03). Platelets were more inhibited by 2 hours in the no-fentanyl arm (71 vs. 113 by PRU, p = 0.03, and 25% vs. 41% for adenosine diphosphate response by platelet aggregation, p < 0.01). Mean hs-TnI was higher with fentanyl at 2 hours post-PCI (11.9 vs. 7.0 ng/L, p = 0.04) with a rate of enzymatic myocardial infarction of 11% for fentanyl and 0% for no-fentanyl (p = 0.08). No statistical differences in self-reported pain or anxiety were found. In conclusion, fentanyl administration can impair ticagrelor absorption and delay platelet inhibition, resulting in mild excess of myocardial damage. This newly described drug interaction should be recognized by physicians and suggests that the interaction between opioids and oral P2Y12 platelet inhibitors is a drug class effect associated with all opioids.
Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02683707 (NCT02683707).
Zielgerichtete Erstdiagnostik bei Typ-1-Diabetes
Authors: Kalscheuer Hannes , Lehnert Hendrik 
Published: July 2018

ZusammenfassungTyp-1-Diabetes mellitus (T1DM) nimmt weltweit zu und ist eine der häufigsten chronischen Erkrankungen im Kindesalter. Allerdings manifestiert sich ein T1DM nicht selten erst im Erwachsenenalter – dann wird die eindeutige klinische Abgrenzung zum Typ-2-Diabetes (T2DM) mitunter schwierig. Andere Diabetesformen und Autoimmunsyndrome müssen ebenfalls berücksichtigt werden. Dieser Beitrag stellt die Möglichkeiten einer zielführenden Diagnostik vor.
Experimental Evidence on Policy Approaches That Link Agricultural Subsidies to Water Quality Outcomes
Authors: Palm‐Forster Leah H. , Suter Jordan F. , Messer Kent D. 
Published: July 2018

AbstractImproving water quality in agricultural landscapes is an ongoing challenge, and most agri‐environmental programs in the United States rely on voluntary adoption of conservation practices. Conservation‐compliance initiatives require producers to meet specific conservation standards to qualify for payments from farm programs. However, these requirements do not require actual improvements in observed water quality. In this study, we introduce policies to reduce nonpoint source pollution that link eligibility for agricultural subsidies to compliance with water quality goals. We then use economic laboratory experiments to provide empirical evidence related to the performance of these policies. In the policy treatments, participants risk losing some or all of their subsidies if the ambient pollution level exceeds an announced target. A novel feature of our experiment is that we test a policy treatment that ensures that no subsidies are lost if a producer implements a verifiable conservation technology that reduces emissions. In these experiments, policies that link the receipt of subsidies to ambient water quality nearly achieve the socially optimal level of pollution. The results suggest that water quality policies that rely on the threat of subsidy reductions are a potentially viable option for reducing aggregate water pollution. Although a policy that allows polluters to avoid potential losses by implementing a verifiable conservation technology could increase political support for ambient‐based policies, our results suggest that, depending upon the magnitudes of social damages from emissions and the cost of implementing a conservation technology, such policies may be less cost‐effective for a comparable reduction in pollution.
Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches
Authors: Nguyen Dai Hai , Nguyen Canh Hao , Mamitsuka Hiroshi 
Published: July 2018

AbstractMotivation: Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.
Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Authors: Tsubaki Masashi , Tomii Kentaro , Sese Jun 
Published: July 2018

Abstract

Motivation
In bioinformatics, machine learning-based methods that predict the compound–protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems. For the CPI problem, data are provided as discrete symbolic data, i.e. compounds are represented as graphs where the vertices are atoms, the edges are chemical bonds, and proteins are sequences in which the characters are amino acids. In this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new CPI prediction approach by combining a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.

Results
Our experiments using three CPI datasets demonstrated that the proposed end-to-end approach achieves competitive or higher performance as compared to various existing CPI prediction methods. In addition, the proposed approach significantly outperformed existing methods on an unbalanced dataset. This suggests that data-driven representations of compounds and proteins obtained by end-to-end GNNs and CNNs are more robust than traditional chemical and biological features obtained from databases. Although analyzing deep learning models is difficult due to their black-box nature, we address this issue using a neural attention mechanism, which allows us to consider which subsequences in a protein are more important for a drug compound when predicting its interaction. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model even when modeling is performed using real-valued representations instead of discrete features.

Availability and implementation
https://github.com/masashitsubaki

Supplementary information
Supplementary data are available at Bioinformatics online.
Statistical tests for detecting variance effects in quantitative trait studies
Authors: Dumitrascu Bianca , Darnell Gregory , Ayroles Julien , Engelhardt Barbara E 
Published: July 2018

AbstractMotivationIdentifying variants, both discrete and continuous, that are associated with quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are limited to identifying mean effects, or associations between genotype or covariates and the mean value of a quantitative trait. It is possible, however, that a variant may affect the variance of the quantitative trait in lieu of, or in addition to, affecting the trait mean. Here, we develop a general methodology to identify covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model (BTH). We compare BTH with existing methods to detect variance effects across a large range of simulations drawn from scenarios common to the analysis of quantitative traits.ResultsWe find that BTH and a double generalized linear model (dglm) outperform classical tests used for detecting variance effects in recent genomic studies. We show BTH and dglm are less likely to generate spurious discoveries through simulations and application to identifying methylation variance QTs and expression variance QTs. We identify four variance effects of sex in the Cardiovascular and Pharmacogenetics study. Our work is the first to offer a comprehensive view of variance identifying methodology. We identify shortcomings in previously used methodology and provide a more conservative and robust alternative. We extend variance effect analysis to a wide array of covariates that enables a new statistical dimension in the study of sex and age specific quantitative trait effects.Availability and implementationhttps://github.com/b2du/bth.Supplementary informationSupplementary data are available at Bioinformatics online.
PingPongPro: a tool for the detection of piRNA-mediated transposon-silencing in small RNA-Seq data
Authors: Uhrig Sebastian , Klein Holger 
Published: July 2018

Abstract

Summary
Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs which guide endonucleases to mRNAs of actively transcribed transposons in order to prevent their translation. The resulting mRNA fragments induce a positive feedback loop (the ‘ping-pong cycle’), which reinforces piRNA production and hence the transposon-silencing effect. PingPongPro is a command-line tool to scan small RNA-Seq data for signs of ping-pong cycle activity. It implements a novel algorithm that combines empirical probabilities in a multi-factor model to accurately identify transposons which are suppressed through the ping-pong cycle.

Availability and implementation
Source code, a user manual, and binaries for Microsoft Windows and Linux are available at https://github.com/suhrig/pingpongpro under the GPLv3 license.

Supplementary information
Supplementary data are available at Bioinformatics online.
GDAP2 mutations implicate susceptibility to cellular stress in a new form of cerebellar ataxia
Authors: Eidhof Ilse , Baets Jonathan , Kamsteeg Erik-Jan , Deconinck Tine , van Ninhuijs Lisa , Martin Jean-Jacques , Schüle Rebecca , Züchner Stephan , De Jonghe Peter , Schenck Annette , van de Warrenburg Bart P 
Published: July 2018

AbstractAutosomal recessive cerebellar ataxias are a group of rare disorders that share progressive degeneration of the cerebellum and associated tracts as the main hallmark. Here, we report two unrelated patients with a new subtype of autosomal recessive cerebellar ataxia caused by biallelic, gene-disruptive mutations inGDAP2, a gene previously not implicated in disease. Both patients had onset of ataxia in the fourth decade. Other features included progressive spasticity and dementia. Neuropathological examination showed degenerative changes in the cerebellum, olive inferior, thalamus, substantia nigra, and pyramidal tracts, as well as tau pathology in the hippocampus and amygdala. To provide further evidence for a causative role ofGDAP2 mutations in autosomal recessive cerebellar ataxia pathophysiology, its orthologous gene was investigated in the fruit flyDrosophila melanogaster. Ubiquitous knockdown ofDrosophila Gdap2 resulted in shortened lifespan and motor behaviour anomalies such as righting defects, reduced and uncoordinated walking behaviour, and compromised flight. Gdap2 expression levels responded to stress treatments in control flies, and Gdap2 knockdown flies showed increased sensitivity to deleterious effects of stressors such as reactive oxygen species and nutrient deprivation. Thus,Gdap2 knockdown inDrosophila andGDAP2 loss-of-function mutations in humans lead to locomotor phenotypes, which may be mediated by altered responses to cellular stress.
Standardized classification and framework for reporting, interpreting, and analysing medication non-adherence in cardiovascular clinical trials: a consensus report from the Non-adherence Academic Research Consortium (NARC)
Authors: Valgimigli Marco , Garcia-Garcia Hector M , Vrijens Bernard , Vranckx Pascal , McFadden Eugène P , Costa Francesco , Pieper Karen , Vock David M , Zhang Min , Van Es Gerrit-Anne , Tricoci Pierluigi , Baber Usman , Steg Gabriel , Montalescot Gilles , Angiolillo Dominick J , Serruys Patrick W , Farb Andrew , Windecker Stephan , Kastrati Adnan , Colombo Antonio , Feres Fausto , Jüni Peter , Stone Gregg W , Bhatt Deepak L , Mehran Roxana , Tijssen Jan G P 
Published: July 2018

Abstract
Non-adherence has been well recognized for years to be a common issue that significantly impacts clinical outcomes and health care costs. Medication adherence is remarkably low even in the controlled environment of clinical trials where it has potentially complex major implications. Collection of non-adherence data diverge markedly among cardiovascular randomized trials and, even where collected, is rarely incorporated in the statistical analysis to test the consistency of the primary endpoint(s). The imprecision introduced by the inconsistent assessment of non-adherence in clinical trials might confound the estimate of the calculated efficacy of the study drug. Hence, clinical trials may not accurately answer the scientific question posed by regulators, who seek an accurate estimate of the true efficacy and safety of treatment, or the question posed by payers, who want a reliable estimate of the effectiveness of treatment in the marketplace after approval. The Non-adherence Academic Research Consortium is a collaboration among leading academic research organizations, representatives from the U.S. Food and Drug Administration and physician-scientists from the USA and Europe. One in-person meeting was held in Madrid, Spain, culminating in a document describing consensus recommendations for reporting, collecting, and analysing adherence endpoints across clinical trials. The adoption of these recommendations will afford robustness and consistency in the comparative safety and effectiveness evaluation of investigational drugs from early development to post-marketing approval studies. These principles may be useful for regulatory assessment, as well as for monitoring local and regional outcomes to guide quality improvement initiatives.
Age and growth rate estimations of the commercially fished gastropod Buccinum undatum
Authors: Hollyman Philip R , Chenery Simon R N , Leng Melanie J , Laptikhovsky Vladimir V , Colvin Charlotte N , Richardson Christopher A 
Published: July 2018

Abstract
Calculating age and growth rate for the commercially important whelk, Buccinum undatum in the aid of fishery management has historically been undertaken using growth rings on the organic operculum. This is difficult due to their poor readability and confusion between two different sets of growth lines present. Recent work presented the calcium carbonate statolith as an alternative for age determination of B. undatum. Here we compare the use of statoliths and opercula, comparing their readability and creating growth curves for three distinct populations across the United Kingdom. Using these data, we also test the most appropriate growth equation to model this species. Lastly, we use oxygen isotope analysis of the shells to assign accurate ages to several individuals from each site. These data were used to test the accuracy of statolith and operculum ages. Statoliths, whilst more time consuming to process have improved clarity and accuracy compared with the opercula. This improved readability has highlighted that a Gompertz growth function should be used for populations of this species, when in past studies, von Bertalanffy is often used. Statoliths are a viable improvement to opercula when assessing B. undatum in the context of fishery monitoring and management.
Marine environmental vulnerability and cumulative risk profiles to support ecosystem-based adaptive maritime spatial planning
Authors: Aps Robert , Herkül Kristjan , Kotta Jonne , Cormier Roland , Kostamo Kirsi , Laamanen Leena , Lappalainen Juho , Lokko Külli , Peterson Anneliis , Varjopuro Riku 
Published: July 2018

Abstract
Human use of marine and coastal areas is increasing worldwide, resulting in conflicts between different interests for marine space, overexploitation of marine resources, and environmental degradation. In this study we developed a methodology that combines assessments of marine environmental vulnerability and cumulative human pressures to support the processes of ecosystem-based adaptive maritime spatial planning. The methodology is built on the spatially explicit marine environmental vulnerability profile (EVP) that is an aggregated product of the distribution of essential nature values (habitat-forming benthic macroalgal and invertebrate species, benthic species richness, birds and seals as top marine predators) and their sensitivities to disturbances. The marine environmental cumulative risk profile (ERP) combines the EVP and the HELCOM Baltic Sea Pressure Index (BSPI), the latter representing the spatial distribution of intensities of cumulative anthropogenic pressures. The ERP identifies areas where environmental risks are the highest due to both long recoveries of the biota and high intensities of human pressures. This methodology can be used in any other sea areas by modifying the list of nature values, their sensitivity to disturbances, and the intensities of human pressure.
Optimal public health intervention in a behavioural vaccination model: the interplay between seasonality, behaviour and latency period
Authors: Buonomo Bruno , Della Marca Rossella , d’Onofrio Alberto 
Published: July 2018

AbstractHesitancy and refusal of vaccines preventing childhood diseases are spreading due to ‘pseudo-rational’ behaviours: parents overweigh real and imaginary side effects of vaccines. Nonetheless, the ‘Public Health System’ (PHS) may enact public campaigns to favour vaccine uptake. To determine the optimal time profiles for such campaigns, we apply the optimal control theory to an extension of the susceptible-infectious-removed (SIR)-based behavioural vaccination model by d’Onofrio et al. (2012, PLoS ONE, 7, e45653). The new model is of susceptible-exposed-infectious-removed (SEIR) type under seasonal fluctuations of the transmission rate. Our objective is to minimize the total costs of the disease: the disease burden, the vaccination costs and a less usual cost: the economic burden to enact the PHS campaigns. We apply the Pontryagin minimum principle and numerically explore the impact of seasonality, human behaviour and latency rate on the control and spread of the target disease. We focus on two noteworthy case studies: the low (resp. intermediate) relative perceived risk of vaccine side effects and relatively low (resp. very low) speed of imitation. One general result is that seasonality may produce a remarkable impact on PHS campaigns aimed at controlling, via an increase of the vaccination uptake, the spread of a target infectious disease. In particular, a higher amplitude of the seasonal variation produces a higher effort and this, in turn, beneficially impacts the induced vaccine uptake since the larger is the strength of seasonality, the longer the vaccine propensity remains large. However, such increased effort is not able to fully compensate the action of seasonality on the prevalence.
Sharp Area Bounds for Free Boundary Minimal Surfaces in Conformally Euclidean Balls
Authors: Freidin Brian , McGrath Peter 
Published: July 2018

Abstract
We prove that the area of a free boundary minimal surface $\Sigma ^2 \subset B^n$, where $B^n$ is a geodesic ball contained in a round hemisphere $\mathbb{S}^n_+$, is at least as big as that of a geodesic disk with the same radius as $B^n$; equality is attained only if $\Sigma $ coincides with such a disk. More generally, we prove analogous results for a class of conformally euclidean ambient spaces. This follows works of Brendle and Fraser–Schoen in the euclidean setting.