Search results for "Prediction."
showing 10 items of 490 documents
Prognostic Value of Troponins in Patients With or Without Coronary Heart Disease: Is it Dependent on Structure and Biology?
2020
Convincing evidence has emerged that cardiac troponins (cTns) T and I are the biochemical gold standard for diagnosing cardiac injury, and may also be used as efficient screening and risk stratification tools, especially when measured with the new high-sensitivity (hs-) immunoassays. In this narrative review, we aim to explore and critically discuss the results of recent epidemiological studies that have attempted to characterise the prognostic value of cTns in patients with or without cardiovascular disease, and then interpret this information according to cTn biology. Overall, all recent studies agree that higher blood levels of cTns reflect the larger risk of cardiovascular events and/or…
New set of 2D/3D thermodynamic indices for proteins. A formalism based on "Molten Globule" theory
2010
Abstract We define eight new macromolecular indices, and several related descriptors for proteins. The coarse grained methodology used for its deduction ensures its fast execution and becomes a powerful potential tool to explore large databases of protein structures. The indices are intended for stability studies, predicting Φ -values, predicting folding rate constants, protein QSAR/QSPR as well as protein alignment studies. Also, these indices could be used as scoring function in protein-protein docking or 3D protein structure prediction algorithms and any others applications which need a numerical code for proteins and/or residues from 2D or 3D format.
First global next-to-leading order determination of diffractive parton distribution functions and their uncertainties within the {\tt xFitter} framew…
2018
We present {\tt GKG18-DPDFs}, a next-to-leading order (NLO) QCD analysis of diffractive parton distribution functions (diffractive PDFs) and their uncertainties. This is the first global set of diffractive PDFs determined within the {\tt xFitter} framework. This analysis is motivated by all available and most up-to-date data on inclusive diffractive deep inelastic scattering (diffractive DIS). Heavy quark contributions are considered within the framework of the Thorne-Roberts (TR) general mass variable flavor number scheme (GM-VFNS). We form a mutually consistent set of diffractive PDFs due to the inclusion of high-precision data from H1/ZEUS combined inclusive diffractive cross sections me…
Search for the Associated Production of the Standard-Model Higgs Boson in the All-Hadronic Channel
2009
We report on a search for the standard-model Higgs boson in pp collisions at s=1.96 TeV using an integrated luminosity of 2.0 fb(-1). We look for production of the Higgs boson decaying to a pair of bottom quarks in association with a vector boson V (W or Z) decaying to quarks, resulting in a four-jet final state. Two of the jets are required to have secondary vertices consistent with B-hadron decays. We set the first 95% confidence level upper limit on the VH production cross section with V(-> qq/qq('))H(-> bb) decay for Higgs boson masses of 100-150 GeV/c(2) using data from run II at the Fermilab Tevatron. For m(H)=120 GeV/c(2), we exclude cross sections larger than 38 times the standard-m…
THERP and HEART integrated methodology for human error assessment
2015
Abstract THERP and HEART integrated methodology is proposed to investigate accident scenarios that involve operator errors during high-dose-rate (HDR) treatments. The new approach has been modified on the basis of fuzzy set concept with the aim of prioritizing an exhaustive list of erroneous tasks that can lead to patient radiological overexposures. The results allow for the identification of human errors that are necessary to achieve a better understanding of health hazards in the radiotherapy treatment process, so that it can be properly monitored and appropriately managed.
The upgraded ISOLDE yield database – A new tool to predict beam intensities
2020
At the CERN-ISOLDE facility a variety of radioactive ion beams are available to users of the facility. The number of extractable isotopes estimated from yield database data exceeds 1000 and is still increasing. Due to high demand and scarcity of available beam time, precise experiment planning is required. The yield database stores information about radioactive beam yields and the combination of target material and ion source needed to extract a certain beam along with their respective operating conditions. It allows to investigate the feasibility of an experiment and the estimation of required beamtime. With the increasing demand for ever more exotic beams, needs arise to extend the functi…
Understanding Prediction Limits Through Unbiased Branches
2006
The majority of currently available branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches which are difficult-to-predict. In this paper, we quantify and evaluate the impact of unbiased branches and show that any gain in prediction accuracy is proportional to the frequency of unbiased branches. By using the SPECcpu2000 integer benchmarks we show that there are a significant proportion of unbiased branches which severely impact on prediction accuracy (averaging between 6% and 24% depending on the prediction context used).
Joint use of cardio-embolic and bleeding risk scores in elderly patients with atrial fibrillation
2013
Background Scores for cardio-embolic and bleeding risk in patients with atrial fibrillation are described in the literature. However, it is not clear how they co-classify elderly patients with multimorbidity, nor whether and how they affect the physician's decision on thromboprophylaxis. Methods Four scores for cardio-embolic and bleeding risks were retrospectively calculated for ⥠65 year old patients with atrial fibrillation enrolled in the REPOSI registry. The co-classification of patients according to risk categories based on different score combinations was described and the relationship between risk categories tested. The association between the antithrombotic therapy received and t…
Machine learning in management accounting research: Literature review and pathways for the future
2021
This paper explores the possibilities of machine learning (ML) methods in management accounting research and showcases one future avenue in practice by applying ML-based textual literature review to ML/AI research in accounting. The review reveals that machine learning methods in management accounting (MA) are still in their infancy, and current research in accounting has progressed in and focused mainly on three areas related to ML and AI: 1) effects on the field of accounting and the development of the accounting profession, 2) textual analysis related to accounting data/reports, and 3) prediction methods. Based on our literature review and recently published related ML research from othe…
The Stroke Riskometer (TM) App: Validation of a data collection tool and stroke risk predictor
2014
Background The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app fo…