Determining the Veracity of Rumours on Twitter

While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate mis- information often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users’ past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors’ profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis

Photon Radiation with MadDipole

We present the automation of a subtraction method for photon radiation using the dipole formalism within the MadGraph framework. The subtraction terms are implemented both in dimensional regularization and mass regularization for massless and massive cases and non-collinear-safe observables are accounted for.Comment: 23 pages, 2 figures, minor additions, references added, version published in JHE

Preoperative information for ICU patients to reduce anxiety during and after the ICU-stay: protocol of a randomized controlled trial [NCT00151554]

BACKGROUND: According to current evidence and psychological theorizing proper information giving seems to be a promising way to reduce patient anxiety. In the case of surgical patients, admission to the intensive care unit (ICU) is strongly associated with uncertainty, unpredictability and anxiety for the patient. Thus, ICU specific information could have a high clinical impact. This study investigates the potential benefits of a specifically designed ICU-related information program for patients who undergo elective cardiac, abdominal or thoracic surgery and are scheduled for ICU stay. METHODS/DESIGN: The trial is designed as a prospective randomized controlled trial including an intervention and a control group. The control group receives the standard preparation currently conducted by surgeons and anesthetists. The intervention group additionally receives a standardized information program with specific procedural, sensory and coping information about the ICU. A measurable clinical relevant difference regarding anxiety will be expected after discharge from ICU. Power calculation (α = 0.05; β = 0.20; Δ = 8.50 score points) resulted in a required sample size of N = 120 cardiac surgical patients (n = 60 vs. n = 60). Furthermore, N = 20 abdominal or thoracic surgical patients will be recruited (n = 10 vs. n = 10) to gain insight to a possible generalization to other patient groups. Additionally the moderating effect of specific patient attributes (need for cognition, high trait anxiety) will be investigated to identify certain patient groups which benefit most. DISCUSSION: The proposed study promises to strengthen evidence on effects of a specific, concise information program that addresses the information needs of patients scheduled for ICU stay

Model-based machine learning

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications

Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions

Objective: Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients ’ quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. Methods: Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence $ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. Results: Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78 % and 75%, respectively). The patterns were largely age-dependent an

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Evasion of IFN-γ Signaling by Francisella novicida Is Dependent upon Francisella Outer Membrane Protein C

Francisella tularensis is a Gram-negative facultative intracellular bacterium and the causative agent of the lethal disease tularemia. An outer membrane protein (FTT0918) of F. tularensis subsp. tularensis has been identified as a virulence factor. We generated a F. novicida (F. tularensis subsp. novicida) FTN_0444 (homolog of FTT0918) fopC mutant to study the virulence-associated mechanism(s) of FTT0918.The ΔfopC strain phenotype was characterized using immunological and biochemical assays. Attenuated virulence via the pulmonary route in wildtype C57BL/6 and BALB/c mice, as well as in knockout (KO) mice, including MHC I, MHC II, and µmT (B cell deficient), but not in IFN-γ or IFN-γR KO mice was observed. Primary bone marrow derived macrophages (BMDM) prepared from C57BL/6 mice treated with rIFN-γ exhibited greater inhibition of intracellular ΔfopC than wildtype U112 strain replication; whereas, IFN-γR KO macrophages showed no IFN-γ-dependent inhibition of ΔfopC replication. Moreover, phosphorylation of STAT1 was downregulated by the wildtype strain, but not the fopC mutant, in rIFN-γ treated macrophages. Addition of NG-monomethyl-L-arginine, an NOS inhibitor, led to an increase of ΔfopC replication to that seen in the BMDM unstimulated with rIFN-γ. Enzymatic screening of ΔfopC revealed aberrant acid phosphatase activity and localization. Furthermore, a greater abundance of different proteins in the culture supernatants of ΔfopC than that in the wildtype U112 strain was observed.F. novicida FopC protein facilitates evasion of IFN-γ-mediated immune defense(s) by down-regulation of STAT1 phosphorylation and nitric oxide production, thereby promoting virulence. Additionally, the FopC protein also may play a role in maintaining outer membrane stability (integrity) facilitating the activity and localization of acid phosphatases and other F. novicida cell components

Lifted graphical models: a survey

Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field

Diversity arrays technology (DArT) markers in apple for genetic linkage maps

Diversity Arrays Technology (DArT) provides a high-throughput whole-genome genotyping platform for the detection and scoring of hundreds of polymorphic loci without any need for prior sequence information. The work presented here details the development and performance of a DArT genotyping array for apple. This is the first paper on DArT in horticultural trees. Genetic mapping of DArT markers in two mapping populations and their integration with other marker types showed that DArT is a powerful high-throughput method for obtaining accurate and reproducible marker data, despite the low cost per data point. This method appears to be suitable for aligning the genetic maps of different segregating populations. The standard complexity reduction method, based on the methylation-sensitive PstI restriction enzyme, resulted in a high frequency of markers, although there was 52–54% redundancy due to the repeated sampling of highly similar sequences. Sequencing of the marker clones showed that they are significantly enriched for low-copy, genic regions. The genome coverage using the standard method was 55–76%. For improved genome coverage, an alternative complexity reduction method was examined, which resulted in less redundancy and additional segregating markers. The DArT markers proved to be of high quality and were very suitable for genetic mapping at low cost for the apple, providing moderate genome coverage