Signature features with the visibility transformation

The signature in rough path theory provides a graduated summary of a path through an examination of the effects of its increments. Inspired by recent developments of signature features in the context of machine learning, we explore a transformation that is able to embed the effect of the absolute position of the data stream into signature features. This unified feature is particularly effective for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values

Decisions by 'Science Proficient' Year 10 Students About Post-Compulsory High School Science Enrolment: A Sociocultural Exploration

Motivated by chronic declines in post-compulsory high school science participation, this research provides a new perspective on the enrolment decisions of science proficient Year 10 students in New South Wales (NSW). The study adapted the 'multiple worlds' model of Phelan, Davidson and Cao (1991) to explore students' perceptions of their family, peer, school science and mass media worlds, for influences on their decisions about enrolling in post-compulsory science courses. A survey of 196 science proficient students, in six schools, provided a context for interviews with 37 students deciding for, or against, taking further science. The study considered influences within each world, and the effects of congruency or incongruency between cultural features of different worlds. The opinions of 24 science teachers regarding the enrolment decisions of science proficient students provided a triangulation of perspectives. The study found science proficient students often cross referenced perceptions of the attitudes and values within family and school science worlds when deciding whether to take science courses. In particular, the resources of cultural and social capital within students' families were strongly influential in many decisions, since experiences of school science alone did not tend to encourage further participation, particularly in the physical sciences. Teachers' opinions that science proficient students were being drawn away from science courses and careers by external influences were not supported by students' narratives

Leukocyte and serum S100A8/S100A9 expression reflects disease activity in ANCA-associated vasculitis and glomerulonephritis.

Antineutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV) commonly results in glomerulonephritis, in which neutrophils and monocytes have important roles. The heterodimer calprotectin (S100A8/S100A9, mrp8/14) is a Toll-like receptor-4 ligand found in neutrophils and monocytes and is elevated in inflammatory conditions. By immunohistochemistry of renal biopsies, patients with focal or crescentic glomerular lesions were found to have the highest expression of calprotectin and those with sclerotic the least. Serum levels of calprotectin as measured by ELISA were elevated in patients with active AAV and the levels decreased but did not normalize during remission, suggesting subclinical inflammation. Calprotectin levels in patients with limited systemic disease increased following treatment withdrawal and were significantly elevated in patients who relapsed compared with those who did not. As assessed by flow cytometry, patients with AAV had higher monocyte and neutrophil cell surface calprotectin expression than healthy controls, but this was not associated with augmented mRNA expression in CD14(+) monocytes or CD16(+) neutrophils. Thus, serum calprotectin is a potential disease biomarker in patients with AAV, and may have a role in disease pathogenesis

New Horizons in the use of routine data for ageing research

The past three decades have seen a steady increase in the availability of routinely collected health and social care data and the processing power to analyse it. These developments represent a major opportunity for ageing research, especially with the integration of different datasets across traditional boundaries of health and social care, for prognostic research and novel evaluations of interventions with representative populations of older people. However, there are considerable challenges in using routine data at the level of coding, data analysis and in the application of findings to everyday care. New Horizons in applying routine data to investigate novel questions in ageing research require a collaborative approach between clinicians, data scientists, biostatisticians, epidemiologists and trial methodologists. This requires building capacity for the next generation of research leaders in this important area. There is a need to develop consensus code lists and standardised, validated algorithms for common conditions and outcomes that are relevant for older people to maximise the potential of routine data research in this group. Lastly, we must help drive the application of routine data to improve the care of older people, through the development of novel methods for evaluation of interventions using routine data infrastructure. We believe that harnessing routine data can help address knowledge gaps for older people living with multiple conditions and frailty, and design interventions and pathways of care to address the complex health issues we face in caring for older people

Support and Assessment for Fall Emergency Referrals (SAFER 1): Cluster Randomised Trial of Computerised Clinical Decision Support for Paramedics

Objective: To evaluate effectiveness, safety and cost-effectiveness of Computerised Clinical Decision Support (CCDS) for paramedics attending older people who fall. Design: Cluster trial randomised by paramedic; modelling. Setting: 13 ambulance stations in two UK emergency ambulance services. Participants: 42 of 409 eligible paramedics, who attended 779 older patients for a reported fall. Interventions: Intervention paramedics received CCDS on Tablet computers to guide patient care. Control paramedics provided care as usual. One service had already installed electronic data capture. Main Outcome Measures: Effectiveness: patients referred to falls service, patient reported quality of life and satisfaction, processes of care. Safety: Further emergency contacts or death within one month. Cost-Effectiveness Costs and quality of life. We used findings from published Community Falls Prevention Trial to model cost-effectiveness. Results: 17 intervention paramedics used CCDS for 54 (12.4%) of 436 participants. They referred 42 (9.6%) to falls services, compared with 17 (5.0%) of 343 participants seen by 19 control paramedics [Odds ratio (OR) 2.04, 95% CI 1.12 to 3.72]. No adverse events were related to the intervention. Non-significant differences between groups included: subsequent emergency contacts (34.6% versus 29.1%; OR 1.27, 95% CI 0.93 to 1.72); quality of life (mean SF12 differences: MCS −0.74, 95% CI −2.83 to +1.28; PCS −0.13, 95% CI −1.65 to +1.39) and non-conveyance (42.0% versus 36.7%; OR 1.13, 95% CI 0.84 to 1.52). However ambulance job cycle time was 8.9 minutes longer for intervention patients (95% CI 2.3 to 15.3). Average net cost of implementing CCDS was £208 per patient with existing electronic data capture, and £308 without. Modelling estimated cost per quality-adjusted life-year at £15,000 with existing electronic data capture; and £22,200 without. Conclusions: Intervention paramedics referred twice as many participants to falls services with no difference in safety. CCDS is potentially cost-effective, especially with existing electronic data capture

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs

Genetic associations with sporadic cerebral small vessel disease

Background: Cerebral small vessel disease (SVD) causes substantial cognitive, psychiatric and physical disabilities. Despite its common nature, SVD pathogenesis and molecular mechanisms remain poorly understood, and prevention and treatment are probably suboptimal. Identifying the genetic determinants of SVD will improve understanding and may help identify novel treatment targets. The aim of this thesis is to better understand genetic associations with SVD through investigating its pathological, radiological and clinical phenotypes. Methods: To unravel the genetic associations with SVD, I used three complementary approaches. First, I performed a systematic review looking at existing intracerebral haemorrhage (ICH) classification systems and their reliability, to help inform future studies of ICH genetics. Second, I performed a series of systematic reviews and meta-analyses, investigating associations between genetic polymorphisms and histopathologically confirmed cerebral amyloid angiopathy (CAA). Third, I performed meta-analyses of existing genome-wide datasets to determine associations of >1000 common single nucleotide polymorphisms (SNP) in the COL4A1/COL4A2 genomic region with clinico-radiological SVD phenotypes: ICH and its subtypes, ischaemic stroke and its subtypes, and white matter hyperintensities. Results: The reliability of existing ICH classification systems appeared excellent in eight studies conducted in specialist centres with experienced raters, although these existing systems have several limitations. In my systematic evaluation of CAA genetics, meta-analyses of 24 studies including 3520 participants showed robust evidence for a dose-dependent association between APOE ɛ4 and histopathological CAA. There was, however, no convincing association between APOE ɛ2 and presence of CAA in a meta-analysis of 11 studies including 1640 participants. Meta-analyses of five studies including 497 participants showed, contrary to an existing popular hypothesis, that while APOE 4 may increase the risk of developing severe CAA vasculopathy, there is no clear evidence to support a role of ɛ2. There were few data about the role of APOE in hereditary CAA, but in the three studies that had looked at this, there was no evidence for an association between APOE ɛ4 and CAA severity. There were too few studies and participants to draw firm conclusions about the effect of non-APOE ε2/ε3/ε4 genetic polymorphisms on CAA, but there were positive associations with TGF-β1, TOMM40 and CR1 genes in four studies. Finally, in my meta-analyses of the COL4A1/COL4A2 genomic region, three intronic SNPs in COL4A2 were associated with SVD phenotypes: significantly with deep ICH, and suggestively with lacunar ischaemic stroke and WMH. Conclusions: I have shown that while existing ICH classification systems appear to have very good reliability, further research is needed to determine their performance in different settings. For large population-based prospective studies of ICH genetics, anatomical systems are likely to be more feasible, scalable and appropriate, although they have limitations and will need to be further developed. Using systematic reviews and meta-analyses, I have confirmed a dose-related association between APOE ɛ4 and histopathological CAA, but also demonstrated that, despite popular acceptance, there is insufficient data to draw firm conclusions about the association with APOE ɛ2. I found some positive associations with CAA in other genes, which merit replication in further larger studies, and showed that there is currently insufficient data about the role of APOE in hereditary CAA. Finally, I identified a novel association between a locus in a known hereditary SVD gene – COL4A2 – and sporadic SVD. This highlights a new and successful approach for selecting candidate genes and can be expanded in future studies to include other known hereditary SVD genes

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis