Examining the importance of local and global patterns for familiarity detection in soccer action sequences

Pattern recognition is a defining characteristic of expertise across multiple domains. Given the dynamic interactions at local and global levels, team sports can provide a vehicle for investigating skilled pattern recognition. The aims of this study were to investigate whether global patterns could be recognised on the basis of localised relational information and if relations between certain display features were more important than others for successful pattern recognition. Elite (n = 20), skilled (n = 34), and less-skilled (n = 37) soccer players completed three recognition paradigms of stimuli presented in point-light-stimuli format across three counterbalanced conditions: ‘whole-part’; ‘part-whole’; and ‘whole-whole’. ‘Whole’ clips represented a 11v11 soccer match and ‘part’ clips presented the same passages of play with only two centre forwards or two peripheral players. Elite players recognised significantly more accurately than the skilled and less-skilled groups. Participants were significantly more accurate in the ‘whole-whole’ condition compared to others, and recognised stimuli featuring the two central attacking players significantly more accurately than those featuring peripheral players. Findings provide evidence that elite players can encode localised relations and then extrapolate this information to recognise more global macro patterns

Peregrine: A Pattern-Aware Graph Mining System

Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process. However, the state-of-the-art graph mining systems remain largely oblivious to the shape (or pattern) of the subgraphs that they mine. This causes them to: (a) explore unnecessary subgraphs; (b) perform expensive computations on the explored subgraphs; and, (c) hold intermediate partial subgraphs in memory; all of which affect their overall performance. Furthermore, their programming models are often tied to their underlying exploration strategies, which makes it difficult for domain users to express complex mining tasks. In this paper, we develop Peregrine, a pattern-aware graph mining system that directly explores the subgraphs of interest while avoiding exploration of unnecessary subgraphs, and simultaneously bypassing expensive computations throughout the mining process. We design a pattern-based programming model that treats "graph patterns" as first class constructs and enables Peregrine to extract the semantics of patterns, which it uses to guide its exploration. Our evaluation shows that Peregrine outperforms state-of-the-art distributed and single machine graph mining systems, and scales to complex mining tasks on larger graphs, while retaining simplicity and expressivity with its "pattern-first" programming approach.Comment: This is the full version of the paper appearing in the European Conference on Computer Systems (EuroSys), 202

Charge transport mechanisms in monovalent doped mixed valent manganites

Abstract In this communication, we report the results of the studies on structural and transport properties of monovalent Na + doped La 1-x Na x MnO 3 (LNMO; x = 0.00, 0.05, 0.10, 0.15, 0.20, 0.25 and 0.30) manganites synthesized by conventional ceramic method. X-ray diffraction (XRD) and Rietveld refinements reveal the single phasic nature of LNMO manganites without any detectable impurity within the measurement range. Temperature dependent resistivity, under different applied magnetic fields, has been performed on LNMO samples. Samples understudy exhibit metal to insulator (semiconductor) transition at temperature T P which is strongly influenced by the substitution of Na + at La 3+ site. -T plots also exhibit resistivity upturn behavior at low temperature well below 40K under all the applied fields. Variation in T P and resistivity has been discussed in the context of the competition between the transport favoring tolerance factor and zener double exchange (ZDE) mechanism and transport degrading Jahn-Teller (JT) and size variance effects. In order to understand the mechanisms responsible for the charge transport in metallic and semiconducting regions and to explore the possible electronic processes responsible for the observed low temperature resistivity minima in all the presently studied LNMO manganites, various models have been employed. It has been found that VRH mechanism gets successfully fitted to the resistivity data in the semiconducting region while ZDE polynomial law is responsible for the charge conduction in metallic region for all the presently studied LNMO samples. A strong dependence of activation energy on the Na + -content as well as applied magnetic field has been discussed in the context of variation and interrelations between the structural parameters. Charge conduction in metallic region has been discussed in the light of electron-phonon interactions which is influenced by the Na + -content and applied magnetic field. Electrostatic blockade model has been employed to understand the low temperature resistivity minima behavior. Blocking energy for the charge carriers shows a dependence on the magnetic energy provided to the charge carriers. Present study can be useful to understand and to control the charge conduction in the manganites and hence to design the manganite based thin film devices for various spintronic applications

Integration of copy number and transcriptomics provides risk stratification in prostate cancer : a discovery and validation cohort study

Study data are deposited in NCBI GEO (unique identifier number GSE70770).Background : Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. Methods : In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. Findings : We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer ( MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. Interpretation : For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.Publisher PDFPeer reviewe

Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.

BACKGROUND: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. METHODS: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. FINDINGS: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. INTERPRETATION: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.Cambridge work was funded by a CRUK programme grant awarded to DEN; Swedish work and tissue collections were funded by grants from the Linne Centre for Breast and Prostate Cancer (CRISP, grant 70867901), Karolinska Institutet, the Swedish Research Council (K2010-70X-20430-04-3), and the Swedish Cancer Society (11-0287).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ebiom.2015.07.01

Enhanced detection of circulating tumor DNA by fragment size analysis.

Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA.We would like to acknowledge the support of The University of Cambridge, Cancer Research UK and the EPSRC (CRUK grant numbers A11906 (NR), A20240 (NR), A22905 (JDB), A15601 (JDB), A25177 (CRUK Cancer Centre Cambridge), A17242 (KMB), A16465 (CRUK-EPSRC Imaging Centre in Cambridge and Manchester)). The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 337905. The research was supported by the National Institute for Health Research Cambridge, National Cancer Research Network, Cambridge Experimental Cancer Medicine Centre and Hutchison Whampoa Limited. This research is also supported by Target Ovarian Cancer and the Medical Research Council through their Joint Clinical Research Training Fellowship for Dr Moore. The CALIBRATE study was supported by funding from AstraZeneca

Predictive factors of antiproliferative activity of octreotide LAR as first-line therapy for advanced neuroendocrine tumours

background: The antiproliferative activity of octreotide LAR in neuroendocrine tumours (NETs) has been demonstrated by small retrospective studies and confirmed by a prospective phase III trial (PROMID). However, there are limited data about the duration and predictors of response. The aim of our retrospective study was to determine the time to radiological progression (TTRP) of disease and the factors that were associated with better response. methods: A total of 254 treatment naïve patients with advanced NETs and positive somatostatin receptor scintigraphy were included. Mean follow-up period was 42 months. results: The location of primary was in the small bowel in 204, pancreas in 22, lungs in 14, rectum in 7 and unknown in 7 patients. Most tumours were well-differentiated, G1 (58%) and G2 (23%). The majority of patients commenced octreotide LAR due to functional symptoms (57%), radiological progression (10%) or in the presence of asymptomatic and stable disease on the basis of data from the PROMID trial (18.5%). Partial response occurred in 5%. For all patients, the median TTRP was 37 months (95% confidence interval, CI: 32–52 months). There was a statistically significant shorter TTRP in patients with pancreatic tumours, liver metastases and intermediate grade tumours. Extremely raised (>10 times the upper limit of normal) baseline chromogranin A levels were associated with an unfavourable outcome. In contrast, male sex, carcinoid heart disease and initiation of treatment in the presence of stable disease were predictive of a better response. Age, extra-hepatic metastases, presence of mesenteric desmoplasia, previous resection and functional status of the primary tumour did not affect response. conclusions: The duration of the antiproliferative effect of octreotide LAR seems to be longer than previously reported. This study has identified several predictors of response in a large cohort of patients with NETs on somatostatin analogue therapy