Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease.

Background: Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Methods: Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the `GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, `leave-one-out' cross-validation method. Results: 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P <= 0.01, false discovery rate <= 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100\% sensitivity and specificity (95\% confidence interval: 67.9-99.2\%). Supervised prediction using `leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212). Conclusions: Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival

Analysis of post-operative changes in serum protein expression profiles from colorectal cancer patients by MALDI-TOF mass spectrometry: a pilot methodological study.

Background: Mass spectrometry-based protein expression profiling of blood sera can be used to discriminate colorectal cancer (CRC) patients from unaffected individuals. In a pilot methodological study, we have evaluated the changes in protein expression profiles of sera from CRC patients that occur following surgery to establish the potential of this approach for monitoring post-surgical response and possible early prediction of disease recurrence. Methods: In this initial pilot study, serum specimens from 11 cancer patients taken immediately prior to surgery and at approximately 6 weeks following surgery were analysed alongside 10 normal control sera by matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Using a two-sided t-test the top 20 ranked protein peaks that discriminate normal from pre-operative sera were identified. These were used to classify postoperative sera by hierarchical clustering analysis (Spearman's Rank correlation) and, as an independent `test' dataset, by k-nearest neighbour and weighted voting supervised learning algorithms. Results: Hierarchical cluster analysis classified post-operative sera from all six early Dukes' stage (A and B) patients as normal. The remaining five post-operative sera from more advanced Dukes' stages (C1 and C2) were classified as cancer. Analysis by supervised learning algorithms similarly grouped all advanced Dukes' stages as cancer, with four of the six post-operative sera from early Dukes' stages being classified as normal (P = 0.045; Fisher's exact test). Conclusions: The results of this pilot methodological study illustrate the proof-of-concept of using protein expression profiling of post-surgical blood sera from individual patients to monitor disease course. Further validation on a larger patient cohort and using an independent post-operative sera dataset would be required to evaluate the potential clinical relevance of this approach. Prospective data, including follow-up on patient survival, could in the future, then be evaluated to inform decisions on individualised treatment modalities

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201

An advanced Bayesian model for the visual tracking of multiple interacting objects

Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend on the inference of potential events of object occlusion. The proposed tracking model can also handle false and missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results have been obtained using a publicly available database, proving the efficiency of the proposed approach

A robust tracking system for low frame rate video

Tracking in low frame rate (LFR) videos is one of the most important problems in the tracking literature. Most existing approaches treat LFR video tracking as an abrupt motion tracking problem. However, in LFR video tracking applications, LFR not only causes abrupt motions, but also large appearance changes of objects because the objects’ poses and the illumination may undergo large changes from one frame to the next. This adds extra difficulties to LFR video tracking. In this paper, we propose a robust and general tracking system for LFR videos. The tracking system consists of four major parts: dominant color-spatial based object representation, bin-ratio based similarity measure, annealed particle swarm optimization (PSO) based searching, and an integral image based parameter calculation. The first two parts are combined to provide a good solution to the appearance changes, and the abrupt motion is effectively captured by the annealed PSO based searching. Moreover, an integral image of model parameters is constructed, which provides a look-up table for parameters calculation. This greatly reduces the computational load. Experimental results demonstrate that the proposed tracking system can effectively tackle the difficulties caused by LFR

Blood pressure and cholesterol level checks as dynamic interrelated screening examinations

This study analysed the determinants of screening uptake for blood pressure and cholesterol level checks. Furthermore, it investigated the presence of possible spillover effects from one type of cardiovascular screening to another type of cardiovascular screening. A dynamic random effects bivariate panel probit model with initial conditions (Wooldridge-type estimator) was adopted for the estimation. The outcome variables were the participation in blood pressure and cholesterol level checks by individuals in a given year. The balanced panel sample of 21,138 observations was constructed from 1,626 individuals from the British Household Panel Survey (BHPS) between 1996 and 2008. The analysis showed the significance of past screening behaviour for both cardiovascular screening examinations. For both cardiovascular screening examinations state dependence exist. The study also shows a significant spillover effect of the cholesterol level check on the blood pressure check and vice versa. Also a poorer health status led to a higher uptake for both types of screening examinations. Changes in recommendations have to consider the fact that taking part in one type of cardiovascular screening examination can influence the decision to take part in the other type of cardiovascular screening examination

Endophilia or Exophobia:Beyond Discrimination

The discrimination literature treats outcomes as relative. But does a differential arise because agents discriminate against others - exophobia - or because they favour their own kind - endophilia? Using a field experiment that assigned graders randomly to students' examinations that did/did not contain names, we find favouritism but no discrimination by nationality nor by gender. We are able to identify these preferences under a wide range of behavioural scenarios regarding the graders. That endophilia dominates exophobia alters how we should measure discriminatory wage differentials and should inform the formulation of anti-discrimination policy

EAES and SAGES 2018 consensus conference on acute diverticulitis management:evidence-based recommendations for clinical practice

Background Acute diverticulitis (AD) presents a unique diagnostic and therapeutic challenge for general surgeons. This collaborative project between EAES and SAGES aimed to summarize recent evidence and draw statements of recommendation to guide our members on comprehensive AD management. Methods Systematic reviews of the literature were conducted across six AD topics by an international steering group including experts from both societies. Topics encompassed the epidemiology, diagnosis, management of non-complicated and complicated AD as well as emergency and elective operative AD management. Consensus statements and recommendations were generated, and the quality of the evidence and recommendation strength rated with the GRADE system. Modified Delphi methodology was used to reach consensus among experts prior to surveying the EAES and SAGES membership on the recommendations and likelihood to impact their practice. Results were presented at both EAES and SAGES annual meetings with live re-voting carried out for recommendations with < 70% agreement. Results A total of 51 consensus statements and 41 recommendations across all six topics were agreed upon by the experts and submitted for members’ online voting. Based on 1004 complete surveys and over 300 live votes at the SAGES and EAES Diverticulitis Consensus Conference (DCC), consensus was achieved for 97.6% (40/41) of recommendations with 92% (38/41) agreement on the likelihood that these recommendations would change practice if not already applied. Areas of persistent disagreement included the selective use of imaging to guide AD diagnosis, recommendations against antibiotics in non-complicated AD, and routine colonic evaluation after resolution of non-complicated diverticulitis. Conclusion This joint EAES and SAGES consensus conference updates clinicians on the current evidence and provides a set of recommendations that can guide clinical AD management practice