Christos Maramis

Efficient Quantitative Information Extraction from PCR-RFLP Gel Electrophoresis Images

For the purpose of PCR-RFLP analysis, as in the case of human papillomavirus (HPV) typing, quantitative information needs to be extracted from images resulting from one-dimensional gel electrophoresis by associating the image intensity with the concentration of biological material at the corresponding position on a gel matrix. However, the background intensity of the image stands in the way of quantifying this association. We propose a novel, efficient methodology for modeling the image background with a polynomial function and prove that this can benefit the extraction of accurate information from the lane intensity profile when modeled by a superposition of properly shaped parametric functions.

Restrictions in the T-cell repertoire of chronic lymphocytic leukemia: high-throughput immunoprofiling supports selection by shared antigenic elements

Immunoglobulin (IG) gene repertoire restrictions strongly support antigen selection in the pathogenesis of chronic lymphocytic leukemia (CLL). Given the emerging multifarious interactions between CLL and bystander T cells, we sought to determine whether antigen(s) are also selecting T cells in CLL. We performed a large-scale, next-generation sequencing (NGS) study of the T-cell repertoire, focusing on major stereotyped subsets representing CLL subgroups with undisputed antigenic drive, but also included patients carrying non-subset IG rearrangements to seek for T-cell immunogenetic signatures ubiquitous in CLL. Considering the inherent limitations of NGS, we deployed bioinformatics algorithms for qualitative curation of T-cell receptor rearrangements, and included multiple types of controls. Overall, we document the clonal architecture of the T-cell repertoire in CLL. These T-cell clones persist and further expand overtime, and can be shared by different patients, most especially patients belonging to the same stereotyped subset. Notably, these shared clonotypes appear to be disease-specific, as they are found in neither public databases nor healthy controls. Altogether, these findings indicate that antigen drive likely underlies T-cell expansions in CLL and may be acting in a CLL subset-specific context. Whether these are the same antigens interacting with the malignant clone or tumor-derived antigens remains to be elucidated.

Preventing obesity and eating disorders through behavioural modifications: The SPLENDID vision

Recent intensive research in the fields of obesity and eating disorders has proved most traditional interventions inadequate: The obesity-targeting interventions have either failed or are strongly social context dependent, while the interventions for eating disorders have poor results and high levels of relapse. On the contrary, recent randomized control trials have illustrated that supervised training of patients to eat and move in a non-pathological way is effective in the prevention of both obesity and eating disorders. Applying the same kind of methodologies to the general population in real life conditions for prevention purposes comes as the logical next step. SPLENDID is a recently initiated EU-funded collaborative project that intends to develop a personalised guidance system for helping and training children and young adults to improve their eating and activity behaviour. By combining expertise in behavioural patterns with current advancements in intelligent systems and sensor technologies, SPLENDID is going to detect subjects at risk for developing obesity or eating disorders and offer them enhanced monitoring and guidance to prevent further disease progression. Both behavioural data collection and system evaluation are going to be performed via pilot studies supported by expert health professionals.

A Computerized Methodology for Improved Virus Typing by PCR-RFLP Gel Electrophoresis

The analysis of digitized images from polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) gel electrophoresis examinations is a popular method for virus typing, i.e., for identifying the virus type(s) that have infected an investigated biological sample. However, being mostly manual, the conventional virus typing protocol remains laborious, time consuming, and error prone. In order to overcome these shortcomings, we propose a computerized methodology for improving virus typing via PCR-RFLP gel electrophoresis. A novel realistic observation model of the viral DNA motion on the gel matrix is employed to assist in exploiting additional virus-related information in comparison to the conventional approaches. The extracted rich information is fed to a novel typing algorithm, resulting in faster and more accurate decisions. The proposed methodology is evaluated for the case of the human papillomavirus typing on a dataset of 80 real and 1500 simulated samples, producing very satisfactory results.

Analysis of PCR-RFLP gel electrophoresis images for accurate and automated HPV typing

The identification of the types of the human papilomavirus (HPV) that have infected a woman provides valuable information as regards to her risk for developing cervical cancer. HPV typing is often performed by means of manually analyzing PCR-RFLP gel electrophoresis images. However, the typing procedure that is currently employed suffers from unsatisfactory accuracy and high time consumption. In order to treat these problems we propose a novel approach to HPV typing that automates the analysis of the electrophoretic images and concurrently improves the accuracy of the typing decision. The proposed methodology contributes both to the extraction of information from the images through a novel modeling approach and also to the process of making a typing decision based on the above information by the introduction of an original HPV typing algorithm. The efficiency of our approach is demonstrated with the help of a complex worked example that involves multiple HPV infections.

Improved Modeling of Lane Intensity Profiles on Gel Electrophoresis Images

The quantitative information extraction from PCR-RFLP gel electrophoresis images requires the efficient modeling of the lane intensity profiles. To improve the acquired modeling accuracy, we introduce two novel ideas that can be incorporated in the modeling process. The first one proposes the use of the simplified integrated Weibull function as the basis function of the employed superposition model and the second proposes switching the domain of the intensity profile tobe-modeled to the unexploited fragment length domain.

Real-time Bite Detection from Smartwatch Orientation Sensor Data

The introduction of smartwatches over the last few years has made widely available a new type of wearable, everyday-usage device that is equipped with dozens of sensors. The sensors embedded in the smartwatch constitute a valuable source of data about the bodily functions of the smartwatch user; a source that has already been exploited for inferring information concerning the human behavior. One possible application of the aforementioned information inference is the prediction of eating-related events, such as bite instances. Accurate bite instance prediction from smartwatch sensors could serve as a trigger for appropriate user feedback in the context of just-in-time adaptive interventions for eating behavior management. In this paper, we present a novel method for real-time detection of bite instances from 3-axis orientation data acquired by a smartwatch. The evaluation of proposed method has been performed on eight annotated orientation timeseries, generated by eight individuals who wore a commercial smartwatch on their active hand while eating a bowl of milk with cereals. Both the classification accuracy of the method and its ability to make real-time decisions were evaluated, yielding very promising results.

Objective measures of eating behaviour in a Swedish high school

ABSTRACT Studying eating behaviours is important in the fields of eating disorders and obesity. However, the current methodologies of quantifying eating behaviour in a real-life setting are lacking, either in reliability (e.g. self-reports) or in scalability. In this descriptive study, we deployed previously evaluated laboratory-based methodologies in a Swedish high school, using the Mandometer®, together with video cameras and a dedicated mobile app in order to record eating behaviours in a sample of 41 students, 16–17 years old. Without disturbing the normal school life, we achieved a 97% data-retention rate, using methods fully accepted by the target population. The overall eating style of the students was similar across genders, with male students eating more than females, during lunches of similar lengths. While both groups took similar number of bites, males took larger bites across the meal. Interestingly, the recorded school lunches were as long as lunches recorded in a laboratory setting, which is characterised by the absence of social interactions and direct access to additional food. In conclusion, a larger scale use of our methods is feasible, but more hypotheses-based studies are needed to fully describe and evaluate the interactions between the school environment and the recorded eating behaviours.

Applying semantic technologies in cervical cancer research

Abstract In this paper we present a research system that follows a semantic approach to facilitate medical association studies in the area of cervical cancer. Our system, named ASSIST and developed as an EU research project, assists in cervical cancer research by unifying multiple patient record repositories, physically located in different medical centers or hospitals. Semantic modeling of medical data and rules for inferring domain-specific information allow the system to (i) homogenize the information contained in the isolated repositories by translating it into the terms of a unified semantic representation, (ii) extract diagnostic information not explicitly stored in the individual repositories, and (iii) automate the process of evaluating medical hypotheses by performing case–control association studies, which is the ultimate goal of the system.

Objective and Subjective Meal Registration via a Smartphone Application

SPLENDID is a research programme that develops a novel preventive intervention for young people at risk for obesity and eating disorders. The SPLENDID app, a novel smartphone application that mediates the monitoring and modification of the participants’ eating and activity behaviors, resides in the intervention’s core. The app receives and manages eating and physical activity related signals from three communicating sensors as well as subjective user input. In this paper, we present two discrete meal registration mechanisms – subjective and objective – that have been implemented and incorporated in the SPLENDID app, along with the relevant user feedback. In objective meal registration, the app records meal information with the help of a portable food weight scale, while an electronic meal report is employed for the subjective registration. Certain components of the proposed registration mechanisms and the relevant feedback have been evaluated with respect to usability on forty young adolescents, yielding promising results.