Search results for "Computer Science Applications"
showing 10 items of 3993 documents
The Effects of Attachment, Temperament, and Self-Esteem on Technology Addiction: A Mediation Model among Young Adults
2022
Excessive use of technology has become a worldwide problem due to its high prevalence, fast growth rate, and undesirable consequences. However, little is known about underlying psychological mechanisms that maintain excessive use of technology. We investigated the mediating role of self-esteem, novelty seeking, and persistence on the relationship between attachment dimensions and technology addiction among young adults. Data were collected from 727 young adults (females, N = 478; 66.3 percent), aged 23.44 ± 3.02 years. Participants completed self-report measures of secure and insecure attachment dimensions, personality, and temperament characteristics (i.e., self-esteem, novelty seeking, an…
NeuPAT: an intranet database supporting translational research in neuroblastic tumors.
2013
Translational research in oncology is directed mainly towards establishing a better risk stratification and searching for appropriate therapeutic targets. This research generates a tremendous amount of complex clinical and biological data needing speedy and effective management. The authors describe the design, implementation and early experiences of a computer-aided system for the integration and management of data for neuroblastoma patients. NeuPAT facilitates clinical and translational research, minimizes the workload in consolidating the information, reduces errors and increases correlation of data through extensive coding. This design can also be applied to other tumor types.
An adapted optical flow algorithm for robust quantification of cardiac wall motion from standard cine-MR examinations
2012
International audience; This paper presents a method for local myocardial motion estimation from a conventional steady-state free precession cine-MRI sequence using a modified phase-based optical flow (OF) technique. Initially, the technique was tested on synthetic images to evaluate its robustness with regards to Rician noise and to brightness variations. The method was then applied to cardiac images acquired on 11 healthy subjects. Myocardial velocity is measured in centimeter per second in each studied pixel and visualized as colored vectors superimposed on MRI images. The estimated phase-based OF results were compared with a reference OF method and gave similar results on synthetic imag…
miR-20b and miR-451a Are Involved in Gastric Carcinogenesis through the PI3K/AKT/mTOR Signaling Pathway: Data from Gastric Cancer Patients, Cell Line…
2020
Gastric cancer (GC) is one of the most common and lethal gastrointestinal malignancies worldwide. Many studies have shown that development of GC and other malignancies is mainly driven by alterations of cellular signaling pathways. MicroRNAs (miRNAs) are small noncoding molecules that function as tumor-suppressors or oncogenes, playing an essential role in a variety of fundamental biological processes. In order to understand the functional relevance of miRNA dysregulation, studies analyzing their target genes are of major importance. Here, we chose to analyze two miRNAs, miR-20b and miR-451a, shown to be deregulated in many different malignancies, including GC. Deregulated expression of miR…
Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation
2014
This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical …
Blaming the Victim: The Effects of Extraversion and Information Disclosure on Guilt Attributions in Cyberbullying
2013
Cyberbullying victims' success in coping with bullying largely depends on schoolmates and other bystanders' social support. However, factors influencing the degree of social support have as yet not been investigated. In this article, the concept of victim blaming is applied to cyberbullying incidents. It is assumed that a cyberbullying victim receives less social support when the victim's behavior is perceived as very overt. It is further assumed that this effect's underlying process is the partial attribution of responsibility for the incident to the victim and not to the bully. The hypotheses are tested with a 2×2 online experiment. In this experiment, varying online self-presentations of…
HIF-Overexpression and Pro-Inflammatory Priming in Human Mesenchymal Stromal Cells Improves the Healing Properties of Extracellular Vesicles in Exper…
2021
Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) have therapeutic potential in the treatment of several immune disorders, including ulcerative colitis, owing to their regenerative and immunosuppressive properties. We recently showed that MSCs engineered to overexpress hypoxia-inducible factor 1-alpha and telomerase (MSC-T-HIF) and conditioned with pro-inflammatory stimuli release EVs (EVMSC-T-HIFC) with potent immunomodulatory activity. We tested the efficacy of EVMSC-T-HIFC to repolarize M1 macrophages (Mφ1) to M2-like macrophages (Mφ2-like) by analyzing surface markers and cytokines and performing functional assays in co-culture, including efferocytosis and T-cel…
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
2018
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how f…
A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer
2013
International audience; BACKGROUND: With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data to support the exploration of trajectories of care. METHODS: A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. Th…
Discrimination of retinal images containing bright lesions using sparse coded features and SVM
2015
Diabetic Retinopathy (DR) is a chronic progressive disease of the retinal microvasculature which is among the major causes of vision loss in the world. The diagnosis of DR is based on the detection of retinal lesions such as microaneurysms, exudates and drusen in retinal images acquired by a fundus camera. However, bright lesions such as exudates and drusen share similar appearances while being signs of different diseases. Therefore, discriminating between different types of lesions is of interest for improving screening performances. In this paper, we propose to use sparse coding techniques for retinal images classification. In particular, we are interested in discriminating between retina…