Caractérisation du Prostate-derived Ets transcription factor (PDEF) comme antigène tumoral candidat des cancers invasifs du sein

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Valeur des collégiens et réussite scolaire filles et garçons au collège : des univers parallèles ? : étude sur la problématique des genres et la réussite scolaire en milieu collégial /

"Recherche subventionnée par le ministère de l'Éducation, du Loisir et du Sport dans le cadre du Programme d'aide à la recherche sur l'enseignement et l'apprentissage (PAREA)"Titre de l'écran-titre (visionné le 20 sept. 2010)Également disponible en version papier.Bibliogr

Childhood Socioeconomic Status Does Not Predict Late-Life Cognitive Decline in the 1936 Lothian Birth Cohort

This study examined childhood socioeconomic status (SES) as a predictor of later life cognitive decline. Data came from 519 participants in the Lothian Birth Cohort 1936 (LBC1936) study. SES measures at 11 years of age included parental educational attainment, father’s occupational status, household characteristics and a composite measure of global childhood SES (i.e., a total of low SES childhood indicators). Cognitive abilities were assessed by the Mini-Mental State Exam at ages 69.8, 72.8 and 76.7 years. Most indicators of low childhood SES (i.e., father manual worker, less than secondary school father education, household overcrowding, exterior located toilet, and global childhood SES) did not predict cognitive decline between the ages of 69.8 and 76.7. Participants with less educated mothers showed an increase in cognitive decline (β = −0.132, p = 0.048, and CI = −0.80, −0.00). The relationship between maternal educational attainment and cognitive decline became non-significant when controlling for adult SES (i.e., participant educational attainment and occupation). Adult SES did not mediate the latter relationship. This study provides new evidence that childhood SES alone is not strongly associated with cognitive decline. New knowledge is critical to improving population health by identifying life span stages in which interventions might be effective in preventing cognitive decline

End-to-End Discriminative Deep Network for Liver Lesion Classification

Colorectal liver metastasis is one of most aggressive liver malignancies. While the definition of lesion type based on CT images determines the diagnosis and therapeutic strategy, the discrimination between cancerous and non-cancerous lesions are critical and requires highly skilled expertise, experience and time. In the present work we introduce an end-to-end deep learning approach to assist in the discrimination between liver metastases from colorectal cancer and benign cysts in abdominal CT images of the liver. Our approach incorporates the efficient feature extraction of InceptionV3 combined with residual connections and pre-trained weights from ImageNet. The architecture also includes fully connected classification layers to generate a probabilistic output of lesion type. We use an in-house clinical biobank with 230 liver lesions originating from 63 patients. With an accuracy of 0.96 and a F1-score of 0.92, the results obtained with the proposed approach surpasses state of the art methods. Our work provides the basis for incorporating machine learning tools in specialized radiology software to assist physicians in the early detection and treatment of liver lesions

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction

Colorectal liver metastases (CLM) significantly impact colon cancer patients, influencing survival based on systemic chemotherapy response. Traditional methods like tumor grading scores (e.g., tumor regression grade - TRG) for prognosis suffer from subjectivity, time constraints, and expertise demands. Current machine learning approaches often focus on radiological data, yet the relevance of histological images for survival predictions, capturing intricate tumor microenvironment characteristics, is gaining recognition. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with H&E and HPS. We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing feature maps. We use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. We exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer (ViT) in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. In our evaluation on a clinical dataset of 258 patients, our approach demonstrates superior performance with c-indexes of 0.804 (0.014) for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy in predicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRG classification task, our approach outperforms comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.Comment: 16 pages, 7 figures and 7 tables. Submitted to Medical Journal Analysis (MedIA) journa

A search for non-pulsating, chemically normal stars in the Scuti instability strip using Kepler data

We identify stars in the δ Sct instability strip that do not pulsate in p modes at the 50-μmag limit, using Kepler data. Spectral classification and abundance analyses from high-resolution spectroscopy allow us to identify chemically peculiar stars, in which the absence of pulsations is not surprising. The remaining stars are chemically normal, yet they are not δ Sct stars. Their lack of observed p modes cannot be explained through any known mechanism. However, they are mostly distributed around the edges of the δ Sct instability strip, which allows for the possibility that they actually lie outside the strip once the uncertainties are taken into account.We investigated the possibility that the non-pulsators inside the instability strip could be unresolved binary systems, having components that both lie outside the instability strip. If misinterpreted as single stars, we found that such binaries could generate temperature discrepancies of ∼300 K – larger than the spectroscopic uncertainties, and fully consistent with the observations. After these considerations, there remains one chemically normal nonpulsator that lies in the middle of the instability strip. This star is a challenge to pulsation theory. However, its existence as the only known star of its kind indicates that such stars are rare. We conclude that the δ Sct instability strip is pure, unless pulsation is shut down by diffusion or another mechanism, which could be interaction with a binary companion