Expression and role of 17BETA-hydroxysteroid dehydrogenase type 1, 5 and 7 in epithelial ovarian cancer

Le cancer de l’ovaire est l’une des cinq causes les plus fréquentes de décès par cancer chez les femmes dans le monde développé. Environ 90% des cancers de l’ovaire proviennent de l’épithélium que l’on nomme cancer de l’ovaire épithélial (EOC). Le EOC est un cancer hormono-dépendant et les stéroïdes sexuels jouent un rôle crucial en favoriant la prolifération et de la survie des cellules. Les 17β-hydroxystéroïdes déshydrogénases (17β-HSDs) jouent un rôle important pour le contrôle de la concentration intracellulaire de tous les stéroïdes sexuels actifs. Le mécanisme qui reculent le fonctionnent et l’expression des 17β-HSDs dans le EOC sont très peu compris. L’inhibition de certains 17β-HSDs pourrait être un traitement de l’EOC et ette approche thérapeutique doit être étudiée. Les résultats de notre étude ont démontré que les 17β- HSD types 1, 5 et 7 sont tous exprimés dans les cellules OOC-3, mais que la type 1 est la plus abondante. L’expression des 17β-HSD types 1 et 7 dans les tumeurs ovariennes épithéliales que dans les ovaires normaux (type 1, 2.2 fois; type 7, 1.9 fois). Mais l’expression de la 17β-HSD 5 est significativement plus faible dans les tumeurs, suite au développement de l’EOC (-5.217 fois). De plus, la prolifération cellulaire a diminué à la suite du knockdown la 17β-HSD type 1 ou type 7 par des siRNAs spécifiques dans les cellules OVCAR-3, mais, le knockdown de la type 5 a un effet contraire. Nous suggérons que la 17β-HSD 5 peut être impliquée dans une signalisation d’hormones stéroïdiennes pour le développement du cancer de l’ovaire épithélial. Les 17β-HSD 1 et 7 pourraient être des biomarqueurs importants pour l’EOC diagnostiqué tôt et ils peuvent également être de nouvelles cibles pour le traitement de l’EOC.Ovarian cancer is one of the top five commonest causes of female cancer death in the developed world. About 90% of ovarian cancer have epithelial origins. Epithelial ovarian cancer (EOC) is a hormone-dependent cancer, in which the sex steroids play a crucial role in maintaining the cell proliferation and survival. The 17β-hydroxysteroid dehydrogenases (17β-HSDs) are important in the control of intracellular concentration of all active sex steroids. The function and expression of 17β-HSDs in EOC is not fully understood. Whether or not 17β-HSDs could be a therapeutic approach for the EOC treatment needs to be studied. Our results showed that 17β-HSD types 1, 5 and 7 are all expressed in EOC cells OVCAR-3 and type 1 is the highest one. The expression of 17β-HSD types 1 and 7 is higher in epithelial ovarian tumor tissues than in normal ovaries (type1, 2.2-fold; type7, 1.9-fold), but the expression of 17β-HSD type 5 is significantly lower in the tumor, following the EOC development (-5.2-fold). We found that cell proliferation was decreased after 17β-HSD type 1 or 7 knockdown by specific siRNAs in OVCAR-3 cells. While knocking down type 5 has the opposite effect. We suggest that 17β- HSD type 5 may be involved in steroid hormone signaling in EOC development. Moreover, 17β-HSD types 1 and 7 could be important biomarkers for early diagnosed EOC and novel targets for EOC treatment

Geometry of quantum evolution in a nonequilibrium environment

We theoretically study the geometric effect of quantum dynamical evolution in the presence of a nonequilibrium noisy environment. We derive the expression of the time dependent geometric phase in terms of the dynamical evolution and the overlap between the time evolved state and initial state. It is shown that the frequency shift induced by the environmental nonequilibrium feature plays a crucial role in the geometric phase and evolution path of the quantum dynamics. The nonequilibrium feature of the environment makes the length of evolution path becomes longer and reduces the dynamical decoherence and non-Markovian behavior in the quantum dynamics

3D-2D Spatiotemporal Registration for human motion analysis

Ph.DDOCTOR OF PHILOSOPH

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

The ability to train deep neural networks under label noise is appealing, as imperfectly annotated data are relatively cheaper to obtain. State-of-the-art approaches are based on semi-supervised learning(SSL), which selects small loss examples as clean and then applies SSL techniques for boosted performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. In this work, we propose a novel noisy label learning framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high-confidence selection technique that selects those examples having high confidence and matched prediction with its given labels. Combining with the small-loss selection, our method is able to achieve a precision of 99.27 and a recall of 98.22 in detecting clean samples on the CIFAR-10N dataset. Based on such a large set of clean data, ProMix improves the best baseline method by +2.67% on CIFAR-10N and +1.61% on CIFAR-100N datasets. The code and data are available at https://github.com/Justherozen/ProMixComment: Winner of the 1st Learning and Mining with Noisy Labels Challenge in IJCAI-ECAI 2022 (an informal technical report

Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection

Out-of-distribution (OOD) detection is essential when deploying neural networks in the real world. One main challenge is that neural networks often make overconfident predictions on OOD data. In this study, we propose an effective post-hoc OOD detection method based on a new feature masking strategy and a novel logit smoothing strategy. Feature masking determines the important features at the penultimate layer for each in-distribution (ID) class based on the weights of the ID class in the classifier head and masks the rest features. Logit smoothing computes the cosine similarity between the feature vector of the test sample and the prototype of the predicted ID class at the penultimate layer and uses the similarity as an adaptive temperature factor on the logit to alleviate the network's overconfidence prediction for OOD data. With these strategies, we can reduce feature activation of OOD data and enlarge the gap in OOD score between ID and OOD data. Extensive experiments on multiple standard OOD detection benchmarks demonstrate the effectiveness of our method and its compatibility with existing methods, with new state-of-the-art performance achieved from our method. The source code will be released publicly.Comment: 10 pages, 7 figure

FR: Folded Rationalization with a Unified Encoder

Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by a predictor that makes predictions based on the selected pieces. However, such a two-phase model may incur the degeneration problem where the predictor overfits to the noise generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces. To tackle this challenge, we propose Folded Rationalization (FR) that folds the two phases of the rationale model into one from the perspective of text semantic extraction. The key idea of FR is to employ a unified encoder between the generator and predictor, based on which FR can facilitate a better predictor by access to valuable information blocked by the generator in the traditional two-phase model and thus bring a better generator. Empirically, we show that FR improves the F1 score by up to 10.3% as compared to state-of-the-art methods.Comment: Accepted at NeurIPS 202