Simulating drug responses in laboratory test time series with deep generative modeling

Drug effects can be unpredictable and vary widely among patients with environmental, genetic, and clinical factors. Randomized control trials (RCTs) are not sufficient to identify adverse drug reactions (ADRs), and the electronic health record (EHR) along with medical claims have become an important resource for pharmacovigilance. Among all the data collected in hospitals, laboratory tests represent the most documented and reliable data type in the EHR. Laboratory tests are at the core of the clinical decision process and are used for diagnosis, monitoring, screening, and research by physicians. They can be linked to drug effects either directly, with therapeutic drug monitoring (TDM), or indirectly using drug laboratory effects (DLEs) that affect surrogate tests. Unfortunately, very few automated methods use laboratory tests to inform clinical decision making and predict drug effects, partly due to the complexity of these time series that are irregularly sampled, highly dependent on other clinical covariates, and non-stationary. Deep learning, the branch of machine learning that relies on high-capacity artificial neural networks, has known a renewed popularity this past decade and has transformed fields such as computer vision and natural language processing. Deep learning holds the promise of better performances compared to established machine learning models, although with the necessity for larger training datasets due to their higher degrees of freedom. These models are more flexible with multi-modal inputs and can make sense of large amounts of features without extensive engineering. Both qualities make deep learning models ideal candidate for complex, multi-modal, noisy healthcare datasets. With the development of novel deep learning methods such as generative adversarial networks (GANs), there is an unprecedented opportunity to learn how to augment existing clinical dataset with realistic synthetic data and increase predictive performances. Moreover, GANs have the potential to simulate effects of individual covariates such as drug exposures by leveraging the properties of implicit generative models. In this dissertation, I present a body of work that aims at paving the way for next generation laboratory test-based clinical decision support systems powered by deep learning. To this end, I organized my experiments around three building blocks: (1) the evaluation of various deep learning architectures with laboratory test time series and their covariates with a forecasting task; (2) the development of implicit generative models of laboratory test time series using the Wasserstein GAN framework; (3) the inference properties of these models for the simulation of drug effects in laboratory test time series, and their application for data augmentation. Each component has its own evaluation: The forecasting task enabled me to explore the properties and performances of different learning architectures; the Wasserstein GAN models are evaluated with both intrinsic metrics and extrinsic tasks, and I always set baselines to avoid providing results in a "neural-network only" referential. Applied machine learning, and more so with deep learning, is an empirical science. While the datasets used in this dissertation are not publicly available due to patient privacy regulation, I described pre-processing steps, hyper-parameters selection and training processes with reproducibility and transparency in mind. In the specific context of these studies involving laboratory test time series and their clinical covariates, I found that for supervised tasks, machine learning holds up well against deep learning methods. Complex recurrent architectures like long short-term memory (LSTM) do not perform well on these short time series, while convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) provide the best performances, at the cost of extensive hyper-parameter tuning. Generative adversarial networks, enabled by deep learning models, were able to generate high-fidelity laboratory test time series, and the quality of the generated samples was increased with conditional models using drug exposures as auxiliary information. Interestingly, forecasting models trained on synthetic data exclusively still retain good performances, confirming the potential of GANs in privacy-oriented applications. Finally, conditional GANs demonstrated an ability to interpolate samples from drug exposure combinations not seen during training, opening the way for laboratory test simulation with larger auxiliary information spaces. In specific cases, augmenting real training sets with synthetic data improved performances in the forecasting tasks, and could be extended to other applications where rare cases present a high prediction error

Visual localisation of electricity pylons for power line inspection

Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastructure components. In this research, we studied the visual localisation of a pylon. The pylon is the most prominent component of the power infrastructure and can provide a context for the inspection of the other components. Point-based descriptors tend to perform poorly on texture less objects such as pylons, therefore we explored the localisation using convolutional neural networks and geometric constraints. The crossings of the pylon, or vertices, are salient points on the pylon. These vertices aid with recognition and pose estimation of the pylon. We were successfully able to use a convolutional neural network for the detection of the vertices. A model-based technique, geometric hashing, was used to establish the correspondence between the stored pylon model and the scene object. We showed the effectiveness of the method as a voting technique to determine the pose estimation from a single image. In a localisation framework, the method serves as the initialization of the tracking process. We were able to incorporate an extended Kalman filter for subsequent incremental tracking of the camera relative to the pylon. Also, we demonstrated an alternative tracking using heatmap details from the vertex detection. We successfully demonstrated the proposed algorithms and evaluated their effectiveness using a model pylon we built in the laboratory. Furthermore, we revalidated the results on a real-world outdoor electricity pylon. Our experiments illustrate that model-based techniques can be deployed as part of the navigation aspect of a robot

Synthetic lethality of flavonoids towards homologous recombination deficient cells through PARP inhibition

2019 Fall.Includes bibliographical references.Flavonoids can be isolated from many different sources such as plants, fruits, and beverages and they have long been associated with various health benefits. Both in vitro and in vivo studies have shown potential anti-inflammatory, anti-allergic, anti-viral, and antioxidant activities associated with these compounds. Previously published research has shown that the anti-cancer effects of flavonoids on BRCA2 deficient cells can be attributed to a PARP inhibitory mechanism. Therefore, thirteen structurally similar flavonoids were screened and identified as PARP inhibitory flavonoids. Seven different cell lines: Chinese hamster lung V79 cells, its BRCA2 deficient derivative V-C8 cells, gene corrected V-C8 cells, Chinese hamster ovary (CHO) wild type cells, rad51D deficient CHO cells (51D1), Human colorectal adenocarcinoma cells (DLD-1), and their BRCA2 knockout cells (DLD1 BRCA2-/- ) were used to assess the degree of synthetic lethality due to PARP inhibition. Colony formation and doubling time assays identified selective toxicity in DNA repair deficient cells for the flavonoids Kaempferol, Myricetin, Quercetin, Theaflavin and Epigallocatechin gallate. A Sister Chromatid Exchange (SCE) assay indicated Kaempferol, Myricetin, Quercetin Theaflavin and Epigallocatechin gallate exhibited a marked increase in SCE rate, which is indicative of PARP inhibition. These results were confirmed via an in vitro PARP inhibition assay. This study identified Kaempferol as a natural PARP inhibitor leading to potential lethality to BRCA2 cancers. All flavonoids identified as effective PARP inhibitors had similar structural components: hydroxyl groups on the 5 and 7 position of the A-ring, another hydroxyl on the B ring in the 4 position, and a C-2,3 double bond (a 4-ketone function)

Molecular insights into amyloid regulation by membrane cholesterol and sphingolipids: common mechanisms in neurodegenerative diseases

Alzheimer, Parkinson and other neurodegenerative diseases involve a series of brain proteins, referred to as ‘amyloidogenic proteins’, with exceptional conformational plasticity and a high propensity for self-aggregation. Although the mechanisms by which amyloidogenic proteins kill neural cells are not fully understood, a common feature is the concentration of unstructured amyloidogenic monomers on bidimensional membrane lattices. Membrane-bound monomers undergo a series of lipid-dependent conformational changes, leading to the formation of oligomers of varying toxicity rich in β-sheet structures (annular pores, amyloid fibrils) or in α-helix structures (transmembrane channels). Condensed membrane nano- or microdomains formed by sphingolipids and cholesterol are privileged sites for the binding and oligomerisation of amyloidogenic proteins. By controlling the balance between unstructured monomers and α or β conformers (the chaperone effect), sphingolipids can either inhibit or stimulate the oligomerisation of amyloidogenic proteins. Cholesterol has a dual role: regulation of protein–sphingolipid interactions through a fine tuning of sphingolipid conformation (indirect effect), and facilitation of pore (or channel) formation through direct binding to amyloidogenic proteins. Deciphering this complex network of molecular interactions in the context of age- and disease-related evolution of brain lipid expression will help understanding of how amyloidogenic proteins induce neural toxicity and will stimulate the development of innovative therapies for neurodegenerative diseases

La Reprise en actes (sous la dir. de Marie-Dominique Popelard)

Par bien des aspects, ce recueil d’articles témoigne d’une extrême actualité du réemploi. Entendu ici comme reprise, ce mode de création semble pourtant échapper à une désignation figée et se dote au fil des contributions, de divers substituts : répétition, recyclage, actualisation, inversion, redite, adaptation, rétrospective, citation…Cette variété reflète la grande diversité des objets d’étude et des approches méthodologiques réunies au sein de cet ouvrage à l’interdisciplinarité affirmée ..

L’étude du rôle de l’interleukine 6 dans le métabolisme lipidique de la cardiomyopathie diabétique

La cardiomyopathie diabétique est associée à une accumulation des lipides au niveau du muscle cardiaque. Afin de comprendre l'effet de la lipotoxicité on a voulu savoir quelles sont les voies de signalisation du métabolisme lipidique qui sont perturbées dans les maladies cardiaques. Nous avons précédemment montré que le palmitate, un acide gras saturé toxique, induit fortement l’expression de l’IL-6 dans les cardiomyocytes primaires. Par contre, le co-traitement de ces cellules avec de l'oléate, un acide gras mono-insaturé non-toxique cause une diminution de l’expression de IL-6 et de la toxicité due au palmitate. Ceci suggère que la lipotoxicité du palmitate pourrait être due à l'IL-6. Pour confirmer cela, nous avons testé l'effet de l'IL-6 knockdown (KD) de façon préliminaire, utilisant deux différents siARNs ciblant différentes séquences du gène IL-6. Nous avons constaté que la diminution du taux d’IL-6 avec les siARNs n’a pas atténué la mort cellulaire induite par le palmitate, mais au contraire l’a accentuée. Dans les cellules traitées avec l’oléate qui est normalement non toxique, le KD de l’IL-6 a provoqué une mort cellulaire. Ces résultats nous ont mené aux travaux de cette maitrise qui avait pour but d’évaluer comment l’IL-6 pouvait affecter le métabolisme lipidique. Tout d’abord, nous avons mesuré le taux d’IL-6 sécrété par les cardiomyocytes primaires suite aux traitements avec l’oléate et le palmitate. Ensuite nous avons évalué l’effet de l’IL-6 recombinante sur les cardiomyoblastes H9C2, d’une part sur la viabilité et d’une autre part sur la lipotoxicité en utilisant la chromatographie sur couche mince. Nos résultats préliminaires supportent l’idée que l’IL-6 serait nécessaire pour le métabolisme lipidique et aurait un effet cardioprotecteur à une certaine dose. Ainsi, elle protégerait les cardiomyocytes contre la lipotoxicité et cela en favorisant la β-oxydation (dégradation des acides gras) au niveau des mitochondries. Il reste à valider les travaux sur un modèle animal. L’ensemble des résultats constituent une ébauche pour comprendre le rôle que pourrait jouer l’IL-6 dans le métabolisme lipidique de la cardiomyopathie diabétique.Diabetic cardiomyopathy is associated with an accumulation of lipids in the heart muscle and to understand the effect of lipotoxicity, we wanted to know what are the signaling pathways of lipid metabolism that are disrupted in heart disease. We have previously shown that palmitate, a toxic saturated fatty acid, induces IL-6 in primary cardiomyocytes. In contrast, co-treatment of these cells with oleate, a non-toxic monounsaturated fatty acid, promotes a decrease in IL-6 expression and palmitate toxicity. This suggests that the lipotoxicity of palmitate may be caused by the IL-6. To confirm this, we tested the effect of IL-6 knockdown (KD), using two different siRNAs targeting the sequences of the IL-6 gene. Interestingly, the decrease of IL-6 with siRNAs did not attenuate the palmitate-induced cell death, but on the contrary accentuated it. In addition, the KD of IL-6 in cells treated with oleate, which is normally nontoxic, caused cell death. This last finding had led to the work of this master. Our objective was to evaluate the effect of IL-6 on lipid metabolism. Firstly, we measured the level of exogenous IL-6 secreted by primary cardiomyocytes treated with oleate or palmitate. Then, we tested the effect of IL6-Recombinant in cardiomyocytes, using the cardiomioblasts H9C2, on viability and on lipotoxicity using thin layer chromatography. Our preliminary results support that IL-6 could be necessary for lipid metabolism, with a cardioprotective effect at a certain dose by protecting cardiomyocytes against lipotoxicity by promoting β-oxydation (degradation of fatty acids) in the mitochondria. This results should be validated using an animal model. All of our results is a rough sketch to understand the role that IL-6 could play in the lipid metabolism of diabetic cardiomyopathy

Inventory of Beekeeping in the Algerian north (Tizi-ouzou and Bejaia)

The objective of this study is to make an inventory of the honey production in the counties of Bejaia and Tizi-Ouzou in northeastern Algeria. To achieve this, a survey was carried out on 31 beekeepers (14 in Bejaia and 17 in Tizi-Ouzou). The survey shows a social, cultural and religious role for 61.29% of the beekeepers. The beekeeping is primarily held by men (93.55%). It is the basic source of income for 64.52% of the respondents. The average age of the beekeepers is 42.90years (Min-Max: 28-67 years, Median: 40.5 years). The main products of the hive are honey (100%), swarms (16.13%), royal jelly (9.68%) and propolis (9.68%). The average honey production per hive is 7.70kg (Min-Max: 0.4-15kg hives, Median: 10kg). The average number of hives per beekeeper is 42.20 hives (Min-Max: 3-300 hives, Median: 17.50 hives). The average selling price of one kg of honey is 4000.00 DA / kg (Min-Max: 2000-5000DA; Median: 4000DA). The phenotype of the bee reported by 74.19% of the surveys is of small size with a long body and dark pigmentation corresponding to the breed “Apis mellifera intermissa”. Two apiculturists described another phenotype corresponding to the “Apis mellifera major”. The factors behind the motivations for beekeeping are consumption of honey (100%), income generation (90.32%), hobby (58.06%) and conservation of biodiversity (22.58%). The multiple constraints associated with several diseases, notably Varroase (mentioned by 80.65% of beekeepers), cause difficulties for the breeders. Thus they cannot profit maximum from beekeeping. Other constraints which were reported are; forest fires (35.48%), wasps (32.26%), absence of beekeeping professionals or technicians (29.03%), harsh and cold winters with snow (19.35%), high density of hives in the region (16.13%) and uncontrolled spreading of pesticides and crop protection products at farms (12.90%). The economic situation of the Algerian beekeepers can be optimized by improving the production potential of the local bees

Art and the Politics of Visibility : Contesting the Global, Local and the In-Between

L’intention de cet ouvrage est d’interroger les enjeux, les limites et les dangers de la visibilité – entendue comme représentation – au sein d’une culture visuelle transnationale. Il s’agit notamment de penser la réception des œuvres d’art non-occidentales par le public de l’Occident en évitant les écueils d’une lecture ethnographique ou « monoculturelle » (p .59) ; et cela tout en acceptant les lacunes qui empêcheraient de comprendre entièrement la portée des œuvres localement situées (Juli..