L’évaluation des impacts d’un dépistage de porteurs de maladies génétiques : la perspective des personnes visées par le dépistage

Au Québec, les personnes ayant une ascendance géographique des régions du Saguenay-Lac- Saint-Jean, de Charlevoix et de la Haute-Côte-Nord ont une prévalence plus élevée que le reste de la population québécoise d’être porteurs de certaines maladies héréditaires récessives. Depuis 2018, une offre de tests de porteurs en ligne est proposée par le Ministère de la Santé et des Services Sociaux du Québec pour quatre maladies autosomiques récessives : l’acidose lactique congénitale, la tyrosinémie héréditaire de type 1, la neuropathie sensitivomotrice avec ou sans agénésie du corps calleux et l’ataxie récessive spastique de Charlevoix-Saguenay. Ce même dépistage peut être offert en contexte clinique, chez des adultes éligibles lors de consultations en lien avec un désir de grossesse ou une grossesse en cours. Les objectifs de ce projet de recherche sont (1) de décrire l’expérience des patients ayant eu accès au dépistage de porteurs en contexte clinique et (2) d’identifier, analyser et comparer les enjeux éthiques soulevés par un dépistage de porteurs dans le cadre d’un programme structuré versus un dépistage de porteurs en contexte clinique. Pour ce faire, une série de questionnaires destinée aux patients auxquels le dépistage a été offert lors d’un rendez-vous en clinique a été mise en place et une analyse éthique à l’aide d’un cadre éthique de santé publique a été réalisée. À la lumière de ce projet, l’autonomie décisionnelle du patient est mise de l’avant. Des pistes de réflexion ainsi que des recommandations ont été développées afin de répondre au mieux aux besoins des personnes qui considèrent avoir recours à des tests de porteurs.In Quebec, people with geographical ancestry from the Saguenay-Lac-Saint-Jean, Charlevoix and Haute-Côte-Nord regions have a higher prevalence than the rest of the Quebec population of being carriers of specific recessive hereditary diseases. Since 2018, online carrier testing has been offered by the Ministère de la Santé et des Services Sociaux du Québec for four autosomal recessive diseases: congenital lactic acidosis, hereditary tyrosinemia type 1, sensitivomotor neuropathy with or without agenesis of the corpus callosum and Charlevoix-Saguenay recessive spastic ataxia. This same screening can be offered in a clinical setting, to eligible adults during consultations related to a pregnancy desire or a pregnancy in progress. The objectives of this research project are (1) to describe the experience of patients who have had access to carrier screening in a clinical setting and (2) to identify, analyze and compare the ethical issues raised by carrier screening in a structured program versus carrier screening in a clinical setting. To this end, a series of questionnaires was administered to patients who were offered screening during a clinic appointment, and an ethical analysis was carried out using a public health ethics framework. In the light of this project, the patient's decision-making autonomy is emphasized. A number of ideas and recommendations have been developed to best meet the needs of people considering carrier testing

Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10 5 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Electrical resistance tomography of unsaturated flow and transport in Yucca Mountain

Electrical Resistance Tomography (ERT), a new geophysical imaging technique, was used to study the movement of a tracer through the test block at the Unsaturated Zone Transport Test (UZTT) at Busted Butte, Nevada. Data were collected four times starting in July and ending in early September, 1998. ERT baseline images show a resistivity structure which is consistent with the known lithology in the rear part of the test block. There appears to be a low resistivity region in the front half of the block, particularly near the bottom. Difference images from August 19 and September 9 show clear and consistent resistivity decreases in the region near injection holes 18, 20, and 21 which can be associated with the injection of conductive water. The images show very little effect in the region around the other injection holes, 23, and 24 through 27 where far less water was injected. Difference images from August 19 and September 9 show resistivity decreases which could be interpreted as water moving down into the block. This is the same region which has an anomalously low resistivity in the baseline image. These results should be considered preliminary, and are subject to further interpretation

Material Decomposition in Spectral CT using deep learning: A Sim2Real transfer approach

The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomography is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specific materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. We compared projection- and image-based decomposition approaches where the network is trained to decompose the materials either in the projection or in the image domain. The proposed Sim2Real transfer strategies are compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data

The effects of melt depletion and metasomatism on highly siderophile and strongly chalcophile elements: S–Se–Te–Re–PGE systematics of peridotite xenoliths from Kilbourne Hole, New Mexico

The composition of the Earth’s upper mantle is a function of melt depletion and subsequent metasomatism; the latter obscuring many of the key characteristics of the former, and potentially making predictions of Primitive Upper Mantle (PUM) composition problematic. To date, estimates of PUM abundances of highly siderophile element (HSE = platinum group elements (PGE) and Re) and the strongly chalcophile elements Se and Te, have been the subject of less scrutiny than the lithophile elements. Critically, estimates of HSE and strongly chalcophile element abundances in PUM may have been derived by including a large number of metasomatized and refertilized samples whose HSE and chalcophile element abundances may not be representative of melt depletion alone. Unravelling the effects of metasomatism on the S–Se–Te–HSE abundances in peridotite xenoliths from Kilbourne Hole, New Mexico, USA, potentially provides valuable insights into the abundances of HSE and strongly chalcophile element abundances in PUM. Superimposed upon the effects of melt depletion is the addition of metasomatic sulfide in approximately half of the xenoliths from this study, while the remaining half have lost sulfide to a late S-undersaturated melt. Despite these observations, the Kilbourne Hole peridotite xenoliths have HSE systematics that are, in general, indistinguishable from orogenic peridotites and peridotite xenoliths used for determination of PUM HSE abundances. This study represents the first instance where Se-Te-HSE systematics in peridotite xenoliths are scrutinized in detail in order to test their usefulness for PUM estimates. Despite earlier studies attesting to the relative immobility of Se during supergene weathering, low S, Se, Os and Se/Te in peridotite xenoliths suggests that Se may be more mobile than originally thought, and for this reason, peridotite xenoliths may not be suitable for making predictions of the abundance of these elements in PUM. Removal of Se, in turn, lowers the Se/Te in basalt-borne xenolithic peridotites to subchondritic values. This is in contrast to what has been recently reported in kimberlite-borne peridotite xenoliths. Moreover, Te added to melt depleted peridotite in metasomatic sulfide is more difficult to remove in a S-undersaturated melt than the HSE and Se due to the generation of micron-scale tellurides. The effects of these processes in orogenic peridotites and xenoliths, from which PUM abundances have been calculated, require further scrutiny before unequivocal Se-Te-Re-PGE values for PUM can be derived

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Pathogenomic Sequence Analysis of B. cereus and B. Thuringiensis isolates closely related to Bacillus anthracis

The sequencing and analysis of two close relatives of Bacillus anthracis are reported. AFLP analysis of over 300 isolates of B. cereus, B. thuringiensis and B. anthracis identified two isolates as being very closely related to B. anthracis. One, a B. cereus, BcE33L, was isolated from a zebra carcass in Nambia; the second, a B. thuringiensis, 97-27, was isolated from a necrotic human wound. The B. cereus appears to be the closest anthracis relative sequenced to date. A core genome of over 3,900 genes was compiled for the Bacillus cereus group, including B anthracis. Comparative analysis of these two genomes with other members of the B. cereus group provides insight into the evolutionary relationships among these organisms. Evidence is presented that differential regulation modulates virulence, rather than simple acquisition of virulence factors. These genome sequences provide insight into the molecular mechanisms contributing to the host range and virulence of this group of organisms

Deep learning based methods for decomposition in spectral computed tomography : Application to knee osteoarthritis

La tomographie spectrale est une nouvelle modalité d'imagerie à rayons X qui permet d'acquérir des données avec une dimension énergétique. Cela est possible grâce aux détecteurs à comptage de photons qui classent les photons en fonction de leur énergie. En exploitant cette dimension énergétique nous pouvons estimer les matériaux constituant l'objet ou reconstruire des images mono-énergétiques. Dans le domaine médical, la décomposition de matériaux est souvent opérée sur des bases de matériaux anatomiques tels que les tissus mous et l'os, combinés à des produits de contraste. D'un point de vue mathématique, la décomposition en matériaux est un problème inverse non linéaire et mal posé. De nombreuses méthodes de décomposition de matériaux ont été développées. Certaines sont basées sur l'inversion d'un modèle physique prenant en compte la source, la réponse du détecteur et l'atténuation théorique des matériaux constituant l'objet. Toutefois, les algorithmes d'apprentissage profond peuvent également être utilisés pour résoudre des problèmes inverses. Des travaux récents en tomographie ont montré que ceux-ci peuvent améliorer les qualités des reconstructions et sont plus rapides que les algorithmes basés sur le modèle. Le but de la thèse est de développer des algorithmes d'apprentissage profond pour la décomposition de matériaux et la reconstruction d'images mono-énergétiques en tomographie spectrale et de les évaluer par rapport à des méthodes basées sur des techniques d'optimisation. Nous nous sommes particulièrement focalisés sur l'application de la tomographie spectrale à l'arthrose du genou sans produit de contraste. Des genoux humains excisés ont été scannés à l'aide d'un prototype de scanner spectral mais aussi par tomographie synchrotron mono-énergétique afin d'avoir une image de référence. Des fantômes de genoux ont été générés à partir de ces volumes synchrotron en segmentant les matériaux tels que les tissus mous, l'os et potentiellement le cartilage. Puis, les projections spectrales sont simulées en utilisant des modèles de scanners spectraux. Nous avons ensuite développé un algorithme d'apprentissage profond basé sur U-net afin d'effectuer la décomposition de matériaux (tissus mous et os) dans le domaine des projections. Les résultats sont comparés à un algorithme de décomposition itératif basé sur le modèle, Gauss-Newton régularisé. Nous reconstruisons également les images mono-énergétiques soit grâce à la décomposition de matériaux, soit directement grâce à une adaptation de la méthode d'apprentissage. Les méthodes sont comparées en calculant l'erreur quadratique moyenne et l'indice de similarité. Finalement, les méthodes basées sur le modèle et d'apprentissage sont appliquées sur des données réelles issues du prototype clinique de scanner spectral.Spectral computed tomography is an emerging modality of X ray imaging that allows to acquire energy resolved data thanks to photon counting detectors. They are able to sort photons according to their energy. This extra energy dimension allows to decompose the objet into its material constituents or to reconstruct virtual mono-energetic images. In the medical field, material decomposition is often based on anatomic materials like soft tissue and bone, combined with contast agents. From a mathematical point of view, material decomposition is an ill posed non linear inverse problem. Several material decomposition methods have been developped. Ones are based on the inversion of a physical model taking into account the source spectrum, detector reponse function and theoritical material constituents' attenuation. However, a lot of deep learning algorithms can be used to solve inverse problems. Recent studies in tomography show that they can improve reconstructions accuracy and are faster than the model-based one. The goal of this thesis is developping deep learning algorithms for material decomposition and mono-energetic images reconstruction and evaluating them with respect to methods based on optimization. In particular, we focus on the application of spectral computed tomography to knee osteoarthritis without contrast agent. Excised human knees are scanned with a spectral computed tomography scanner but also with synchrotron mono-energetic computed tomography to have a gold standard. Phantoms are generated from this synchrotron computed tomography volumes, segmenting materials as soft tissue, bone and potentially cartilage. Then, spectral projections are simulated using spectral computed tomography scanners models. After, we developped a learning algorithm based on U-net to decompose materials (soft tissue and bone) in the projection domain. Results are compared with a model-based iterative decomposition algorithm, regularized Gauss-Newton. We also reconstruct mono-energetic images thanks to decomposed material maps, or directly with an adaptation of the learning method. We compare the methods computing the mean squared error and the structural similarity matrix. Finally, model-based and learning-based methods are applied to real data from the spectral computed tomography clinic prototype