A critical ability of any aircraft is to be able to detect potential collisions with other airborne objects, and maneuver to avoid these collisions. This can be done by utilizing sensors on the aircraft to monitor the sky for collision threats. However, several problems face a system which aims to use multiple sensors for target tracking. The data collected from sensors needs to be clustered, fused, and otherwise processed such that the flight control system can make accurate decisions based on it. Raw sensor data, while filled with useful information, is tainted with inaccuracies due to limitations and imperfections of the sensor. Combined use of different sensors presents further issues in how to handle disagreements between sensor data. This thesis project tackles the problem of processing data from multiple sensors (in this application, a radar and an infrared sensor) on an airborne platform in order to allow the aircraft to make flight corrections to avoid collisions.

The recent COVID-19 pandemic poses a serious threat to global public health, thus there is an urgent need to define the molecular mechanisms involved in SARS-CoV-2 spike (S) protein-mediated virus entry that is essential for preventing and/or treating this emerging infectious disease. In this study, we examined the blocking activity of human COVID-19 convalescent plasma by cell–cell fusion assays using SARS-CoV-2-S-transfected 293 T as effector cells and ACE2-expressing 293 T as target cells. We demonstrate that the SARS-CoV-2 S protein exhibits a very high capacity for membrane fusion and is efficient in mediating virus fusion and entry into target cells. Importantly, we find that COVID-19 convalescent plasma with high titers of IgG neutralizing antibodies can block cell–cell fusion and virus entry by interfering with the SARS-CoV-2-S/ACE2 or SARS-CoV-S/ACE2 interactions. These findings suggest that COVID-19 convalescent plasma may not only inhibit SARS-CoV-2-S but also cross-neutralize SARS-CoV-S-mediated membrane fusion and virus entry, supporting its potential as a preventive and/or therapeutic agent against SARS-CoV-2 as well as other SARS-CoV infections.

The cocktail party problem introduced in 1953 describes the ability to focus auditory attention in a noisy environment epitomised by a cocktail party. An individual with normal hearing uses several cues to unmask talkers of interest, such cues often lacks for people with hearing loss. This thesis explores the possibility to use a pair of glasses equipped with an inertial measurement unit (IMU), monocular camera and eye tacker to estimate an auditory scene and estimate the attention of the person wearing the glasses. Three main areas of interest have been investigated: estimating head orientation of the user; track faces in the scene and determine talker of interest using gaze. Implemented on a hearing aid, this solution could be used to artificially unmask talkers in a noisy environment. The head orientation of the user has been estimated with an extended Kalman filter (\EKF) algorithm, with a constant velocity model and different sets of measurements: accelerometer; gyrosope; monocular visual odometry (MVO); gaze estimated bias (GEB). An intrinsic property of IMU sensors is a drift in yaw. A method using eye data and gyroscope measurements to estimate gyroscope bias has been investigated and is called GEB. The MVO methods investigated use either optical flow to track features in succeeding frames or a key frame approach to match features over multiple frames.Using estimated head orientation and face detection software, faces have been tracked since they can be assumed as regions of interest in a cocktail party environment. A constant position EKF with a nearest neighbour approach has been used for tracking. Further, eye data retrieved from the glasses has been analyzed to investigate the relation between gaze direction and current talker during conversations.

Includes bibliographical references. / A Monte Carlo code MUGLU has been, written for the simulation of experiments to measure alpha-muon sticking in muon catalysed deuterium-tritium fusion. The experiments simulated are those which measure the ratio of αμ to α from fusion in a low density gas target by detecting collinear alpha-neutron coincidences and relying on the differing stopping powers of the α and αμ ions. The Monte Carlo simulations provide estimates of geometrical and detection efficiency factors required for the calculation of the sticking coefficient from the experimental measurements. Simulations have been made of alternative experimental geometries in order to investigate the α-neutron coincidence signature and other characteristics of existing and proposed systems. The characteristics of a neutron detector used in, one of the current sticking experiments (Rutherford Appleton Laboratory) have been studied experimentally, as well as simulated, using the T(d,n)α reaction to emulate the fusion source. The results obtained show that the dependence of the neutron detection efficiency on the position and angle of neutron incidence on the detector is significant and should be taken into account when determining sticking coefficients from alpha-neutron coincidence measurements.

RNA-Seq (Whole Transcriptome Shotgun Sequencing) provides an ideal platform to study the complete set of transcripts for a specific developmental stage or physiological condition. It reveals not only expression-level changes, but also structural changes in the coding sequences, including gene rearrangements. In this dissertation, I present my contributions to the development of computational tools for the robust and efficient analysis of RNA-seq data to support cancer research. To automate the laborious and computationally intensive procedure of RNA-seq data management, I worked on the development of Hydra, an RNA-seq pipeline for the parallel processing and quality control of large numbers of samples. With user-friendly reports on quality control and running checkpoints, Hydra makes the data processing procedure fast, efficient and reliable. Here, I report my application of the pipeline to the analysis of patient-derived lymphoma xenograft samples, to show Hydra’s ability to detect abnormalities (e.g., mouse tissue contamination) in the sequencing data. Because fusions play an important role in carcinogenesis, fusion detection has become an important area of methodological research. Several computational methods have been developed to identify fusion transcripts from RNA-seq data. However, all these methods require realignment to the transcriptome, a computationally expensive task, unnecessary in many cases. Here, I present QueryFuse, a novel gene-specific fusion-detection algorithm for aligned RNA-seq data. It is designed to help biologists find and/or computationally validate fusions of interest quickly, and to annotate the detected events with visualization and detailed properties of the supporting reads. By focusing the fusion detection on read pairs aligned to query genes, we can not only reduce realignment time, but also afford to use a more accurate but computationally expensive local aligner. In the extensive evaluation I performed, I obtained comparable or better results compared with two widely adopted tools (deFuse and TophatFusion) on two simulated datasets, as well as on cell line datasets with known fusions. Finally, I contributed to the identification of a novel fusion event in lymphoma, with potential therapeutic implications in clinical samples. I validated this fusion in silico by my putative reference method before experimental validation.

Les cancers pédiatriques représentent la première cause de décès par maladie chez les enfants puisque 20% des patients présentent des résistances et rechutent. Les traitements actuels sont à leurs limites d'efficacité mais aussi de toxicité. L'amélioration des connaissances de la biologie des tumeurs résistantes et l'identification de nouvelles cibles moléculaires sont nécessaires pour ces pathologies rares, extrêmement diverses et mal comprises. Les oncogènes de fusion sont responsables de l’oncogenèse de ~ 20% des cancers. En raison de la biologie particulière des cancers pédiatriques, nous avons émis l'hypothèse qu’ils seraient dus à des oncogènes de fusion. Leur exploration, particulièrement à la rechute où les données actuelles sont limitées, permettrait de mieux comprendre ces pathologies et d’introduire de nouvelles cibles thérapeutiques. Notre premier objectif était de détecter de nouveaux transcrits de fusion parmi 48 patients pédiatriques en rechute inclus dans l'essai de médecine de précision MOSCATO-01 mené à Gustave Roussy. A partir de données de RNA-seq, nous avons détecté l’ensemble des fusions retrouvées dans ces tumeurs et classé les plus pertinentes selon leur fonction et leur potentiel thérapeutique. Un nouvel oncogène de fusion nommé LMO3-BORCS5 retrouvé chez un patient atteint de sarcome d'Ewing dans les biopsies tumorales à la rechute et au diagnostic, et dans diverses lignées cellulaires a particulièrement retenu notre attention. Nos études fonctionnelles montrent que LMO3-BORCS5 a un rôle important dans la tumorigenèse et la sensibilité au traitement et que BORCS5 fonctionnerait, in vivo, comme un gène suppresseur de tumeur. Ces résultats montrent le rôle critique des transcrits de fusion dans l'initiation et la progression tumorale ainsi que dans l’acquisition de résistance au traitement et que les fusions isolées ne doivent pas être ignorées. A l'avenir, la prise en charge des cancers réside en une médecine moléculaire de précision, les oncogènes de fusions représentant l'un des outils les plus remarquables en clinique. / Pediatric cancers represent the first cause of death by disease in children as 20% of patients harbor resistances and die from recurrence of the malignancy. Nowadays treatment are at their limits of efficacy but also toxicity. Improving the knowledge on the biology of the relapsed/resistant tumors and identifying new molecular targets are needed for these rare, extremely diverse and misunderstood pathologies. Fusion oncogenes are known to be major drivers of oncogenesis, responsible for ~20% of cancers. Due to their particular biology, we hypothesized that pediatric tumors would be more likely to harbor fusions and that their exploration, especially at relapse where very limited data are available, could help to understand the particularity of these malignancies and consequently to provide new therapeutic targets. Our first aim was to detect new fusion transcripts in a cohort of 48 pediatric patients at relapse included in the precision medicine trial MOSCATO-01, previously conducted in Gustave Roussy. From the RNA-seq data, we described the landscape of fusions in these tumors and classified the most confident candidates according to their biological function and druggability predictions. A new fusion named LMO3-BORCS5 found in biopsies of a patient with Ewing's sarcoma, at diagnosis and at relapse as well as in various cell lines particularly caught our attention. Functional studies showed that LMO3-BORCS5 has a high impact on tumorigenesis and treatment sensitivity and that BORCS5 would act, in vivo, as a tumor suppressor gene. Our results strengthened the critical role of fusion transcripts in tumor initiation, progression and resistance and reinforces the idea that a private fusion oncogene should not be ignored. Indeed, the future of cancer care lays in precision medicine, with fusion transcripts being one of the most remarkable tool for treatment decision.

Lumbar fusion performed among injured workers has dramatically increased over the past two decades, coinciding with the increased use of more advanced surgical technology. Despite recent changes in how this surgery is performed, few outcome studies have been conducted, particularly among workers compensation populations. In prior studies, several biopsychosocial risk factors were found to be predictors of functional outcomes of lumbar fusion. Considering the recent changes in lumbar fusion surgery, there is a need to identify how patient outcomes have changed among injured workers, and whether a biopsychosocial model continues to be predictive of outcomes. The current study aimed to address multidimensional patient outcomes associated with lumbar fusion and examine the relationship between presurgical biopsychosocial variables and outcomes by testing the predictive efficacy of a multiple variable model. Injured workers (N = 245) who underwent their first lumbar fusion between 1998 and 2007 were included in a retrospective-cohort study performed in two phases that involved coding presurgical information documented in patient medical charts in the Worker's Compensation Fund of Utah computer database (Phase 1) and administering a telephone outcome survey with patients at least 2 years post-surgery (Phase 2). Of the total sample, 45% (n = 110) of patients were contacted and completed follow-up outcome surveys on several measures of patient satisfaction, quality of life, fusion status, dysfunction level, disability status, pain, and general physical and mental health functioning. Results revealed injured workers reported a solid fusion rate of 89.0%, disability rate of 28.7%, and a poor outcome rate of 57.1%. Multiple linear regression analyses demonstrated an eight variable model was a statistically significant predictor of multiple patient outcomes. Involvement of a nurse case manger, vocational rehabilitation, and litigation at the time of fusion were the most prominent predictors across outcome measures, while age and depression history showed modest prediction of outcomes. Prior back operations, number of vertebral levels fused, and type of instrumentation showed no statistically significant prediction of outcomes. Results were evaluated and compared to prior lumber fusion studies on injured worker and fusion outcome literature, in general. Specific implications for our findings and limitations associated with this study were addressed.

As computing power becomes more and more available, robotic systems are moving away from active sensors for environmental awareness and transitioning into passive vision sensors.  With the advent of teleoperation and real-time video tracking of dynamic environments, the need to stabilize video onboard mobile robots has become more prevalent. This thesis presents a digital stabilization method that incorporates inertial fusion with a Kalman filter.  The camera motion is derived visually by tracking SIFT features in the video feed and fitting them to an affine model.  The digital motion is fused with a 3 axis rotational motion measured by an inertial measurement unit (IMU) rigidly attached to the camera. The video is stabilized by digitally manipulating the image plane opposite of the unwanted motion. The result is the foundation of a robust video stabilizer comprised of both visual and inertial measurements.  The stabilizer is immune to dynamic scenes and requires less computation than current digital video stabilization methods. / Master of Science

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With the advent of the Global Position System (GPS), we now have the ability to determine absolute position anywhere on the globe. Although GPS systems work well in open environments with no overhead obstructions, they are subject to large unavoidable errors when the reception from some of the satellites is blocked. This occurs frequently in urban environments, such as downtown New York City. GPS systems require at least four satellites visible to maintain a good position 'fix' . Tall buildings and tunnels often block several, if not all, of the satellites. Additionally, due to Selective Availability (SA), where small amounts of error are intentionally introduced, GPS errors can typically range up to 100 ft or more. This thesis proposes several methods for improving the position estimation capabilities of a system by incorporating other sensor and data technologies, including Kalman filtered inertial navigation systems, rule-based and fuzzy-based sensor fusion techniques, and a unique map-matching algorithm. / Master of Science