La présentation d'antigène par les molécules d'histocompatibilité majeure de classe I (CMHI) permet au système immunitaire adaptatif de détecter et éliminer les agents pathogènes intracellulaires et des cellules anormales. La surveillance immunitaire est effectuée par les lymphocytes T CD8 qui interagissent avec le répertoire de peptides associés au CMHI présentés à la surface de toutes cellules nucléées. Les principaux gènes humains de CMHI, HLA-A et HLA-B, sont très polymorphes et par conséquent montrent des différences dans la présentation des antigènes. Nous avons étudié les différences qualitatives et quantitatives dans l'expression et la liaison peptidique de plusieurs allotypes HLA. Utilisant la technique de cytométrie de flux quantitative nous avons établi une hiérarchie d'expression pour les quatre HLA-A, B allotypes enquête. Nos résultats sont compatibles avec une corrélation inverse entre l'expression allotypique et la diversité des peptides bien que d'autres études soient nécessaires pour consolider cette hypothèse. Les origines mondiales du répertoire de peptides associés au CMHI restent une question centrale à la fois fondamentalement et dans la recherche de cibles immunothérapeutiques. Utilisant des techniques protéogénomiques, nous avons identifié et analysé 25,172 peptides CMHI isolées à partir des lymphocytes B de 18 personnes qui exprime collectivement 27 allotypes HLA-A,B. Alors que 58% des gènes ont été la source de 1-64 peptides CMHI par gène, 42% des gènes ne sont pas représentés dans l'immunopeptidome. Dans l'ensemble, l’immunopeptidome présenté par 27 allotypes HLA-A,B ne couvrent que 17% des séquences exomiques exprimées dans les cellules des sujets. Nous avons identifié plusieurs caractéristiques des transcrits et des protéines qui améliorent la production des peptides CMHI. Avec ces données, nous avons construit un modèle de régression logistique qui prédit avec une grande précision si un gène de notre ensemble de données ou à partir d'ensembles de données indépendants génèrerait des peptides CMHI. Nos résultats montrent la sélection préférentielle des peptides CMHI à partir d'un répertoire limité de produits de gènes avec des caractéristiques distinctes. L'idée que le système immunitaire peut surveiller des peptides CMHI couvrant seulement une fraction du génome codant des protéines a des implications profondes dans l'auto-immunité et l'immunologie du cancer. / Antigen presentation by major histocompatibility complex class I (MHCI) molecules allows the adaptive immune system to detect and eliminate intracellular pathogens or abnormal cells. Immune surveillance is executed by CD8 T cells that monitor the repertoire of MHCI-associated peptides (MAPs) presented at the surface of all nucleated cells. The primary human MHCI genes, HLA-A and HLA-B, are highly polymorphic and consequentially demonstrate differences in antigen presentation. We investigated qualitative and quantitative differences in expression and peptide binding. Using quantitative flow cytometry we establish clear hierarchy of expression for the four HLA-A,B allotypes investigated. Our results are consistent with an inverse correlation between expression and peptide diversity although further work is necessary to solidify this hypothesis. The global origins of the MAP repertoire remains a central question both fundamentally and in the search for immunotherapeutic targets. Using proteogenomics, we identified and analyzed 25,172 MAPs isolated from B lymphocytes of 18 individuals who collectively expressed 27 HLA-A,B allotypes. While 58% of genes were the source of 1-64 MAPs per gene, 42% of genes were not represented in the immunopeptidome. Overall, we estimate the immunopeptidome presented by 27 HLA-A,B allotypes covered only 17% of exomic sequences expressed in subjects’ cells. We identified several features of transcripts and proteins that enhance MAP production. From these data we built a logistic regression model that predicts with high accuracy whether a gene from our dataset or from independent datasets would generate MAPs. Our results show preferential selection of MAPs from a limited repertoire of gene products with distinct features. The notion that the immune system can monitor MAPs covering only a fraction of the protein coding genome has profound implications in autoimmunity and cancer immunology.

Depuis quatre décennies, la prévalence des affections respiratoires chroniques de l'enfant a considérablement augmenté dans les pays développés. Les conditions de survenue de ces affections sont complexes, mais de nombreux travaux suggèrent la contribution importante de l'exposition par inhalation aux polluants de l'air intérieur. Dans ce contexte, cette thèse vise à évaluer l’exposition cumulée à une gamme de polluants chimiques et biologiques de l’air intérieur dans un échantillon donné de logements. Il a également pour objectif de créer une typologie des logements en fonction de leur multi-contamination, et vise à construire des modèles explicatifs des concentrations des polluants en fonction des caractéristiques de l’habitat et des habitudes de vie des occupants.Une enquête environnementale a été menée dans 150 logements issus de la cohorte Pélagie, suivie en Bretagne depuis 2002. Des prélèvements ont permis de mesurer la concentration de 8 aldéhydes, 4 THM, 22 autres COV, 9 COSV et 4 genres de moisissures dans l’air de ces logements. Celles-ci, ainsi que 4 allergènes ont également été dosés dans des échantillons de poussières. Les paramètres d’ambiance (température, humidité relative et dioxyde de carbone) ont été mesurés. Un questionnaire renseigné par les familles a permis de collecter des informations sur les logements et leurs occupants : structure et historique du bâtiment, revêtements, ménage, chauffage, aération, utilisation de certains produits ou réalisation d’activités particulières. Ces données ont été analysées par des approches statistiques multivariées, et des modèles de régression linéaire et logistique ont été mis en oeuvre pour relier les concentrations des contaminants aux caractéristiques des logements. Ces mesures ont mis en évidence une contamination importante et systématique des logements par une grande part des contaminants chimiques et biologiques, à des niveaux parfois élevés au regard d’études comparables et des valeurs guides lorsqu’elles existent. Des analyses en composantes principales ont permis de mettre en évidence des sous-groupes de composés qui ont pu être interprétés en termes de sources, et de sélectionner un certain nombre de composés traceurs représentatifs de chaque sous-groupe. Une analyse factorielle multiple a permis de répartir les logements en 7 classes, chacune présentant un profil de multi-contamination particulier. Enfin, les modèles de régression linéaire et logistique construits pour les composés traceurs permettent d’expliquer entre 5 et 60% de la variabilité des concentrations, et mettent en évidence la multiplicité des sources, l’importance de la description précise des environnements intérieurs, et l’impact des paramètres d’ambiance sur ces concentrations. Ce travail décrit donc une contribution importante à l’évaluation des expositions aux contaminants de l’air intérieur et fournit un certain nombre d’éléments quant à la prédiction des expositions dans les environnements intérieurs. / For the last four decades, the prevalence of chronic respiratory affections in children has increased dramatically in developed countries. Occurring conditions of these affections are complex, but many studies suggest the important contribution of inhalation exposure to indoor air pollutants. In this context, this thesis aims to assess the cumulative exposure to a range of chemical and biological pollutants in indoor air in a given sample of dwellings. It also aims to create a typology of these dwellings based on their multi-contamination, and to build explanatory models for concentrations of pollutants based on characteristics of the dwellings and lifestyle of the occupants. An environmental survey was conducted in 150 dwellings from the Pelagie cohort, followed in Brittany since 2002. We measured the concentration of 8 aldehydes, 4 THMs, 22 other VOCs, 9 SVOCs and 4 mold genera in the air of these dwellings. Molds as well as four allergens were also measured in dust samples. Ambient parameters (temperature, relative humidity and carbon dioxide) were also measured. A questionnaire completed by families allowed collecting information on dwellings and their occupants: structure and history of the building, wall and floor coatings, cleaning, heating and ventilation habits, use of certain products or performing specific activities. These data were analyzed by multivariate statistical approaches, and linear and logistic regression models were used to link the concentrations of the contaminants with the housing characteristics. These measures showed an important and systematic contamination of the dwellings by a large amount of both chemical and biological contaminants, sometimes at relatively high levels regarding comparable studies and guideline values when they exist. Principal components analysis allowed to identify subgroups of compounds that could be interpreted in terms of sources, and to select representative compounds of each subgroup. A multiple factor analysis was used to classify the dwellings into 7 categories, each with a special multi-contamination profile. Finally, linear and logistic regression models built for the representative compounds explained between 5 and 60% of the variability of the concentrations, and highlighted the multiplicity of sources, the importance of a precise description of indoor environments, and the impact of the ambient parameters on these concentrations. This work thus describes an important contribution to the exposure assessment to indoor air contaminants and provides elements for prediction of exposures in indoor environments.

For the west coast of North America, from northern California to southern Washington, a habitat suitability prediction framework was developed to support wave energy device siting. Concern that wave energy devices may impact the seafloor and benthos has renewed research interest in the distribution of marine benthic invertebrates and factors influencing their distribution. A Bayesian belief network approach was employed for learning species-habitat associations for Rhabdus rectius, a tusk-shaped marine infaunal Mollusk. Environmental variables describing surficial geology and water depth were found to be most influential to the distribution of R. rectius. Water property variables, such as temperature and salinity, were less influential as distribution predictors. Species-habitat associations were used to predict habitat suitability probabilities for R. rectius, which were then mapped over an area of interest along the south-central Oregon coast. Habitat suitability prediction models tested well against data withheld for crossvalidation supporting our conclusion that Bayesian learning extracts useful information available in very small, incomplete data sets and identifies which variables drive habitat suitability for R. rectius. Additionally, Bayesian belief networks are easily updated with new information, quantitative or qualitative, which provides a flexible mechanism for multiple scenario analyses. The prediction framework presented here is a practical tool informing marine spatial planning assessment through visualization of habitat suitability. / Graduation date: 2012

Many metropolitan cities motivate people to exploit public bike-sharing programs as alternative transportation for many reasons. Due to its’ popularity, multiple types of research on optimizing public bike-sharing systems is conducted on city-level, neighborhood-level, station-level, or user-level to predict the public bike demand. Previously, the research on the public bike demand prediction primarily focused on discovering a relationship with weather as an external factor that possibly impacted the bike usage or analyzing the bike user trend in one aspect. This work hypothesizes two external factors that are likely to affect public bike demand: weather and air pollution. This study uses a public bike data set, daily temperature, precipitation data, and air condition data to discover the trend of bike usage using multiple machine learning techniques such as Decision Tree, Naïve Bayes, and Random Forest. After conducting the research, each algorithm’s output is evaluated with performance comparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is an efficient classifier for the bike demand prediction by weather and precipitation, and Decision Tree performs best for the bike demand prediction by air pollutants. Also, the three class labelings in the daily bike demand has high specificity, and is easy to trace the trend of the public bike system.

No description available.

The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investigated. The statistical machine learning methods applied are Random Forests and Neural Networks along with the standard method Logistic Regression. The data is collected from a well established retail company and the purpose of the project is thus to investigate which uplift modeling approach and statistical machine learning method that yields in the best performance given the data used in this project. The variable selection step was shown to be a crucial component in the modeling processes as so was the amount of control data in each data set. For the uplift to be successful, the method of choice should be either the Modeling Uplift Directly using Random Forests, or the Class Variable Transformation using Logistic Regression. Neural network - based approaches are sensitive to uneven class distributions and is hence not able to obtain stable models given the data used in this project. Furthermore, the Subtraction of Two Models did not perform well due to the fact that each model tended to focus too much on modeling the class in both data sets separately instead of modeling the difference between the class probabilities. The conclusion is hence to use an approach that models the uplift directly, and also to use a great amount of control data in each data set. / Behovet av att kunna modellera den verkliga vinsten av riktad marknadsföring har lett till den idag vanligt förekommande metoden inkrementell responsanalys. För att kunna utföra denna typ av metod krävs förekomsten av en existerande testgrupp samt kontrollgrupp och målet är således att beräkna differensen mellan de positiva utfallen i de två grupperna. Sannolikheten för de positiva utfallen för de två grupperna kan effektivt estimeras med statistiska maskininlärningsmetoder. De inkrementella responsanalysmetoderna som undersöks i detta projekt är subtraktion av två modeller, att modellera den inkrementella responsen direkt samt en klassvariabeltransformation. De statistiska maskininlärningsmetoderna som tillämpas är random forests och neurala nätverk samt standardmetoden logistisk regression. Datan är samlad från ett väletablerat detaljhandelsföretag och målet är därmed att undersöka vilken inkrementell responsanalysmetod och maskininlärningsmetod som presterar bäst givet datan i detta projekt. De mest avgörande aspekterna för att få ett bra resultat visade sig vara variabelselektionen och mängden kontrolldata i varje dataset. För att få ett lyckat resultat bör valet av maskininlärningsmetod vara random forests vilken används för att modellera den inkrementella responsen direkt, eller logistisk regression tillsammans med en klassvariabeltransformation. Neurala nätverksmetoder är känsliga för ojämna klassfördelningar och klarar därmed inte av att erhålla stabila modeller med den givna datan. Vidare presterade subtraktion av två modeller dåligt på grund av att var modell tenderade att fokusera för mycket på att modellera klassen i båda dataseten separat, istället för att modellera differensen mellan dem. Slutsatsen är således att en metod som modellerar den inkrementella responsen direkt samt en relativt stor kontrollgrupp är att föredra för att få ett stabilt resultat.

No description available.

Many, if not all people have online social accounts (OSAs) on an online community (OC) such as Facebook (Meta), Twitter (X), Instagram (Meta), Mastodon, Nostr. OCs enable quick and easy interaction with friends, family, and even online communities to share information about. There is also a dark side to Ocs, where users with malicious intent join OC platforms with the purpose of criminal activities such as spreading fake news/information, cyberbullying, propaganda, phishing, stealing, and unjust enrichment. These criminal activities are especially concerning when harming minors. Detection and mitigation are needed to protect and help OCs and stop these criminals from harming others. Many solutions exist; however, they are typically focused on a single category of malicious intent detection rather than an all-encompassing solution. To answer this challenge, we propose the first steps of a framework for analyzing and identifying malicious intent in OCs that we refer to as malicious mntent detection framework (MIDF). MIDF is an extensible proof-of-concept that uses machine learning techniques to enable detection and mitigation. The framework will first be used to detect malicious users using solely relationships and then can be leveraged to create a suite of malicious intent vector detection models, including phishing, propaganda, scams, cyberbullying, racism, spam, and bots for open-source online social networks, such as Mastodon, and Nostr.

<p>From idea generation to conceptualization and development of products and technologies is a non-linear and iterative process. The work in this thesis follows a process that initiates with the review of existing technologies and products, examining their unique value proposition in the context of the specific applications for which they are designed. Next, the unmet needs of novel or emerging applications are identified that require new product or technologies. Once these user needs and product requirements are identified, the specific functions to be addressed by the product are specified. The subsequent process of design of products and technologies to meet these functions is enabled by engineering tools such as three-dimensional modelling, physics-based simulations, and manufacturing of a minimum viable prototype. In these steps, un-biased decisions have to be taken using weighted decision matrices to cater to the design requirements. Finally, the minimum viable prototype is tested to demonstrate the principal functionalities. The results obtained from the testing process identify the potential future improvements in the next generations of the prototype that would subsequently inform the final design of product. This thesis adopted this methodology to initiate the design two product-prototypes: i) an image-recognition-integrated service (IRIS) robotic hand for children and ii) cascaded multi-core vapor chamber (CMVC) for improving performance of next-generation computing systems. Minimum viable product-prototypes were manufactured to demonstrate the principal functionalities, followed by clear identification of future potential improvements. Tests of the prosthetic hand indicate that the image-recognition based feedback can successfully drive the actuators to perform the intended grasping motions. Experimental testing with the multi-core vapor chamber demonstrates successful performance of the prototype, which offers notable reduction in temperatures relative to the existing benchmark solid copper spreader. </p>