La vaccination est l'un des plus grands progrès réalisés en santé publique. Toutefois, malgré de nombreuses connaissances sur le système immunitaire, de nombreux pans d’ombre empêchent la conception de vaccins contre des pathogènes complexes. Pour pallier ce problème, une meilleure compréhension des modes d'action des vaccins est requise. En particulier, la plupart des vaccins nécessitent plusieurs immunisations pour induire une mémoire immunitaire adaptative au long terme, mais l'impact du délai entre primo-vaccination, induisant une mémoire primaire, et rappel(s) la restimulant pour générer une mémoire secondaire, est peu défini. De plus, la réponse immunitaire innée, induite à chaque immunisation et façonnant l'immunité adaptative, reste peu caractérisée dans ce contexte vaccinal. En vaccinant des macaques cynomolgus avec le virus de la vaccine modifiée Ankara, selon un schéma de primo-vaccination suivie d’un rappel homologue à deux mois, et en utilisant la cytométrie de masse couplée à des analyses bio-informatiques, nous avons caractérisé la réponse innée induite par chaque immunisation. Les réponses innées diffèrent entre primo-vaccination et rappel, avec induction par la primo-vaccination d’une modification phénotypique tardive des cellules innées, suggérant une meilleure capacité à répondre au rappel. De surcroît, la réduction à deux semaines du délai entre primo-vaccination et rappel abroge la mobilisation de ces cellules innées phénotypiquement modifiées et altère la qualité de la réponse humorale. En définitive, en plus de la réponse innée précoce, ce projet a mis en évidence l'induction par la primo-vaccination d'un vraisemblable entraînement inné tardif, un concept émergent traduisant la capacité de mémorisation des cellules innées via des modifications épigénétiques. Ce vraisemblable entraînement, non seulement des monocytes et cellules tueuses naturelles, mais aussi des cellules dendritiques et surprenamment des neutrophiles, est corrélé à la qualité de la mémoire immunitaire adaptative, de manière hautement dépendante du délai entre primo-vaccination et rappel. Ces résultats contribuent à ouvrir la voie vers l’optimisation rationnelle des futurs vaccins, via l'optimisation des calendriers vaccinaux et la valorisation de l'entraînement inné. / Vaccination is one of the best achievements made in public health. However, designing vaccines against complex pathogens is currently challenging. The immune system is indeed uncompletely characterized, despite large amount of accumulated knowledges. A better understanding of vaccine-induced immunity is then required to optimize vaccine design. In particular, while most vaccines require several immunizations to induce a long-lasting adaptive immune memory, little is known on the impact of the delay beween the prime inducing a primary memory and the boost restimulating it to induce a secondary memory. Also, the innate immunity induced by each immunization and shaping the adaptative immunity is poorly characterized in this vaccine context.We studied the innate immune responses in cynomolgus macaques immunized with the modified vaccinia virus Ankara, following an homologous prime-boost vaccination at two months apart. We applied mass cytometry and bioinformatic analyses to characterize the innate response induced by each immunization. We showed that prime and boost vaccination triggered distinct innate responses. Actually, prime induced late phenotypic modifications of innate cells. These phenotypic changes suggest a stronger ability to react to the boost. Moreover, reducing the delay between prime and boost to two weeks impeded the mobilization of these phenotypically modified innate cells, and qualitatively altered humoral response.In conclusion, beyond the early innate responses, these results highlight the late induction by the prime of "likely trained" innate cells. This emerging concept corresponds to the ability of innate cells to display memory features based on epigenetic modifications. This "likely training" occured not only on monocytes and NK cells, but also on dendritic cells and strikingly on neutrophils. It was deeply connected with adaptive immune memory establishment, in a prime-boost delay dependant fashion. These findings contribute to pave the way towards to the rationale design of future vaccines, via vaccine schedule optimization and harnessment of innate training.

The swift progression of technology has radically reshaped our lives, becoming a big part of our daily routines and paving the way for advancements in communication, automation, and information processing. OpenAI, a company at the forefront of artificial intelligence since 2015, has made remarkable strides towards making AI accessible and beneficial for all (OpenAI, n.d.). A notable accomplishment in their journey has been the development of Chat Generative Pre-trained Transformers (ChatGPT). This study aims to identify and explore the factors influencing students' acceptance and use of ChatGPT in academic settings. Despite the rising prominence of ChatGPT across various disciplines, understanding its acceptance and utilization, particularly within the sphere of higher education, remains limited. ChatGPT holds immense potential as a valuable asset for both students and educators. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) and a quantitative research approach, investigating these factors. The results suggest that student acceptance and use lies in Behavioral Intention, while Behavioral Initiation is influenced by both Effort Expectancy and Performance Expectancy.

Inledning: Mäns våld mot kvinnor är ett allvarligt samhällsproblem och ett folkhälsoproblem som måste bekämpas på flera olika sätt. Sammanfattningsvis är forskningsunderlaget för individuell psykoterapi med klienter som utövat våld begränsat och med specifikt psykodynamisk skolade psykoterapeuter ännu outforskat. Syftet: Syftet med den här studien var att undersöka hur psykodynamisk skolade psykoterapeuters erfarenhet av psykoterapi med klienter som utövat våld ser ut. Hur arbetar man? Vad är hjälpsamt? Metod: En kvalitativ metod. Psykodynamiskt skolade legitimerade psykoterapeuter intervjuades. Datan analyserades med tematisk analys. Resultat: Tre huvudteman framkom med tillhörande subteman: 1. Prioritering av klientens egen traumatisering är en nödvändighet 2. Våldet är i fokus i psykoterapin 3. Relationens är av betydelse i psykoterapin. Diskussion: Huvudfynden visade att erfarenhet fanns hos psykodynamisk skolade psykoterapeuter att bedriva psykoterapi med klienter som utövat våld. Både möjligheter och svårigheter framkom. Psykodynamiska interventioner användes i psykoterapin gemensamt med ett eklektiskt förhållningsätt. Relationen i psykoterapin användes som intervention och för att härbärgera klienten. Studiens resultat visar att psykoterapi med psykodynamisk skolade psykoterapeuter kan också vara en potentiell behandling att erbjuda klienter som utövat våld. Mer forskning behövs inom området. / Introduction: Men's violence against women is a major social problem and a public health problem that must be combated in several different ways. In summary, the research about individual psychotherapy with intimate partner violence perpetrators is limited and with specifically psychodynamically trained psychotherapists are still unexplored. Purpose: The purpose of this study was to investigate how psychodynamically trained psychotherapists experience psychotherapy with intimate partner violence perpetrators. How do they work? What is helpful? Method: A qualitative method. Psychodynamically trained licensed psychotherapists were interviewed. The data was analyzed using thematic analysis. Result: Three main themes emerged with associated subthemes: 1.Prioritization of the client's own traumatization is a necessity 2. Violence is in focus during psychotherapy 3. The relationship is important in psychotherapy. Discussion: The result of the study highlights that psychotherapy with psychodynamically trained psychotherapists can also be a treatment for intimate partner violence perpetrators. Both opportunities and difficulties emerged. Psychodynamic interventions are used in psychotherapy together with an eclectic approach. The relationship in psychotherapy is used as an intervention and as a tool to contain the client. The results of the study show that psychotherapy with psychodynamically trained psychotherapists can also be a potential treatment to offer intimate partner violence perpetrators. More research is needed in the area.

Thesis (M. A. (Psychology)) -- University of Limpopo, 2020 / Though recent South African legislation and policy documents have called for closer collaboration between Western-trained and traditional health practitioners, there is little evidence to show that there is a formal collaboration between the two categories of health care providers. Located within the interpretivist paradigm, and using an exploratory descriptive design, the researcher sought to explore the perceptions of Western-trained health practitioners (WTHPs) in Sekhukhune District (Limpopo Province) towards collaboration between themselves and traditional health practitioners (THPs) in treating mental illness. Seventeen WTHPs (males = 07; females = 10) from three hospitals in Sekhukhune District were selected through purposive sampling and requested to take part in the study. The sample comprised of five clinical psychologists, five medical officers working in psychiatric units, and seven psychiatric nurses. Data were collected using semi-structured interviews and analysed through thematic analysis. Specifically, Renata Tesch’s eight steps were used to analyse the data. The following psychological themes emerged from the study: (a) shared goals on collaboration; (b) a good effect on collaboration is anticipated; (c) managing interdependence between traditional and Western-trained practitioners; (d) proposed ideal structures of governance to govern the collaboration; (e) recommended legislations and policies on collaboration; (f) suggested factors that may foster collaboration; (g) proposed factors that hinder collaboration; and, (h) referral systems that exist in the health care. The findings suggest that some WTHPs are willing to collaborate with THPs, especially if proper guidelines for collaboration could be provided by the government. Some recommendations on an ideal structure of governance and legislation on collaboration were made by the WTHPs. The WTHPs highlighted factors that may hinder or facilitate closer collaboration between themselves and THPs in providing mental health services to communities.

Effectively treating infectious diseases often requires a multi-step approach to target different components involved in disease pathogenesis. Similarly, the COVID-19 pandemic has become a global health crisis that requires a comprehensive understanding of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) infection to develop effective therapeutics. One potential strategy to instill greater immune protection against COVID-19 is boosting the innate immune system. This boosting, termed trained immunity, employs immune system modulators to train innate immune cells to produce an enhanced, non-specific immune response upon reactivation following exposure to pathogens, a process that has been studied in the context of and clinical studies prior to the COVID-19 pandemic. Evaluation of the underlying pathways that are essential to inducing protective trained immunity will provide insight into identifying potential therapeutic targets that may alleviate the COVID-19 crisis. Here we review multiple immune training agents, including Bacillus Calmette-Guérin (BCG), β-glucan, and lipopolysaccharide (LPS), and the two most popular cell types involved in trained immunity, monocytes and natural killer (NK) cells, and compare the signaling pathways involved in innate immunity. Additionally, we discuss COVID-19 trained immunity clinical trials, emphasizing the potential of trained immunity to fight SARS-CoV-2 infection. Understanding the mechanisms by which training agents activate innate immune cells to reprogram immune responses may prove beneficial in developing preventive and therapeutic targets against COVID-19.

The discharge summary is an important document, summarizing a patient’s medical information during their hospital stay. It is crucial for communication between clinicians and primary care physicians. Creating a discharge sum- mary is a necessary task. However, it is time-consuming for physicians. Using technology to automatically generate discharge summaries can be helpful for physicians and assist them in concentrating more on the patients than writing clinical summarization notes and discharge summaries. This master’s thesis aims to contribute to the research of building a transformer-based model for an automated discharge summary with a pre-trained DistilBART language model. This study plans to answer this main research question: How e↵ective is the pre-trained DistilBART language model in predicting an automated discharge summary for multiple clinical texts? The research strategy used in this study is experimental. the dataset is MIMIC- III. To evaluate the e↵ectiveness of the model, ROUGE scores are selected. The result of this model is compared with the result of the baseline BART model, which is implemented on the same dataset in the other recent research. This study regards multiple document summarization as the process of combining multiple inputs into a single input, which is then summarized. The findings indicate an improvement in ROUGE-2 and ROUGE-Lsum in the DistilBART model in comparison with the baseline BART model. However, one important limitation was computational resource constraint. The study also provides eth- ical considerations and some recommendations for future works.

Multilingual natural language processing (NLP) is increasingly recognized for its potential in processing diverse text-type data, including those from social media, reviews, and technical reports. Multilingual language models like mBERT and XLM-RoBERTa (XLM-R) play a pivotal role in multilingual NLP. Notwithstanding their capabilities, the performance of these models largely relies on the availability of annotated training data. This thesis employs the multilingual pre-trained model XLM-R to examine its efficacy in sequence labelling to open-ended questions on democracy across multilingual surveys. Traditional annotation practices have been labour-intensive and time-consuming, with limited automation attempts. Previous studies often translated multilingual data into English, bypassing the challenges and nuances of native languages. Our study explores automatic multilingual annotation at the token level for democracy survey responses in five languages: Hungarian, Italian, Polish, Russian, and Spanish. The results reveal promising F1 scores, indicating the feasibility of using multilingual models for such tasks. However, the performance of these models is closely tied to the quality and nature of the training set. This research paves the way for future experiments and model adjustments, underscoring the importance of refining training data and optimizing model techniques for enhanced classification accuracy.

Nowadays, with the ever growing availability of options in many areas of our lives, it is crucial to have good ways to navigate your choices. This is why recommendation engines’ role is growing more important. Recommenders are often based on user-item interaction. In many areas like news and podcasts, however, by the time there is enough interaction data for an item, the item has already become irrelevant. This is why incorporating content features is desirable, as the content does not depend on the popularity or novelty of an item. Very often, there is text describing an item, so text features are good candidates for features within recommender systems. Within Natural Language Processing (NLP), pre-trained language models based on the Transformer architecture have brought a revolution in recent years, achieving state-of-the-art performance on many language tasks. Because of this, it is natural to explore how such models can play a role within recommendation systems. The scope of this work is on the intersection between NLP and recommendation systems where we investigate what are the effects of adding BERT-based encodings of titles and descriptions of movies and books to a recommender system. The results show that even in off-the-shelf BERT-models there is a considerable amount of information on movie and book similarity. It also shows that BERT based representations could be used in a recommender system for user recommendation to combine the best of collaborative and content representations. In this thesis, it is shown that adding deep pre-trained language model representations could improve a recommender system’s capability to predict good items for users with up to 0.43 AUC-ROC score for a shallow model, and 0.017 AUC-ROC score for a deeper model. It is also shown that SBERT can be fine-tuned to encode item similarity with up to 0.03 nDCG and up to 0.05 nDCG@10 score improvement. / Med den ständigt växande tillgängligheten av val i många delar av våra liv har det blivit viktigt att enkelt kunna navigera kring olika alternativ. Det är därför rekommendationssystems har blivit viktigare. Rekommendationssystem baseras ofta på interaktion-historiken mellan användare och artikel. När tillräckligt mycket data inom nyheter och podcast har hunnits samlats in för att utföra en rekommendation så har artikeln hunnit bli irrelevant. Det är därför det är önskvärt att införa innehållsfunktioner till rekommenderaren, då innehållet inte är beroende av popularitet eller nymodigheten av artikeln. Väldigt ofta finns det text som beskriver en artikel vilket har lett till textfunktioner blivit bra kandidater som funktion för rekommendationssystem. Inom Naturlig Språkbehandling (NLP), har förtränande språkmodeller baserad på transformator arkitekturen revolutionerat området de senaste åren. Den nya arkitekturen har uppnått toppmoderna resultat på flertal språkuppgifter. Tack vare detta, har det blivit naturligt att utforska hur sådana modeller kan fungera inom rekommendationssystem. Det här arbetet är mellan två områden, NLP och rekommendationssystem. Arbetet utforskar effekten av att lägga till BERT-baserade kodningar av titel och beskrivning av filmer, samt böcker till ett rekommendationssystem. Resultaten visar att även i förpackade BERT modeller finns det mycket av information om likheter mellan film och böcker. Resultaten visar även att BERT representationer kan användas i rekommendationssystem för användarrekommendationer, i kombination med kollaborativa och artikel baserade representationer. Uppsatsen visar att lägga till förtränade djupspråkmodell representationer kan förbättra rekommendationssystemens förmåga att förutsäga bra artiklar för användare. Förbättringarna är upp till 0.43 AUC-ROC poäng för en grundmodell, samt 0.017 AUC-ROC poäng för en djupmodell. Uppsatsen visar även att SBERT kan bli finjusterad för att koda artikel likhet med upp till 0.03 nDCG och upp till 0.05 nDCG@10 poängs förbättring.

The purpose of this study is to determine the effects of increased resistance training frequency on strength and hypertrophy in trained individuals. Six Studies were deemed eligible based on the inclusion exclusion criteria. The inclusion criteria for this review were healthy trained individuals. “Trained” refers to over one year of resistance training experience. Exclusion Criteria were study’s that examined either untrained or obese individuals as participants. The evidence indicates a dose-response trend in frequency. Resistance training each muscle group twice a week may be superior compared to once per week. Further more, resistance training each muscle group three times a week may enhance hypertrophy and strength adaptations even more compared to either once or twice a week. Recovery of the muscle may be reached in approximately 72 hours or 3 days. Mechanisms that may correlate to this phenomenon could be related to the more frequent elevations in muscle protein synthesis and physiological anabolic hormones. These results may help develop more specific guidelines in programming for intermediate to advanced athletes as well as lead way to more research on acute training variable manipulation.

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