370 research outputs found

    Immune contexture monitoring in solid tumors focusing on Head and Neck Cancer

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    Forti evidenze dimostrano una stretta interazione tra il sistema immunitario e lo sviluppo biologico e la progressione clinica dei tumori solidi. L'effetto che il microambiente immunitario del tumore può avere sul comportamento clinico della malattia è indicato come "immunecontexture". Nonostante ciò, l'attuale gestione clinica dei pazienti affetti da cancro non tiene conto di alcuna caratteristica immunologica né per la stadiazione né per le scelte terapeutiche. Il tumore della testa e del collo (HNSCC) rappresenta il 7° tumore più comune al mondo ed è caratterizzato da una prognosi relativamente sfavorevole e dall'effetto negativo dei trattamenti sulla qualità della vita dei pazienti. Oltre alla chirurgia e alla radioterapia, sono disponibili pochi trattamenti sistemici, rappresentati principalmente dalla chemioterapia a base di platino-derivati o dal cetuximab. L'immunoterapia è una nuova strategia terapeutica ancora limitata al setting palliativo (malattia ricorrente non resecabile o metastatica). La ricerca di nuovi biomarcatori o possibili nuovi meccanismi target è molto rilevante quindi nel contesto clinico dell'HNSCC. In questa tesi ci si concentrerà sullo studio di tre possibili popolazioni immunitarie pro-tumorali studiate nell'HNSCC: i neutrofili tumore-associati (TAN), le cellule B intratumorali con fenotipo immunosoppressivo e i T-reg CD8+. Particolare attenzione è data all'applicazione di moderne tecniche biostatistiche e bioinformatiche per riassumere informazioni complesse derivate da variabili cliniche e immunologiche multiparametriche e per validare risultati derivati ​​in situ, attraverso dati di espressione genica derivati da dataset pubblici. Infine, la seconda parte della tesi prenderà in considerazione progetti di ricerca clinica rilevanti, volti a migliorare l'oncologia di precisione nell'HNSCC, sviluppando modelli predittivi di sopravvivenza, confrontando procedure oncologiche alternative, validando nuovi classificatori o testando l'uso di nuovi protocolli clinici come l'uso dell'immunonutrizione.Strong evidences demonstrate a close interplay between the immune system and the biological development and clinical progression of solid tumors. The effect that the tumor immune microenvironment can have on the clinical behavior of the disease is referred as the immuno contexture. Nevertheless, the current clinical management of patients affected by cancer does not take into account any immunological features either for the staging or for the treatment choices. Head and Neck Cancer (HNSCC) represents the 7th most common cancer worldwide and it is characterized by a relatively poor prognosis and detrimental effect of treatments on the quality of life of patients. Beyond surgery and radiotherapy, few systemic treatments are available, mainly represented by platinum-based chemotherapy or cetuximab. Immunotherapy is a new therapeutical strategy still limited to the palliative setting (recurrent not resectable or metastatic disease). The search for new biomarkers or possible new targetable mechanisms is meaningful especially in the clinical setting of HNSCC. In this thesis a focus will be given on the study of three possible pro-tumoral immune populations studied in HNSCC: the tumor associated neutrophils (TAN), intratumoral B-cells with a immunosuppressive phenotype and the CD8+ T-regs. Biostatistical and bioinformatical techniques are applied to summarize complex information derived from multiparametric clinical and immunological variables and to validate in-situ derived findings through gene expression data of public available datasets. Lastly, the second part of the thesis will take into account relevant clinical research projects, aimed at improving the precision oncology in HNSCC developing survival prediction models, comparing alternative oncological procedures, validating new classifiers or testing the use of novel clinical protocols as the use of immunnutrition

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Does it pay to optimize AUC?

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    The Area Under the ROC Curve (AUC) is an important model metric for evaluating binary classifiers, and many algorithms have been proposed to optimize AUC approximately. It raises the question of whether the generally insignificant gains observed by previous studies are due to inherent limitations of the metric or the inadequate quality of optimization. To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in R2\mathbb{R}^2, which runs in O(n+nlog(n+n))\mathcal{O}(n_+ n_- \log (n_+ n_-)) where n+n_+ and nn_- are the number of positive and negative samples respectively. Furthermore, it can be naturally extended to Rd\mathbb{R}^d in O((n+n)d1log(n+n))\mathcal{O}((n_+n_-)^{d-1}\log (n_+n_-)) by calling AUC-opt in lower-dimensional spaces recursively. We prove the problem is NP-complete when dd is not fixed, reducing from the \textit{open hemisphere problem}. Experiments show that compared with other methods, AUC-opt achieves statistically significant improvements on between 17 to 40 in R2\mathbb{R}^2 and between 4 to 42 in R3\mathbb{R}^3 of 50 t-SNE training datasets. However, generally the gain proves insignificant on most testing datasets compared to the best standard classifiers. Similar observations are found for nonlinear AUC methods under real-world datasets.Comment: 16 pages, AAA

    Statistical Arbitrage Trading on Electricity Markets Using Deep Reinforcement Learning

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    Statistical Arbitrage Trading on Electricity Markets Using Deep Reinforcement Learning

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    Automated design of local search algorithms for vehicle routing problems with time windows

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    Designing effective search algorithms for solving combinatorial optimisation problems presents a challenge for researchers due to the time-consuming experiments and experience required in decision-making. Automated algorithm design removes the heavy reliance on human experts and allows the exploration of new algorithm designs. This thesis systematically investigates machine learning for the automated design of new and generic local search algorithms, taking the vehicle routing problem with time windows as the testbed. The research starts by building AutoGCOP, a new general framework for the automated design of local search algorithms to optimise the composition of basic algorithmic components. Within the consistent AutoGCOP framework, the basic algorithmic components show satisfying performance for solving the VRPTW. Based on AutoGCOP, the thesis investigates the use of machine learning for automated algorithm composition by modelling the algorithm design task as different machine learning tasks, thus investigating different perspectives of learning in automated algorithm design. Based on AutoGCOP, the thesis first investigates online learning in automated algorithm design. Two learning models based on reinforcement learning and Markov chain are investigated to learn and enhance the compositions of algorithmic components towards automated algorithm design. The Markov chain model presents a superior performance in learning the compositions of algorithmic components during the search, demonstrating its effectiveness in designing new algorithms automatically. The thesis then investigates offline learning to learn the hidden knowledge of effective algorithmic compositions within AutoGCOP for automated algorithm design. The forecast of algorithmic components in the automated composition is defined as a sequence classification task. This new machine learning task is then solved by a Long Short-term Memory (LSTM) neural network which outperforms various conventional classifiers. Further analysis reveals that a Transformer network surpasses LSTM at learning from longer algorithmic compositions. The systematical analysis of algorithmic compositions reveals some key features for improving the prediction. To discover valuable knowledge in algorithm designs, the thesis applies sequential rule mining to effective algorithmic compositions collected based on AutoGCOP. Sequential rules of composing basic components are extracted and further analysed, presenting a superior performance of automatically composed local search algorithms for solving VRPTW. The extracted sequential rules also suggest the importance of considering the impact of algorithmic components on optimisation performance during automated composition, which provides new insights into algorithm design. The thesis gains valuable insights from various learning perspectives, enhancing the understanding towards automated algorithm design. Some directions for future work are present

    Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

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    Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
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