1,236 research outputs found

    Investigating the impact of lung cancer cell-of-origin on tumour metabolic phenotype and heterogeneity

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    Non-small-cell lung cancer has been described as highly heterogenous which results in different metabolic phenotypes. There are multiple factors which contribute to this heterogeneity, one of which is the tumour cell-of-origin. In the lung, there are five cell types reported to be cells-of-origin: alveolar epithelial type 2, club, basal, neuroendocrine and bronchioalveolar stem cells. This project focuses on the interaction between the cell-of-origin and the metabolic phenotype of lung cancer, and we aim to assess the contribution of the cell-of-origin to lung cancer metabolic resultant phenotype and heterogeneity. To accomplish this, we have established two complementary model systems, one in vitro and one in vivo. In our in vitro model, we isolated specific lung cell types, including AT2 cells, basal cells, and club cells, utilising their unique cell surface markers. By introducing oncogenic KRAS mutations and deleting the P53 gene, we are creating lineage-restricted organoids. These organoids will serve as valuable tools for characterizing the metabolic aspects of tumours arising from different cell-of-origin backgrounds within an in vitro setting. In our in vivo model, we induced NSCLC tumours in mice with genetic modifications using viral vectors, namely Ad5-mSPC-Cre, Ad5-CC10-Cre, and Ad5- bk5-Cre. These vectors are selectively expressed in AT2, club, and basal cells, respectively. To ensure the validity of our comparisons, we have carefully monitored tumour growth dynamics and burden in these mouse models. Our comprehensive analysis has revealed three distinct transcriptomic subtypes (S1, S2, and Acetate) within these NSCLC tumours. Notably, S1 and Acetate subtypes are enriched in tumours originating from specific cell types. Positron emission tomography (PET) imaging has unveiled metabolic variations, with S1 tumours displaying heightened [18F]FDG uptake and the Acetate subtype exhibiting increased [11C]acetate uptake. Furthermore, our multi-omics approach, encompassing transcriptomics, proteomics, and metabolomics, has exposed disparities in critical metabolic pathways, such as glycolysis, hypoxia response, and apoptosis. In summary, our research provides a comprehensive examination of the metabolic heterogeneity of NSCLC based on the cell-of-origin independently of genomic alterations

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Explainable temporal data mining techniques to support the prediction task in Medicine

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    In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset

    Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes

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    Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections. In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art. The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks. Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets. Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy

    Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings

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    The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Neural correlates of cognitive ability and visuo-motor speed: validation of IDoCT on UK Biobank Data

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    Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuomotor speeds, testing device latencies and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuomotor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuomotor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuomotor vs. language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities and brain-wide associations
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