MicroRNA enrichment among short ‘ultraconserved’ sequences in insects

MicroRNAs are short (∼22 nt) regulatory RNA molecules that play key roles in metazoan development and have been implicated in human disease. First discovered in Caenorhabditis elegans, over 2500 microRNAs have been isolated in metazoans and plants; it has been estimated that there may be more than a thousand microRNA genes in the human genome alone. Motivated by the experimental observation of strong conservation of the microRNA let-7 among nearly all metazoans, we developed a novel methodology to characterize the class of such strongly conserved sequences: we identified a non-redundant set of all sequences 20 to 29 bases in length that are shared among three insects: fly, bee and mosquito. Among the few hundred sequences greater than 20 bases in length are close to 40% of the 78 confirmed fly microRNAs, along with other non-coding RNAs and coding sequence

Vision-based retargeting for endoscopic navigation

Endoscopy is a standard procedure for visualising the human gastrointestinal tract. With the advances in biophotonics, imaging techniques such as narrow band imaging, confocal laser endomicroscopy, and optical coherence tomography can be combined with normal endoscopy for assisting the early diagnosis of diseases, such as cancer. In the past decade, optical biopsy has emerged to be an effective tool for tissue analysis, allowing in vivo and in situ assessment of pathological sites with real-time feature-enhanced microscopic images. However, the non-invasive nature of optical biopsy leads to an intra-examination retargeting problem, which is associated with the difficulty of re-localising a biopsied site consistently throughout the whole examination. In addition to intra-examination retargeting, retargeting of a pathological site is even more challenging across examinations, due to tissue deformation and changing tissue morphologies and appearances. The purpose of this thesis is to address both the intra- and inter-examination retargeting problems associated with optical biopsy. We propose a novel vision-based framework for intra-examination retargeting. The proposed framework is based on combining visual tracking and detection with online learning of the appearance of the biopsied site. Furthermore, a novel cascaded detection approach based on random forests and structured support vector machines is developed to achieve efficient retargeting. To cater for reliable inter-examination retargeting, the solution provided in this thesis is achieved by solving an image retrieval problem, for which an online scene association approach is proposed to summarise an endoscopic video collected in the first examination into distinctive scenes. A hashing-based approach is then used to learn the intrinsic representations of these scenes, such that retargeting can be achieved in subsequent examinations by retrieving the relevant images using the learnt representations. For performance evaluation of the proposed frameworks, extensive phantom, ex vivo and in vivo experiments have been conducted, with results demonstrating the robustness and potential clinical values of the methods proposed.Open Acces

Effective and Secure Healthcare Machine Learning System with Explanations Based on High Quality Crowdsourcing Data

Affordable cloud computing technologies allow users to efficiently outsource, store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. With this exponential growth of the stored large scale clinical data and the growing need for personalized care, researchers are keen on developing data mining methodologies to learn efficient hidden patterns in such data. While studies have shown that those progresses can significantly improve the performance of various healthcare applications for clinical decision making and personalized medicine, the collected medical datasets are highly ambiguous and noisy. Thus, it is essential to develop a better tool for disease progression and survival rate predictions, where dataset needs to be cleaned before it is used for predictions and useful feature selection techniques need to be employed before prediction models can be constructed. In addition, having predictions without explanations prevent medical personnel and patients from adopting such healthcare deep learning models. Thus, any prediction models must come with some explanations. Finally, despite the efficiency of machine learning systems and their outstanding prediction performance, it is still a risk to reuse pre-trained models since most machine learning modules that are contributed and maintained by third parties lack proper checking to ensure that they are robust to various adversarial attacks. We need to design mechanisms for detection such attacks. In this thesis, we focus on addressing all the above issues: (i) Privacy Preserving Disease Treatment & Complication Prediction System (PDTCPS): A privacy-preserving disease treatment, complication prediction scheme (PDTCPS) is proposed, which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. (ii) Incentivizing High Quality Crowdsourcing Data For Disease Prediction: A new incentive model with individual rationality and platform profitability features is developed to encourage different hospitals to share high quality data so that better prediction models can be constructed. We also explore how data cleaning and feature selection techniques affect the performance of the prediction models. (iii) Explainable Deep Learning Based Medical Diagnostic System: A deep learning based medical diagnosis system (DL-MDS) is present which integrates heterogeneous medical data sources to produce better disease diagnosis with explanations for authorized users who submit their personalized health related queries. (iv) Attacks on RNN based Healthcare Learning Systems and Their Detection & Defense Mechanisms: Potential attacks on Recurrent Neural Network (RNN) based ML systems are identified and low-cost detection & defense schemes are designed to prevent such adversarial attacks. Finally, we conduct extensive experiments using both synthetic and real-world datasets to validate the feasibility and practicality of our proposed systems

Content And Multimedia Database Management Systems

A database management system is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The main characteristic of the ‘database approach’ is that it increases the value of data by its emphasis on data independence. DBMSs, and in particular those based on the relational data model, have been very successful at the management of administrative data in the business domain. This thesis has investigated data management in multimedia digital libraries, and its implications on the design of database management systems. The main problem of multimedia data management is providing access to the stored objects. The content structure of administrative data is easily represented in alphanumeric values. Thus, database technology has primarily focused on handling the objects’ logical structure. In the case of multimedia data, representation of content is far from trivial though, and not supported by current database management systems

How To Touch a Running System

The increasing importance of distributed and decentralized software architectures entails more and more attention for adaptive software. Obtaining adaptiveness, however, is a difficult task as the software design needs to foresee and cope with a variety of situations. Using reconfiguration of components facilitates this task, as the adaptivity is conducted on an architecture level instead of directly in the code. This results in a separation of concerns; the appropriate reconfiguration can be devised on a coarse level, while the implementation of the components can remain largely unaware of reconfiguration scenarios. We study reconfiguration in component frameworks based on formal theory. We first discuss programming with components, exemplified with the development of the cmc model checker. This highly efficient model checker is made of C++ components and serves as an example for component-based software development practice in general, and also provides insights into the principles of adaptivity. However, the component model focuses on high performance and is not geared towards using the structuring principle of components for controlled reconfiguration. We thus complement this highly optimized model by a message passing-based component model which takes reconfigurability to be its central principle. Supporting reconfiguration in a framework is about alleviating the programmer from caring about the peculiarities as much as possible. We utilize the formal description of the component model to provide an algorithm for reconfiguration that retains as much flexibility as possible, while avoiding most problems that arise due to concurrency. This algorithm is embedded in a general four-stage adaptivity model inspired by physical control loops. The reconfiguration is devised to work with stateful components, retaining their data and unprocessed messages. Reconfiguration plans, which are provided with a formal semantics, form the input of the reconfiguration algorithm. We show that the algorithm achieves perceived atomicity of the reconfiguration process for an important class of plans, i.e., the whole process of reconfiguration is perceived as one atomic step, while minimizing the use of blocking of components. We illustrate the applicability of our approach to reconfiguration by providing several examples like fault-tolerance and automated resource control

Salience Coding in the Basal Forebrain and the Heterogeneous Underpinnings Underlying Novelty Computations

Humans and animals are consistently learning from the environment by interacting with it and getting feedback from their actions. In the environment, some objects are more important than others, because they are associated with reward, uncertainty, surprise, or novelty etc. These objects are salient to the animal. Salient objects attract attention and orientation, increase arousal, facilitate learning and memory, and affect reinforcement learning and credit assignment. However, the neural basis to support these effects is still not fully understood.We first studied how the basal forebrain, one of the principal sources of modulation of the neocortex, encodes salience events. We found two types of neurons that process salient events in distinct manners: one with phasic burst activity to cues predicting salient events and one with ramping activity anticipating such events. Bursting neurons respond to reward itself and cues that predict the magnitude, probability, and timing of reward. However, they do not have a selective response to reward omission. Thus, bursting neurons signal surprise associated with external events, which is different from the reward prediction error signaled by the midbrain dopamine neurons. Furthermore, they discriminate fully expected novel visual objects from familiar objects and respond to object-sequence violations. In contrast, ramping neurons predict the timing of many salient, novel, and surprising events. Their ramping activity is highly sensitive to the subjects\u27 confidence in event timing and on average encodes the subjects\u27 surprise after unexpected events occur. These data suggest that the primate BF contains mechanisms to anticipate the timing of a diverse set of salient external events (via tonic ramping activity) and to rapidly deploy cognitive resources when these events occur (via phasic bursting activity). Then we sailed out to study one special salience signal – Novelty. The basal forebrain responds to novelty, but the neuronal mechanisms of novelty detection remain unclear. Prominent theories propose that novelty is either derived from the computation of recency or is a form of sensory surprise. Here, we used high-channel electrophysiology in primates to show that, in many prefrontal, temporal, and subcortical brain areas, object novelty sensitivity is related to both computations of recency (the sensitivity to how long ago a stimulus was experienced) and sensory surprise (violation of predictions about incoming sensory information). Also, we studied neuronal novelty-to-familiarity transformations during learning across many days and found a diversity of timescales in neurons\u27 learning rates and between-session forgetting rates within and across brain regions that is well suited to support flexible behavior and learning in response to novelty. These findings show that novelty sensitivity arises on multiple timescales across single neurons due to diverse related computations of sensory surprise and recency, and shed light on the logic and computational underpinnings of novelty detection in the primate brain

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

Dealing with clones in software : a practical approach from detection towards management

Despite the fact that duplicated fragments of code also called code clones are considered one of the prominent code smells that may exist in software, cloning is widely practiced in industrial development. The larger the system, the more people involved in its development and the more parts developed by different teams result in an increased possibility of having cloned code in the system. While there are particular benefits of code cloning in software development, research shows that it might be a source of various troubles in evolving software. Therefore, investigating and understanding clones in a software system is important to manage the clones efficiently. However, when the system is fairly large, it is challenging to identify and manage those clones properly. Among the various types of clones that may exist in software, research shows detection of near-miss clones where there might be minor to significant differences (e.g., renaming of identifiers and additions/deletions/modifications of statements) among the cloned fragments is costly in terms of time and memory. Thus, there is a great demand of state-of-the-art technologies in dealing with clones in software. Over the years, several tools have been developed to detect and visualize exact and similar clones. However, usually the tools are standalone and do not integrate well with a software developer's workflow. In this thesis, first, a study is presented on the effectiveness of a fingerprint based data similarity measurement technique named 'simhash' in detecting clones in large scale code-base. Based on the positive outcome of the study, a time efficient detection approach is proposed to find exact and near-miss clones in software, especially in large scale software systems. The novel detection approach has been made available as a highly configurable and fully fledged standalone clone detection tool named 'SimCad', which can be configured for detection of clones in both source code and non-source code based data. Second, we show a robust use of the clone detection approach studied earlier by assembling its detection service as a portable library named 'SimLib'. This library can provide tightly coupled (integrated) clone detection functionality to other applications as opposed to loosely coupled service provided by a typical standalone tool. Because of being highly configurable and easily extensible, this library allows the user to customize its clone detection process for detecting clones in data having diverse characteristics. We performed a user study to get some feedback on installation and use of the 'SimLib' API (Application Programming Interface) and to uncover its potential use as a third-party clone detection library. Third, we investigated on what tools and techniques are currently in use to detect and manage clones and understand their evolution. The goal was to find how those tools and techniques can be made available to a developer's own software development platform for convenient identification, tracking and management of clones in the software. Based on that, we developed a clone-aware software development platform named 'SimEclipse' to promote the practical use of code clone research and to provide better support for clone management in software. Finally, we evaluated 'SimEclipse' by conducting a user study on its effectiveness, usability and information management. We believe that both researchers and developers would enjoy and utilize the benefit of using these tools in different aspect of code clone research and manage cloned code in software systems