生醫分析系統之語意整合

[[abstract]]這計畫提議建立一個知識系統，允許生物醫學的研究人員透過以自然語言查詢方 式，綜合查詢複雜的生物資訊數據及影像訊息。我們的數據庫的目標是使數據的輸 入更有效率的，更有組織性，容易取回，及使操作和綜合變得容易。此系統以阿茲海 默症作為研究的對象。這一個知識系統與傳統知識系統的基本的區別在於它支援複雜 的數據組織和一個強大的查詢界面。 SemanticObjects 是由美國加州大學Irvine 分校和日本NEC 共同開發的一個物件 相關的平台，目的是為建造一物件知識系統。它允許使用者有效的組織及儲存生物學 模式和數據成階層式的複雜物件。使用者可利用結構性的自然語言來查詢及利用此知 識系統的數據。 最後，我們將迅速地把這個以SemanticObjects 為主的知識系統成為網站應用。這 使其它的研究人員可分享及獲得是項研究的結果。 我們提議的系統由以下的數個模組組成，a) 文字採礦模組，b) microarry/SNP 模 組，c) 基因網路模組，d)影像模組和e)實驗模組。 This proposal suggests building a knowledge system that allows biomedical researchers to synthesize complex bioinformatics information and images data via natural language query. The goal of our database is to facilitate efficient data entry, organization, retrieval, manipulation and integration. The Alzheimer』s Disease was chosen as our study case. A fundamental distinction of the biological database addressed in this research and the others is that it supports both complex data organization and a powerful querying facility. SemanticObjects is an object-relational platform that has been jointly developed by University of California, Irvine and NEC Soft, Japan as a tool for building object knowledge systems. It allows users to efficiently organize and store biological models and data as complex objects that are hierarchically structured. User can query and manipulate the data in Structured Natural Language (SNL). Finally, we will rapidly deploy this SemanticObjects database into a web application. This makes it easy for the research community to share the results obtained from proposed research. Our proposed system consists of: a) a text mining module, b) a microarry/SNP module, c) a gene network module, d) an image module, and e) a web laboratory module

A framework for safe composition of heterogeneous SOA services in a pervasive computing environment with resource constraints

The Service Oriented Computing (SOC) paradigm, defines services as software artifacts whose implementations are separated from their specifications. Application developers rely on services to simplify the design, reduce the development time and cost. Within the SOC paradigm, different Service Oriented Architectures (SOAs) have been developed. These different SOAs provide platform independence, programming-language independence, defined standards, and network support. Even when different SOAs follow the same SOC principles, in practice it is difficult to compose services from heterogeneous architectures. Automatic the process of composition of services from heterogeneous SOAs is not a trivial task. Current composition tools usually focus on a single SOA, while others do not provide mechanisms for ensuring safety of composite services and their interactions. Given that some services might perform critical operations or manage sensitive data, defining safety for services and checking for compliance is crucial. This work proposes and workflow specification language for composite services that is SOA-independent. It also presents a framework for automatic composition of services of heterogeneous SOAs, supporting web services (WS) and OSGi services as an example. It integrates formal software analysis methods to ensure the safety of composite services and their interactions. Experiments are conducted to study the performance of the composite service generated automatically by the framework with composite services using current composition methods. We use as an example a smart home composite service for the management of medicines, deployed in a regular and in a resource-constrained network environment

Advances towards reliable identification and concentration determination of rare cells in peripheral blood

Through further development, integration and validation of micro-nano-bio and biophotonics systems FP7 CanDo is developing an instrument that will permit highly reproducible and reliable identification and concentration determination of rare cells in peripheral blood for two key societal challenges, early and low cost anti-cancer drug efficacy determination and cancer diagnosis/monitoring. A cellular link between the primary malignant tumour and the peripheral metastases, responsible for 90% of cancerrelated deaths, has been established in the form of circulating tumour cells (CTCs) in peripheral blood. Furthermore, the relatively short survival time of CTCs in peripheral blood means that their detection is indicative of tumour progression thereby providing in addition to a prognostic value an evaluation of therapeutic efficacy and early recognition of tumour progression in theranostics. In cancer patients however blood concentrations are very low (=1 CTC/1E9 cells) and current detection strategies are too insensitive, limiting use to prognosis of only those with advanced metastatic cancer. Similarly, problems occur in therapeutics with anti-cancer drug development leading to lengthy and costly trials often preventing access to market. The novel cell separation/Raman analysis technologies plus nucleic acid based molecular characterization of the CanDo platform will provide an accurate CTC count with high throughput and high yield meeting both key societal challenges. Being beyond the state of art it will lead to substantial share gains not just in the high end markets of drug discovery and cancer diagnostics but due to modular technologies also in others. Here we present preliminary DNA hybridization sensing results

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

The Daily Gamecock, THURSDAY, APRIL 24, 2008

https://scholarcommons.sc.edu/gamecock_2008_apr/1017/thumbnail.jp

Modulation of lipid metabolism for protein production

The present disclosure features methods and compositions for modulating lipid metabolism to achieve improved production and quality of recombinant products, such as next generation biologies. Modulation of lipid metabolism as described herein includes, for example, introducing a lipid metabolism modulator described herein to a cell or a cell-free system. Also encompassed by the present disclosure are engineered cells with improved production capacity and improved product quality, methods for engineering such cells, and preparations and mixtures comprising the products from such cells

The hidden truth: A sociological history of lie detection.

Drawing on Foucault and the sociology of science and technology, this thesis traces the curious attempt that has been made over the last century to capture one of the most elusive social acts - the lie. This endeavour was made possible by the emergence of the human sciences, whose guiding belief was that the subject's inner life could be made apparent by means of physiological measurements and therefore be controlled. My thesis follows the development of the 'embodiment' of the lie within early and recent psychology as a means of detecting the subject's guilt. It examines the disconnection of lie detection from its academic origins and its re-positioning within criminal investigation which engenders the development of polygraphy as a separate profession. In this, it elaborates on the special roles played by instruments in lie detection practices - the 'lie detector' and the 'polygraph' - and analyses changing epistemological aims and models of 'scientific' expertise. In accounting for its contested status, the latter analysis is connected to an evaluation of the continuous exclusion of lie detection as scientific evidence from the courts. The thesis examines the changing functions of the polygraph examination in systems of social control as their logic moves from reform to increased containment and control: from a confessional technique mediating the efficient processing of a delinquent population from the 1920s, to a disciplinary technique controlling employee behaviour from the 1930s. In recent years it has become a 'truth facilitator' in the management and containment of the monstrous individual: the sex offender. In a broader consideration of the power/knowledge mechanism of lie detection, the thesis applies Foucault's notion of grotesque knowledge, arguing that the ensemble of the lie detector/polygraph and psychological expert/interrogator is Ubuesque as it implements an absolute power in the 'diagnosis' of the lie, which is disqualified at the moment of its verification through confession. The thesis demonstrates how Foucauldian analyses and the sociology of science can be fruitfully combined to comprehensively explain both the dynamics of contested expert knowledges and the ways in which psychological techniques operate in shaping the subject. Having traced the emergence of the lie as an object of knowledge and intervention, the thesis concludes by providing directions in an historically informed sociology of the lie

Polyfunctionalised pyrimidines and pyrazines from perhalogenated precursors

Chapter 1 introduces the modem pharmaceutical industry in terms of the drug discovery process leading into a discussion of the relevance of heterocyclic compounds with particular focus on the synthesis of multifunctional pyrimidines and pyrazines. An introduction into organofluorine chemistry is included followed by a review of the literature on 5-chloro-trifluoropyrimidine, tetrafluoropyrimidine and tetrafluoropyrazine. Chapter 2 describes a study of the reactivity of 5-chlorotrifluoropyrimidine with mono- and difunctional-nucleophiles. This research demonstrates the former are not selective and in the latter the 5-position chlorine atom is inert to nucleophilic aromatic substitution and cross-coupling methodologies. Chapter 3 explores the reactivity of tetrafluoropyrimidine with nitrogen, sulphur and oxygen containing nucleophiles and describes the development of a methodology for the synthesis of multisubstituted pyrimidines by establishing the regioselectivities of such processes. Chapter 4 investigates the reactivity of tetrafluoropyrimidine with difunctional nucleophiles. This study indicated it was not possible to synthesise [5,6]-ring fused systems and that in some cases dimers were formed owing to the 5-position fluorine atom being inactive substitution. Chapter 5 discusses the use of tetrafluoropyrazine in the syntheses of [5,6] ring-fused systems. The reactivity of the system towards MiV-dinucleophiles and C,0-dinucleophiles was investigated. Further functionalisations by nucleophilic aromatic substitution of the remaining fluorine atoms with nitrogen and oxygen nucleophiles are also discussed. Chapter 6 contains the experimental data for Chapters 2 to 5

Biomedical entities recognition in Spanish combining word embeddings

El reconocimiento de entidades con nombre (NER) es una tarea importante en el campo del Procesamiento del Lenguaje Natural que se utiliza para extraer conocimiento significativo de los documentos textuales. El objetivo de NER es identificar trozos de texto que se refieran a entidades específicas. En esta tesis pretendemos abordar la tarea de NER en el dominio biomédico y en español. En este dominio las entidades pueden referirse a nombres de fármacos, síntomas y enfermedades y ofrecen un conocimiento valioso a los expertos sanitarios. Para ello, proponemos un modelo basado en redes neuronales y empleamos una combinación de word embeddings. Además, nosotros generamos unos nuevos embeddings específicos del dominio y del idioma para comprobar su eficacia. Finalmente, demostramos que la combinación de diferentes word embeddings como entrada a la red neuronal mejora los resultados del estado de la cuestión en los escenarios aplicados.Named Entity Recognition (NER) is an important task in the field of Natural Language Processing that is used to extract meaningful knowledge from textual documents. The goal of NER is to identify text fragments that refer to specific entities. In this thesis we aim to address the task of NER in the Spanish biomedical domain. In this domain entities can refer to drug, symptom and disease names and offer valuable knowledge to health experts. For this purpose, we propose a model based on neural networks and employ a combination of word embeddings. In addition, we generate new domain- and language-specific embeddings to test their effectiveness. Finally, we show that the combination of different word embeddings as input to the neural network improves the state-of-the-art results in the applied scenarios.Tesis Univ. Jaén. Departamento de Informática. Leída el 22 abril de 2021