A NOVEL OCCLUSION SIGN LANGUAGE RECOGNITION

[[abstract]]Sign language plays an important role in communicate with changers that hearing improved. However, the sign language in many countries and areas different and auto recognition system became the research way in recent year. In this paper, we devise a novel method for occlusion processing in Taiwan Sign Language recognition system. Our method employs adxl345 and Kinect to extract the feature of signer. Then the features are regulated by the dictionary of sparse coding. In final, the HMM model and result signs are recognized from the features that corrected by our method. In experimental result, we present the data that our employ. Then we describe closing test result and future work.[[sponsorship]]National Taipei University[[conferencetype]]國際[[conferencedate]]20150718~20150719[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Supervised classification of combined copy number and gene expression data

Summary In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification

Mesenchymal Stromal Cell secretome is affected by tissue source and donor age.

Variation in Mesenchymal Stromal Cell (MSC) function depending on their origin is problematic, as it may confound clinical outcomes of MSC therapy. Current evidence suggests that the therapeutic benefits of MSCs are attributed to secretion of biologically active factors (secretome). However, the effect of donor characteristics on the MSC secretome remains largely unknown. Here, we examined the influence of donor age, sex and tissue source, on the protein profile of the equine MSC secretome. We used dynamic metabolic labelling with stable isotopes combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify secreted proteins in MSC conditioned media (CM). Seventy proteins were classified as classically-secreted based on the rate of label incorporation into newly synthesised proteins released into the extracellular space. Next, we analysed CM of bone marrow- (n = 14) and adipose-derived MSCs (n = 16) with label-free LC-MS/MS. Clustering analysis of 314 proteins detected across all samples identified tissue source as the main factor driving variability in MSC CM proteomes. Linear modelling applied to the subset of 70 secreted proteins identified tissue-related difference in the abundance of 23 proteins. There was an age-related decrease in the abundance of CTHRC1 and LOX, further validated with orthogonal techniques. Due to the lack of flow cytometry characterisation of MSC surface markers, the analysis could not account for the potential effect of cell population heterogeneity. This study provides evidence that tissue source and donor age contribute to differences in the protein composition of MSC secretomes which may influence the effects of MSC therapy

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Kinect-Based Tennis Swing Motion Analysis And Its Application

[[abstract]]在本論文中利用微軟推出的 Kinect，提出一套以 Kinect 為基礎的網球揮拍動作分析與應用。我們透過 OpenNI 擷取的骨架座標，建立一套經由三維直角座標的座標系，並使用身體部分的關節點對身體軀幹做投影，視為揮拍動作辨識之特徵值。針對網球揮TWELF 2014 拍動作之軌跡，並以此特徵值透過動態時間校正(Dynamic Time Warping)，可以算出時間序列的最佳化，以軌跡路徑做相似度的比較分辨各種揮拍動作，並透過角度判斷式來評估揮拍動作。[[sponsorship]]國立臺灣師範大學[[conferencetype]]國際[[conferencedate]]20141113-20141114[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺灣, 台北

Deciphering Leukaemogenic Mechanisms through System-Scale Analysis of Single-Cell RNA Sequencing Data

Haematopoietic stem cells are responsible for producing and sustaining the diverse array of cell types present in the adult blood system. This complex process requires the strict regulation of haematopoietic fate decisions and differentiation trajectories in order to maintain a healthy state. Haematological malignancies such as leukaemia are associated with various perturbations that disrupt this regulation and drive aberrant cell fate decisions, leading to disease. Much of this dysregulation is proposed to occur at the transcriptional level, and recent technological advancements in single-cell sequencing have made it possible to study the transcriptional effects of leukaemic perturbations at the scale of individual haematopoietic stem and progenitor cells. However, the mechanisms through which specific perturbations lead to dysregulation of the blood system remain poorly understood. The primary aim of this work was to build an integrative computational framework for the analysis and comparison of leukaemic perturbations of the murine blood system as measured by single-cell RNA sequencing. Presented in Chapter 3, this framework aims to dissect the perturbation response across different scales – from individual genes to specific progenitor cell types to the entire blood system – and allow informative comparisons to be made about the similarities and differences between several perturbations. In total, eight genetic perturbations known to associate with leukaemia were analysed, resulting in novel biological insights concerning the behaviour of coordinated gene modules and the cellular abundance shifts driven by them. As many leukaemic drivers act directly upon the most immature long-term haematopoietic stem cells, a highly targeted analysis of these cells was performed across the leukaemic perturbations. In Chapter 4 a novel computational pipeline was built to link FACS-sorted cell populations and single-cell transcriptional landscapes. Using this, the cellular and molecular responses of the perturbations were investigated, resulting in several novel hypotheses. For example, the data suggests that many leukaemic perturbations gain a competitive advantage against wild-type cells by pushing their MPP1 cells into more active states. Additionally the data suggests that increases in the transcriptional variability of blood stem cells is associated with pro-erythroid fate decision shifts and vice-versa. Many different types of haematopoietic perturbations exist and can drive disease progression in the blood system. Chapter 5 focuses on single-cell RNA sequencing data from three further perturbations in various settings, including an infection model of Malaria and a model susceptible to endogenous DNA damage by aldehydes. These analyses have driven and validated bodies of experimental work, and comparing them to the previously described perturbation models highlighted both conserved changes and differences in the response of the haematopoietic system across different perturbation settings. The final project aimed to improve upon current computational methods for cellular trajectory inference from single-cell data. Whilst high-throughput experiments allow for the sequencing of large cell numbers, this is balanced by the sparse and noisy nature of the returned data. Current methods perform poorly on such datasets and either cannot deal with large cell numbers or cannot extract enough relevant signal from sparse count matrices. A new computational tool was designed to work best on these large, sparse datasets, and infer the most likely cellular trajectories through snapshot sequencing data using an iterative process. In Chapter 6 this algorithm was applied to different systems including adult haematopoiesis, and was compared to state-of-the-art methods. Overall, this thesis has investigated the transcriptional consequences of numerous preleukaemic perturbations on the haematopoietic stem and progenitor cell compartment at the single-cell level. New methods have been built for integration of single-cell perturbation experiments and their analysis across different biological scales. This has revealed novel biological insights regarding the mechanisms underpinning leukaemic transformation of the blood system

Analysis of similarity among arterial blood pressure waveforms

Dissertação de mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2017Time series are an important class of data objects that arise from various sources and their analysis typically involves huge amounts of information requiring usage of data mining techniques. Measuring similarity in long time series plays an important role in searching for similar patterns, classification, clustering, prediction and knowledge discovery. In clinical context any estimation of future values based on its past values can be useful in disease prognosis. In this thesis different methods of measuring similarity between time series of arterial blood pressure (ABP) signals are described and experimental results are provided. To classify an ABP record within a particular diseases’ class (a cluster), the typical procedure is the prior determination of the similarity of the ABP record with a reference signal characterizing a cardiovascular disease (CVD) and then identifying the strength of that similarity to enable a true positive classification of the illness (or not). Several methods of measuring similarity among time-series are referred in literature, the most commonly employed one were object of this research. Since the goal was the application of the similarity results to perform clustering of the ABP signals, similarity methods were investigated particularly in what concerns their performance when proceeding for the clustering following step. So, this thesis reports the usage of seven different similarity methods, five working in the time domain and two in the transform-based domain, and explores their usage when clustering by Partitioning Around Medoids is implemented. As data records are noisy and signals suffer from variations due to other sources than heart, six types of variations were imposed on the reference signal and 20 degrees of possible variations were tested. The time series considered on this study were 10 seconds length, referring to healthy, electrocardiogram (ECG) long term ST’s, atrial fibrillation and a collection of diagnostic ECGs. Three clusters were considered, each involving healthy and pathological records, in different proportions. Results demonstrate that the Discrete Wavelet Transform using a Haar wavelet decomposition with the Karhunen-Loève transforms, besides reducing the computational processing load enables clustering with an accuracy between 76% and 84% among the three diagnostic classes considered. The organization of this thesis is as follows. A short representation of Time-series is in chapter.1. A brief description of various similarity methods and clustering methods are given in chapters 2 and 3. Experiments performed and results obtained are described in chapter 4. Finally, the conclusion of this work is presented in chapter 5 where the list of publications resultant from this thesis is included.As séries temporais são uma classe importante de objetos de dados que surgem de várias fontes e a sua análise geralmente envolve enormes quantidades de informações que exigem o uso de técnicas de mineração de dados. A medição da similaridade em séries de longo prazo desempenha um papel importante na busca por padrões semelhantes, classificação, agrupamento, previsão e descoberta de conhecimento. No contexto clínico qualquer estimativa de valores futuros baseada em seus valores passados pode ser útil no prognóstico de doenças. Nesta tese são descritos diferentes métodos para medir a similaridade entre séries temporais de sinais de pressão arterial (ABP) e são fornecidos resultados experimentais. Para classificar um registro ABP dentro de uma classe de doenças particulares (um cluster), o procedimento típico é a determinação prévia da similaridade do registro ABP com um sinal de referência caracterizando uma doença cardiovascular (CVD) e depois, identificando a força dessa similaridade, possibilita-se uma classificação verdadeira positiva da doença (ou não). Vários métodos de mensuração da similaridade entre séries temporais são referidos na literatura, sendo os mais comumente empregados objeto desta pesquisa. Uma vez que o objetivo foi a aplicação dos resultados de similaridade para realizar agrupamento dos sinais ABP (clustering), vários métodos de similaridade foram investigados particularmente no que diz respeito ao seu desempenho ao prosseguir para a etapa seguinte de agrupamento de acordo com a patologia. Assim, esta tese relata o uso de sete métodos de similaridade diferentes, cinco trabalhando no domínio do tempo e dois no domínio baseado em transformação, e explora o seu uso quando o clustering pelo método de Partitioning Around Medoids é implementado. Como os registros de dados são ruidosos e os sinais sofrem de variações devido a outras fontes além das do coração, seis tipos de variações foram impostas ao sinal de referência e foram testados 20 graus de possíveis variações. As séries temporais consideradas neste estudo foram de 10 segundos de duração, referindo-se a eletrocardiogramas (ECG) saudáveis, a sinais de ECG com segmentos ST de longo prazo, a ECG’s relativos a fibrilação atrial e ainda a uma coleção de ECGs de diagnóstico. Foram considerados três agrupamentos, cada um envolvendo registros saudáveis e patológicos, em diferentes proporções. Os resultados demonstram que a Transformação de Wavelet Discreta usando uma decomposição de wavelet de Haar com as transformações de Karhunen-Loève, além de reduzir a carga de processamento computacional, possibilita o agrupamento com uma precisão entre 76% e 84% entre as três classes diagnósticas consideradas. A organização desta tese é a seguinte. Uma breve representação de séries temporais está incluída no capítulo 1. Uma breve descrição de vários métodos de similaridade e métodos de agrupamento são apresentados nos capítulos 2 e 3. As experiências realizadas e os resultados obtidos são descritos no capítulo 4. Finalmente, a conclusão deste trabalho é apresentada no capítulo 5, onde a lista de publicações resultantes desta tese está incluído

Computational analysis of multi-omic data for the elucidation of molecular mechanisms of neuroblastoma

Doctor ScientiaeNeuroblastoma is the most common extracranial solid tumor in childhood. The survival rates of patients with neuroblastoma, especially those in the high-risk category, are still low despite varied therapies. The detailed understanding of the molecular mechanisms underlying the pathogenesis of neuroblastoma is essential to develop better therapeutics and improve the poor survival rates. This study provides a multi-omic analysis of neuroblastoma datasets from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) neuroblastoma project and the Gene Expression Omnibus (GEO) data portals to better understand the molecular mechanisms of neuroblastoma