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hierarchical generative model

M Castellaro, G Rizzo, M Tonietto, M Veronese, F E Turkheimer, M A Chappell, A Bertoldo
In dynamic Positron Emission Tomography (PET) studies, compartmental models provide the richest information on the tracer kinetics of the tissue. Inverting such models at the voxel level is however quite challenging due to the low signal-to-noise ratio of the time activity curves. In this study, we propose the use of a Variational Bayesian (VB) approach to efficiently solve this issue and thus obtain robust quantitative parametric maps. VB was adapted to the non-uniform noise distribution of PET data. Moreover, we propose a novel hierarchical scheme to define the model parameter priors directly from the images in case such information are not available from the literature, as often happens with new PET tracers...
February 14, 2017: NeuroImage
Fan Zong, Lifang Wang
University scientific research ability is an important indicator to express the strength of universities. In this paper, the evaluation of university scientific research ability is investigated based on the output of sci-tech papers. Four university alliances from North America, UK, Australia, and China, are selected as the case study of the university scientific research evaluation. Data coming from Thomson Reuters InCites are collected to support the evaluation. The work has contributed new framework to the issue of university scientific research ability evaluation...
2017: PloS One
Wan-Shan Yang, Mel Campbell, Pei-Ching Chang
Small ubiquitin-like modifier (SUMO) modification of chromatin has profound effects on transcription regulation. By using Kaposi's sarcoma associated herpesvirus (KSHV) as a model, we recently demonstrated that epigenetic modification of viral chromatin by SUMO-2/3 is involved in regulating gene expression and viral reactivation. However, how this modification orchestrates transcription reprogramming through targeting histone modifying enzymes remains largely unknown. Here we show that JMJD2A, the first identified Jumonji C domain-containing histone demethylase, is the histone demethylase responsible for SUMO-2/3 enrichment on the KSHV genome during viral reactivation...
February 17, 2017: PLoS Pathogens
Tiago P Peixoto
A principled approach to characterize the hidden structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization...
January 2017: Physical Review. E
Tomoyasu Horikawa, Yukiyasu Kamitani
Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming...
2017: Frontiers in Computational Neuroscience
Yun Lou, Samson W Tu, Csongor Nyulas, Tania Tudorache, Robert J G Chalmers, Mark A Musen
The International Classification of Diseases (ICD) is the de facto standard international classification for mortality reporting and for many epidemiological, clinical, and financial use cases. The next version of ICD, ICD-11, will be submitted for approval by the World Health Assembly in 2018. Unlike previous versions of ICD, where coders mostly select single codes from pre-enumerated disease and disorder codes, ICD-11 coding will allow extensive use of multiple codes to give more detailed disease descriptions...
February 10, 2017: Journal of Biomedical Informatics
Catharina Melzer, Juliane von der Ohe, Hendrik Lehnert, Hendrik Ungefroren, Ralf Hass
BACKGROUND: The initiation and progression of malignant tumors is driven by distinct subsets of tumor-initiating or cancer stem-like cells (CSCs) which develop therapy/apoptosis resistance and self-renewal capacity. In order to be able to eradicate these CSCs with novel classes of anti-cancer therapeutics, a better understanding of their biology and clinically-relevant traits is mandatory. MAIN BODY: Several requirements and functions of a CSC niche physiology are combined with current concepts for CSC generation such as development in a hierarchical tumor model, by stochastic processes, or via a retrodifferentiation program...
February 1, 2017: Molecular Cancer
Fabrizio Marcucci, Pietro Ghezzi, Cristiano Rumio
Epithelial-mesenchymal transition (EMT) and cancer stem-like cells (CSC) are becoming highly relevant targets in anticancer drug discovery. A large body of evidence suggests that epithelial-mesenchymal transitioned tumor cells (EMT tumor cells) and CSCs have similar functions. There is also an overlap regarding the stimuli that can induce the generation of EMT tumor cells and CSCs. Moreover, direct evidence has been brought that EMT can give rise to CSCs. It is unclear however, whether EMT tumor cells should be considered CSCs or if they have to undergo further changes...
January 30, 2017: Molecular Cancer
Kayo Fujimoto, Tom A B Snijders, Thomas W Valente
In this study, we examined the dynamics of the perception of "dislike" ties (reputational dislike) among adolescents within the contexts of friendship, perceived popularity, substance use, and Facebook use. Survey data were collected from a longitudinal sample of 238 adolescents from the 11th and 12th grades in one California high school. We estimated stochastic actor-based network dynamic models, using reports of reputational dislike, friendships, and perceived popularity, to identify factors associated with the maintenance and generation reputational dislike ties...
January 2017: Social Networks
Michael H Kramer, Jean-Claude Farré, Koyel Mitra, Michael Ku Yu, Keiichiro Ono, Barry Demchak, Katherine Licon, Mitchell Flagg, Rama Balakrishnan, J Michael Cherry, Suresh Subramani, Trey Ideker
We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biological system. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions...
January 23, 2017: Molecular Cell
Yiming Yan, Fengjiao Gao, Shupei Deng, Nan Su
In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced...
January 24, 2017: Sensors
Zhen Zuo, Bing Shuai, Wang Gang, Xiao Liu, Xingxing Wang, Bing Wang, Yushi Chen
Deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information in sequential data, and they only require a limited number of network parameters...
March 29, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Ningning Zhao, Adrian Basarab, Denis Kouame, Jean-Yves Tourneret
This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map and all the hyperparameters are difficult to be expressed in closed form...
May 11, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Liangying Peng, Ling Chen, Xiaojie Wu, Haodong Guo, Gencai Chen
Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components...
August 31, 2016: IEEE Transactions on Bio-medical Engineering
Zhen Zuo, Bing Shuai, Gang Wang, Xiao Liu, Xingxing Wang, Bing Wang, Yushi Chen
Deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependence among different image regions. However, such dependence is very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information in sequential data, and they only require a limited number of network parameters...
July 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Fan Zhang, Patrick Flaherty
BACKGROUND: The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants. RESULTS: We propose a Bayesian statistical model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations...
January 19, 2017: BMC Bioinformatics
Akhil Chopra, Mina Georgieva, Gilberto Lopes, Chong Ming Yeo, Benjamin Haaland
BACKGROUND: To perform a comparative effectiveness analyses between enzalutamide and abiraterone acetate in both the pre-docetaxel and post-docetaxel settings based on published phase III randomized trials. METHODS: The primary measure of efficacy was the posterior probability that enzalutamide outperforms abiraterone acetate (AA) with prednisone in terms of overall survival (OS) on average. Indirect meta-estimates were generated from four randomized studies in the context of a Bayesian hierarchical model with study-specific efficacy estimates meta-analyzed on the log scale...
January 19, 2017: Prostate
Xing Li, Tao Liu, Lifeng Lin, Tie Song, Xiaolong Du, Hualiang Lin, Jianpeng Xiao, Jianfeng He, Liping Liu, Guanghu Zhu, Weilin Zeng, Lingchuan Guo, Zheng Cao, Wenjun Ma, Yonghui Zhang
BACKGROUND: An international spread of Zika virus (ZIKV) infection has attracted global attention in 2015. The infection also affected Guangdong province, which is located in southern China. Multiple factors, including frequent communication with South America and Southeast Asia, suitable climate (sub-tropical) for the habitat of Aedes species, may increase the risk of ZIKV disease transmission in this region. METHODS: An analytic hierarchy process (AHP) method was used to develop a semi-quantitative ZIKV risk assessment model...
January 13, 2017: BMC Infectious Diseases
Anil K Seth, Karl J Friston
We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects...
November 19, 2016: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Hana Ševčíková, Adrian E Raftery
We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids...
December 2016: Journal of Statistical Software
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