Machine Learning Methods for Medical and Biological Image Computing

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries in neuroscience. In this thesis, I propose computational methods using high-level features for automated analysis of brain images at different levels. At the brain function level, I develop a deep learning based framework for completing and integrating multi-modality neuroimaging data, which increases the diagnosis accuracy for Alzheimer’s disease. At the cellular level, I propose to use three dimensional convolutional neural networks (CNNs) for segmenting the volumetric neuronal images, which improves the performance of digital reconstruction of neuron structures. I design a novel CNN architecture such that the model training and testing image prediction can be implemented in an end-to-end manner. At the molecular level, I build a voxel CNN classifier to capture discriminative features of the input along three spatial dimensions, which facilitate the identification of secondary structures of proteins from electron microscopy im-ages. In order to classify genes specifically expressed in different brain cell-type, I propose to use invariant image feature descriptors to capture local gene expression information from cellular-resolution in situ hybridization images. I build image-level representations by applying regularized learning and vector quantization on generated image descriptors. The developed computational methods in this dissertation are evaluated using images from medical and biological experiments in comparison with baseline methods. Experimental results demonstrate that the developed representations, formulations, and algorithms are effective and efficient in learning from brain imaging data

A Hybrid Algorithm Based on Optimal Quadratic Spline Collocation and Parareal Deferred Correction for Parabolic PDEs

Parareal is a kind of time parallel numerical methods for time-dependent systems. In this paper, we consider a general linear parabolic PDE, use optimal quadratic spline collocation (QSC) method for the space discretization, and proceed with the parareal technique on the time domain. Meanwhile, deferred correction technique is also used to improve the accuracy during the iterations. In fact, the optimal QSC method is a correction of general QSC method. Along the temporal direction we embed the iterations of deferred correction into parareal to construct a hybrid method, parareal deferred correction (PDC) method. The error estimation is presented and the stability is analyzed. To save computational cost, we find out a simple way to balance the two kinds of iterations as much as possible. We also argue that the hybrid algorithm has better system efficiency and costs less running time. Numerical experiments by multicore computers are attached to exhibit the effectiveness of the hybrid algorithm

Automated Identification of Cell Type Specific Genes in the Mouse Brain by Image Computing of Expression Patterns

Background: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. Results: Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. Conclusions: Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain

Probucol reduces the cerebral edema area and infarction volume in rat cerebral infarction model via PI3K/Akt pathway

Purpose: To study the effects of probucol on rats with cerebral infarction through the phosphatidylinositol 3-hydroxy kinase (PI3K)/protein kinase B (Akt) pathway.Methods: Sprague-Dawley (SD) rats were divided into sham group (SO group, n = 7), model group (MO group, n = 7) and probucol group (PR group, n = 7). Infarction volume, messenger ribonucleic acid (mRNA), protein expressions of PI3K/Akt, neurological score, brain water content, degree of brain tissue lesions and neurological function score were determined.Results: Neurological score was 0, 2.54 ± 0.67 and 1.34 ± 0.21 points, in SO, O and PR groups, respectively. In turning angle test, neurological function score gradually rose at 24 h after cerebral infarction in PR and MO groups, compared with that in the SO group (p < 0.05), but significantly declined at 48 h in PR group compared with that in MO group (p < 0.05). Brain water content was lowest in the SO group but highest in MO group; it was significantly lower in PR group than that in MO group (p < 0.05). The mRNA and protein expressions of PI3K/Akt were highest in SO group and lowest in MO group; the expressions were higher in PR group than those in the MO group (p < 0.05).Conclusion: Probucol reduces the cerebral edema area and infarction volume by activating PI3K/Akt pathway, thereby exerting a significant therapeutic effect on rat model with cerebral infarction. Thus, this agent has the potential for use in the management of cerebral infarction

Deep convolutional neural networks for annotating gene expression patterns in the mouse brain

Abstract Background Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. Results We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Conclusions Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.http://deepblue.lib.umich.edu/bitstream/2027.42/134736/1/12859_2015_Article_553.pd

Deep convolutional neural networks for annotating gene expression patterns in the mouse brain

Abstract Background Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. Results We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Conclusions Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.http://deepblue.lib.umich.edu/bitstream/2027.42/111637/1/12859_2015_Article_553.pd

Deep Convolutional Neural Networks for Annotating Gene Expression Patterns in the Mouse Brain

Background: Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. Results: We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Conclusions: Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets

Model of strategy control for delayed panic spread in emergencies

In emergencies similar to virus spreading in an epidemic model, panic can spread in groups, which brings serious bad effects to society. To explore the transmission mechanism and decision-making behavior of panic, a government strategy was proposed in this paper to control the spread of panic. First, based on the SEIR epidemiological model, considering the delay effect between susceptible and exposed individuals and taking the infection rate of panic as a time-varying variable, a SEIR delayed panic spread model was established and the basic regeneration number of the proposed model was calculated. Second, the control strategy was expressed as a state delayed feedback and solved using the exact linearization method of nonlinear control system; the control law for the system was determined, and its stability was proven. The aim was to eradicate panic from the group so that the recovered group tracks the whole group asymptotically. Finally, we simulated the proposed strategy of controlling the spread of panic to illustrate our theoretical results

Epigenetic regulation of sulfur homeostasis in plants

Plants have evolved sophisticated mechanisms for adaptation to fluctuating availability of nutrients in soil. Such mechanisms are of importance for plants to maintain homeostasis of nutrient elements for their development and growth. The molecular mechanisms controlling the homeostasis of nutrient elements at the genetic level have been gradually revealed, including the identification of regulatory factors and transporters responding to nutrient stresses. Recent studies have suggested that such responses are controlled not only by genetic regulation but also by epigenetic regulation. In this review, we present recent studies on the involvement of DNA methylation, histone modifications, and non-coding RNA-mediated gene silencing in the regulation of sulfur homeostasis and the response to sulfur deficiency. We also discuss the potential effect of sulfur-containing metabolites such as S-adenosylmethionine on the maintenance of DNA and histone methylation