Triebel-Lizorkin Spaces Estimates for Evolution Equations with Structure Dissipation

This work is concerned with the long time decay estimates of the generalized heat equations and the generalized wave equations in the homogeneous Triebel-Lizorkin spaces. We first extend the known results for the generalized heat equations in the real Hardy spaces. We also extend the known results for the generalized wave equations with structure dissipation in the real Hardy spaces. The main tools employed are the decomposition of the unit, duality property in Triebel-Lizorkin spaces and the multiplier theorems in different function spaces such as Lebesgue spaces, real Hardy spaces and Triebel-Lizorkin spaces

Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process

Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.Comment: The paper has been accepted to the International Conference on Robotics and Automation (ICRA2018

Biological and Practical Implications of Genome-Wide Association Study of Schizophrenia Using Bayesian Variable Selection

Genome-wide association studies (GWAS) have identified over 100 loci associated with schizophrenia. Most of these studies test genetic variants for association one at a time. In this study, we performed GWAS of the molecular genetics of schizophrenia (MGS) dataset with 5334 subjects using multivariate Bayesian variable selection (BVS) method Posterior Inference via Model Averaging and Subset Selection (piMASS) and compared our results with the previous univariate analysis of the MGS dataset. We showed that piMASS can improve the power of detecting schizophrenia-associated SNPs, potentially leading to new discoveries from existing data without increasing the sample size. We tested SNPs in groups to allow for local additive effects and used permutation test to determine statistical significance in order to compare our results with univariate method. The previous univariate analysis of the MGS dataset revealed no genome-wide significant loci. Using the same dataset, we identified a single region that exceeded the genome-wide significance. The result was replicated using an independent Swedish Schizophrenia Case–Control Study (SSCCS) dataset. Based on the SZGR 2.0 database we found 63 SNPs from the best performing regions that are mapped to 27 genes known to be associated with schizophrenia. Overall, we demonstrated that piMASS could discover association signals that otherwise would need a much larger sample size. Our study has important implication that reanalyzing published datasets with BVS methods like piMASS might have more power to discover new risk variants for many diseases without new sample collection, ascertainment, and genotyping

RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia

Background While genome-wide association studies identified some promising candidates for schizophrenia, the majority of risk genes remained unknown. We were interested in testing whether integration gene expression and other functional information could facilitate the identification of susceptibility genes and related biological pathways. Results We conducted high throughput sequencing analyses to evaluate mRNA expression in blood samples isolated from 3 schizophrenia patients and 3 healthy controls. We also conducted pooled sequencing of 10 schizophrenic patients and matched controls. Differentially expressed genes were identified by t-test. In the individually sequenced dataset, we identified 198 genes differentially expressed between cases and controls, of them 19 had been verified by the pooled sequencing dataset and 21 reached nominal significance in gene-based association analyses of a genome wide association dataset. Pathway analysis of these differentially expressed genes revealed that they were highly enriched in the immune related pathways. Two genes, S100A8 and TYROBP, had consistent changes in expression in both individual and pooled sequencing datasets and were nominally significant in gene-based association analysis. Conclusions Integration of gene expression and pathway analyses with genome-wide association may be an efficient approach to identify risk genes for schizophrenia

Vlasov-Poisson equation in weighted Sobolev space Wm,p(w)W^{m, p}(w)

In this paper, we are concerned about the well-posedness of Vlasov-Poisson equation near vaccum in weighted Sobolev space $W^{m, p}(w).$ The most difficult part comes from estimates of the electronic term $\nabla_{x}\phi.$ To overcome this difficulty, we establish the $L^p$-$L^q$ estimates of the electronic term $\nabla_{x}\phi;$ some weight is introduced as well to obtain the off-diagonal estimate. The weight is also useful when it comes to control the higher-order derivative term

Genetic Risks to Nicotine Dependence Predict Negative Mood and Affect in Current Non-Smokers

Nicotine is the psychoactive agent involved in nicotine dependence. However, nicotine as a drug and its effects on human psychology are largely under-investigated in genetic studies. In this study, we recruited 208 current non-smokers to evaluate the effect of nicotine and its relationship to genetic risks to nicotine dependence. Exploratory and confirmatory factor analyses, as well as measurement invariance testing, were conducted to evaluate the latent factor structures of the POMS, PANAS and DEN questionnaires across 3 nicotine doses. Structural models were used to examine the effects of nicotine and their relationship to genetic risks of nicotine dependence. We found that nicotine administration led to the change of both measurement construct and factor means, indicating the causal effect of nicotine on the psychological responses. The genotypes of rs588765 predicted the scores of the DEN Confused and Dizzy factors (p = 0.0003 and 0.001 respectively) and rs16969968 and rs588765 were associated with the PANAS Nervous factor (p = 0.006 and 0.007 respectively). Our study suggested that genetic risk of nicotine dependence is associated with acute psychological responses. The integration of psychometric analyses and dose effects could be a powerful approach for genetic study of nicotine dependence

Genetic Risks to Nicotine Dependence Predict Negative Mood and Affect in Current Non-Smokers

Nicotine is the psychoactive agent involved in nicotine dependence. However, nicotine as a drug and its effects on human psychology are largely under-investigated in genetic studies. In this study, we recruited 208 current non-smokers to evaluate the effect of nicotine and its relationship to genetic risks to nicotine dependence. Exploratory and confirmatory factor analyses, as well as measurement invariance testing, were conducted to evaluate the latent factor structures of the POMS, PANAS and DEN questionnaires across 3 nicotine doses. Structural models were used to examine the effects of nicotine and their relationship to genetic risks of nicotine dependence. We found that nicotine administration led to the change of both measurement construct and factor means, indicating the causal effect of nicotine on the psychological responses. The genotypes of rs588765 predicted the scores of the DEN Confused and Dizzy factors (p = 0.0003 and 0.001 respectively) and rs16969968 and rs588765 were associated with the PANAS Nervous factor (p = 0.006 and 0.007 respectively). Our study suggested that genetic risk of nicotine dependence is associated with acute psychological responses. The integration of psychometric analyses and dose effects could be a powerful approach for genetic study of nicotine dependence

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

Abstract Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.This study was supported in part by grants from the NHLBI 5U19HL065962 and the NCI R01CA141307. ML is supported by the NLM training grant 3T15LM007450-08S1. JS is partially supported by the 2010 NARSAD Young Investigator Award. ZZ is partially supported by the 2009 NARSAD Maltz Investigator Award. MM is supported by a Veterans Administration HSR&D Career Development Award (CDA-08-020)

Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research

Artificial Image Objects for Classification of Breast Cancer Biomarkers With Transcriptome Sequencing Data and Convolutional Neural Network Algorithms

Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results: With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 ± 0.023 and 0.891 ± 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 ± 0.012, and the weighted average of AUC was 0.931 ± 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 ± 0.037 and 0.883 ± 0.016, and that for NHG were 0.764 ± 0.052 and 0.882 ± 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions: We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients