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etc. Given a low resolution image, it aims to reconstruct a high resolution
image. The problem is ill-posed since there can be more than one high resolution
image corresponding to the same low-resolution image. To address this problem, a
number of machine learning-based approaches have been proposed.
In this dissertation, I present my works on single image super-resolution (SISR)
and accelerated magnetic resonance imaging (MRI) (a.k.a. super-resolution on MR
images), followed by the investigation on transfer learning for accelerated MRI reconstruction.
For the SISR, a dictionary-based approach and two reconstruction based
approaches are presented. To be precise, a convex dictionary learning (CDL)
algorithm is proposed by constraining the dictionary atoms to be formed by nonnegative
linear combination of the training data, which is a natural, desired property.
Also, two reconstruction-based single methods are presented, which make use
of (i)the joint regularization, where a group-residual-based regularization (GRR) and
a ridge-regression-based regularization (3R) are combined; (ii)the collaborative representation
and non-local self-similarity. After that, two deep learning approaches
are proposed, aiming at reconstructing high-quality images from accelerated MRI
acquisition. Residual Dense Block (RDB) and feedback connection are introduced
in the proposed models. In the last chapter, the feasibility of transfer learning for
accelerated MRI reconstruction is discussed.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database—the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD.
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.
Background: Healthy individuals on the lower end of the insulin sensitivity spectrum also have a reduced gene expression response to exercise for specific genes. The goal of this study was to determine the relationship between insulin sensitivity and exercise-induced gene expression in an unbiased, global manner.
Methods and Findings: Euglycemic clamps were used to measure insulin sensitivity and muscle biopsies were done at rest and 30 minutes after a single acute exercise bout in 14 healthy participants. Changes in mRNA expression were assessed using microarrays, and miRNA analysis was performed in a subset of 6 of the participants using sequencing techniques. Following exercise, 215 mRNAs were changed at the probe level (Bonferroni-corrected P<0.00000115). Pathway and Gene Ontology analysis showed enrichment in MAP kinase signaling, transcriptional regulation and DNA binding. Changes in several transcription factor mRNAs were correlated with insulin sensitivity, including MYC, r=0.71; SNF1LK, r=0.69; and ATF3, r= 0.61 (5 corrected for false discovery rate). Enrichment in the 5’-UTRs of exercise-responsive genes suggested regulation by common transcription factors, especially EGR1. miRNA species of interest that changed after exercise included miR-378, which is located in an intron of the PPARGC1B gene.
Conclusions: These results indicate that transcription factor gene expression responses to exercise depend highly on insulin sensitivity in healthy people. The overall pattern suggests a coordinated cycle by which exercise and insulin sensitivity regulate gene expression in muscle.
Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral whole blood cells. All subjects (n=184) were Latino descent from the Arizona Insulin Resistance registry. Subjects were classified based on the metabolic syndrome phenotype according to the National Cholesterol Education Program’s Adult Treatment Panel III. Of the 184 Latino subjects in the study, 74 were classified with the metabolic syndrome and 110 were without. Whole blood gene expression profiling was performed using the Agilent 4x44K Whole Human Genome Microarray. Whole blood microarray analysis identified 1,432 probes that were altered in expression ≥1.2 fold and P<0.05 after Benjamini-Hochberg in the metabolic syndrome subjects. KEGG pathway analysis revealed significant enrichment for pathways including ribosome, oxidative phosphorylation and MAPK signaling (all Benjamini-Hochberg P<0.05). Whole blood mRNA expression changes observed in the microarray data were confirmed by quantitative RT-PCR. Transcription factor binding motif enrichment analysis revealed E2F1, ELK1, NF-kappaB, STAT1 and STAT3 significantly enriched after Bonferroni correction (all P<0.05). The results of the present study demonstrate that whole blood is a useful tissue for studying the metabolic syndrome and its underlying insulin resistance although the relationship between blood and skeletal muscle differs.
Background: Although the effect of the fat mass and obesity-associated (FTO) gene on adiposity is well established, there is a lack of evidence whether physical activity (PA) modifies the effect of FTO variants on obesity in Latino populations. Therefore, the purpose of this study was to examine PA influences and interactive effects between FTO variants and PA on measures of adiposity in Latinos.
Results: After controlling for age and sex, participants who did not engage in regular PA exhibited higher BMI, fat mass, HC, and WC with statistical significance (P < 0.001). Although significant associations between the three FTO genotypes and adiposity measures were found, none of the FTO genotype by PA interaction assessments revealed nominally significant associations. However, several of such interactive influences exhibited considerable trend towards association.
Conclusions: These data suggest that adiposity measures are associated with PA and FTO variants in Latinos, but the impact of their interactive influences on these obesity measures appear to be minimal. Future studies with large sample sizes may help to determine whether individuals with specific FTO variants exhibit differential responses to PA interventions.
Background: Obesity is a metabolic disease caused by environmental and genetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to investigate the role of skeletal muscle DNA methylation in combination with transcriptomic changes in obesity.
Results: Muscle biopsies were obtained basally from lean (n = 12; BMI = 23.4 ± 0.7 kg/m[superscript 2]) and obese (n = 10; BMI = 32.9 ± 0.7 kg/m[superscript 2]) participants in combination with euglycemic-hyperinsulinemic clamps to assess insulin sensitivity. We performed reduced representation bisulfite sequencing (RRBS) next-generation methylation and microarray analyses on DNA and RNA isolated from vastus lateralis muscle biopsies. There were 13,130 differentially methylated cytosines (DMC; uncorrected P < 0.05) that were altered in the promoter and untranslated (5' and 3'UTR) regions in the obese versus lean analysis. Microarray analysis revealed 99 probes that were significantly (corrected P < 0.05) altered. Of these, 12 genes (encompassing 22 methylation sites) demonstrated a negative relationship between gene expression and DNA methylation. Specifically, sorbin and SH3 domain containing 3 (SORBS3) which codes for the adapter protein vinexin was significantly decreased in gene expression (fold change −1.9) and had nine DMCs that were significantly increased in methylation in obesity (methylation differences ranged from 5.0 to 24.4 %). Moreover, differentially methylated region (DMR) analysis identified a region in the 5'UTR (Chr.8:22,423,530–22,423,569) of SORBS3 that was increased in methylation by 11.2 % in the obese group. The negative relationship observed between DNA methylation and gene expression for SORBS3 was validated by a site-specific sequencing approach, pyrosequencing, and qRT-PCR. Additionally, we performed transcription factor binding analysis and identified a number of transcription factors whose binding to the differentially methylated sites or region may contribute to obesity.
Conclusions: These results demonstrate that obesity alters the epigenome through DNA methylation and highlights novel transcriptomic changes in SORBS3 in skeletal muscle.
Introduction: Decreased insulin sensitivity blunts the normal increase in gene expression from skeletal muscle after exercise. In addition, chronic inflammation decreases insulin sensitivity. Chronic kidney disease (CKD) is an inflammatory state. How CKD and, subsequently, kidney transplantation affects skeletal muscle gene expression after exercise are unknown.
Methods: Study cohort: non-diabetic male/female 4/1, age 52±2 years, with end-stage CKD who underwent successful kidney transplantation. The following were measured both pre-transplant and post-transplant and compared to normals: Inflammatory markers, euglycemic insulin clamp studies determine insulin sensitivity, and skeletal muscle biopsies performed before and within 30 minutes after an acute exercise protocol. Microarray analyses were performed on the skeletal muscle using the 4x44K Whole Human Genome Microarrays. Since nuclear factor of activated T cells (NFAT) plays an important role in T cell activation and calcineurin inhibitors are mainstay immunosuppression, calcineurin/NFAT pathway gene expression was compared at rest and after exercise. Log transformation was performed to prevent skewing of data and regression analyses comparing measures pre- and post-transplant performed.
Result: Markers of inflammation significantly improved post-transplantation. Insulin infusion raised glucose disposal slightly lower post-transplant compared to pre-transplant, but not significantly, thus concluding differences in insulin sensitivity were similar. The overall pattern of gene expression in response to exercise was reduced both pre-and post-transplant compared to healthy volunteers. Although significant changes were observed among NFAT/Calcineurin gene at rest and after exercise in normal cohort, there were no significant differences comparing NFAT/calcineurin pathway gene expression pre- and post-transplant.
Conclusions: Despite an improvement in serum inflammatory markers, no significant differences in glucose disposal were observed post-transplant. The reduced skeletal muscle gene expression, including NFAT/calcineurin gene expression, in response to a single bout of exercise was not improved post-transplant. This study suggests that the improvements in inflammatory mediators post-transplant are unrelated to changes of NFAT/calcineurin gene expression.