Physics-Driven Turbulence Image Restoration with Stochastic Refinement

Image distortion by atmospheric turbulence is a stochastic degradation, which is a critical problem in long-range optical imaging systems. A number of research has been conducted during the past decades, including model-based and emerging deep-learning solutions with the help of synthetic data. Although fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions recently, the training of such models only relies on the synthetic data and ground truth pairs. This paper proposes the Physics-integrated Restoration Network (PiRN) to bring the physics-based simulator directly into the training process to help the network to disentangle the stochasticity from the degradation and the underlying image. Furthermore, to overcome the ``average effect" introduced by deterministic models and the domain gap between the synthetic and real-world degradation, we further introduce PiRN with Stochastic Refinement (PiRN-SR) to boost its perceptual quality. Overall, our PiRN and PiRN-SR improve the generalization to real-world unknown turbulence conditions and provide a state-of-the-art restoration in both pixel-wise accuracy and perceptual quality. Our codes are available at \url{https://github.com/VITA-Group/PiRN}.Comment: Accepted by ICCV 202

Principal component analysis of early alcohol, drug and tobacco use with major depressive disorder in US adults

Early alcohol, tobacco and drug use prior to 18 years old are comorbid and correlated. This study included 6239 adults with major depressive disorder (MDD) in the past year and 72,010 controls from the combined data of 2013 and 2014 National Survey on Drug Use and Health (NSDUH). To deal with multicollinearity existing among 17 variables related to early alcohol, tobacco and drug use prior to 18 years old, we used principal component analysis (PCA) to infer PC scores and then use weighted multiple logistic regression analyses to estimate the associations of potential factors and PC scores with MDD. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The overall prevalence of MDD was 6.7%. The first four PCs could explain 57% of the total variance. Weighted multiple logistic regression showed that PC1 (a measure of psychotherapeutic drugs and illicit drugs other than marijuana use), PC2 (a measure of cocaine and hallucinogens), PC3 (a measure of early alcohol, cigarettes, and marijuana use), and PC4 (a measure of cigar, smokeless tobacco use and illicit drugs use) revealed significant associations with MDD (OR = 1.12, 95% CI = 1.08-1.16, OR = 1.08, 95% CI = 1.04-1.12, OR = 1.13, 95% CI = 1.07-1.18, and OR = 1.15, 95% CI = 1.09-1.21, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. Early alcohol, tobacco and drug use prior to 18 years old were found to be associated with increased odds of adult MDD

Analysis of PTPRK polymorphisms in association with risk and age at onset of Alzheimer’s disease, cancer risk, and cholesterol

The human receptor-type protein-tyrosine phosphatase kappa (PTPRK) gene is highly expressed in human brain and is previously associated with neuropsychiatric disorders and cancer. This study investigated the association of 52 single nucleotide polymorphisms (SNPs) in the PTPRK with the risk and age at onset (AAO) of Alzheimer’s disease (AD) in 791 AD patients and 782 controls. Five SNPs (top SNP rs4895829 with p=0.0125) were associated with the risk of AD based on a multiple logistic regression (p\u3c0.05); while 6 SNPs (top SNP rs1891150 with p=8.02×10−6) were associated with AAO by using a multiple linear regression analysis. Interestingly, rs2326681 was associated with both the risk and AAO of AD (p=4.65×10−2 and 5.18×10−3, respectively). In a replication study, the results from family-based association test - generalized estimating equation (GEE) statistics and Wilcoxon test showed that seven SNPs were associated with the risk of AD (top SNP rs11756545 with p=1.02×10−2) and 12 SNPs were associated with the AAO (top SNP rs11966128 with p=1.39×10−4), respectively. One additional sample showed that four SNPs were associated with risk of cancer (top SNP rs1339197 with p=4.1×10−3), 12 SNPs associated with LDL-cholesterol (top SNP rs4544930 with p=3.47×10−3), and 8 SNPs associated with total cholesterol (top SNP rs1012049 with p=6.09×10−3). In addition, the AD associated rs4895829 was associated with the gene expression level in the cerebellum (p=7.3×10−5). The present study is the first study providing evidence of several genetic variants within the PTPRK gene associated with the risk and AAO of AD, risk of cancer, LDL and total cholesterol levels

Systematically characterizing dysfunctional long intergenic noncoding RNAs in multiple brain regions of major psychosis

Alzheimer\u27s disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and age at onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 single nucleotide polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8×10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The hazards ratio (HR) with its 95% confidence interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR=1.33, 95%CI=1.13-1.57, p=5.0×10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR=1.34, 95%CI=1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p=0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p=2.27×10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD

Surface plasmon-enhanced zeolite catalysis under light irradiation and its correlation with molecular polarity of reactants

Enhanced catalytic performance of zeolites via the plasmonic effect of gold nanoparticles has been discovered to be closely correlated with the molecular polarity of reactants. The intensified polarised electrostatic field of Na+ in NaY plays a critical role in stretching the C=O bond of aldehydes to improve the reaction rate

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

This letter presents a complete framework Meeting-Merging-Mission for multi-robot exploration under communication restriction. Considering communication is limited in both bandwidth and range in the real world, we propose a lightweight environment presentation method and an efficient cooperative exploration strategy. For lower bandwidth, each robot utilizes specific polytopes to maintains free space and super frontier information (SFI) as the source for exploration decision-making. To reduce repeated exploration, we develop a mission-based protocol that drives robots to share collected information in stable rendezvous. We also design a complete path planning scheme for both centralized and decentralized cases. To validate that our framework is practical and generic, we present an extensive benchmark and deploy our system into multi-UGV and multi-UAV platforms

Bayesian Cox Proportional Hazards Model in Survival Analysis of HACE1 Gene with Age at Onset of Alzheimer’s Disease

Alzheimer’s Disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and Age at Onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 Single Nucleotide Polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8 × 10-3) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The Hazards Ratio (HR) with its 95% Confidence Interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR = 1.33, 95% CI = 1.13-1.57, p = 5.0 × 10-4). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR = 1.34, 95% CI = 1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p = 0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p = 2.27 × 10-3 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD

Current Development of in Vitro and in Vivo Methods for Predicting Glycemic Indexes of Carbohydrate Foods

Glycemic index (GI) is a key indicator for evaluating the postprandial glycemic response to carbohydrate foods. A low GI diet can not only help to control appetite and delay hunger, but also benefit weight control and improve glucose and lipid levels in diabetic patients. The development of low GI foods has thus become a hotspot in current food research. At present, the international standard ISO 26642:2010, issued by the International Standards Organization (ISO), is the gold standard for measuring the GI values of foods using human subjects around the world. However, human testing has some disadvantages, such as individual differences may lead to significantly different results, even for the same foods, and it is costly and time consuming, and should be ethical, and it is unsuitable for high-throughput testing of food GI values. For this reason, researchers have successively developed various in vitro models to predict food GI values. This article focuses on reviewing the current in vitro and in vivo methods for predicting the GI values of foods, with a particular focus on their advantages and disadvantages, as well as their future developments. The current paper is aimed to provide new ideas for the development and promotion of low GI foods