Feature Screening via Distance Correlation Learning

This paper is concerned with screening features in ultrahigh dimensional data analysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure based on the distance correlation (DC-SIS, for short). The DC-SIS can be implemented as easily as the sure independence screening procedure based on the Pearson correlation (SIS, for short) proposed by Fan and Lv (2008). However, the DC-SIS can significantly improve the SIS. Fan and Lv (2008) established the sure screening property for the SIS based on linear models, but the sure screening property is valid for the DC-SIS under more general settings including linear models. Furthermore, the implementation of the DC-SIS does not require model specification (e.g., linear model or generalized linear model) for responses or predictors. This is a very appealing property in ultrahigh dimensional data analysis. Moreover, the DC-SIS can be used directly to screen grouped predictor variables and for multivariate response variables. We establish the sure screening property for the DC-SIS, and conduct simulations to examine its finite sample performance. Numerical comparison indicates that the DC-SIS performs much better than the SIS in various models. We also illustrate the DC-SIS through a real data example.Comment: 32 pages, 5 tables and 1 figure. Wei Zhong is the corresponding autho

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

Feature Screening via Distance Correlation Learning

This article is concerned with screening features in ultrahigh-dimensional data analysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure based on the distance correlation (DC-SIS). The DC-SIS can be implemented as easily as the sure independence screening (SIS) procedure based on the Pearson correlation proposed by Fan and Lv. However, the DC-SIS can significantly improve the SIS. Fan and Lv established the sure screening property for the SIS based on linear models, but the sure screening property is valid for the DC-SIS under more general settings, including linear models. Furthermore, the implementation of the DC-SIS does not require model specification (e.g., linear model or generalized linear model) for responses or predictors. This is a very appealing property in ultrahigh-dimensional data analysis. Moreover, the DC-SIS can be used directly to screen grouped predictor variables and multivariate response variables. We establish the sure screening property for the DC-SIS, and conduct simulations to examine its finite sample performance. A numerical comparison indicates that the DC-SIS performs much better than the SIS in various models. We also illustrate the DC-SIS through a real-data example

Optogenetic dissection of ictal propagation in the hippocampal–entorhinal cortex structures

Temporal lobe epilepsy (TLE) is one of the most common drug-resistant forms of epilepsy in adults and usually originates in the hippocampal formations. However, both the network mechanisms that support the seizure spread and the exact directions of ictal propagation remain largely unknown. Here we report the dissection of ictal propagation in the hippocampal–entorhinal cortex (HP–EC) structures using optogenetic methods in multiple brain regions of a kainic acid-induced model of TLE in VGAT-ChR2 transgenic mice. We perform highly temporally precise cross-area analyses of epileptic neuronal networks and find a feed-forward propagation pathway of ictal discharges from the dentate gyrus/hilus (DGH) to the medial entorhinal cortex, instead of a re-entrant loop. We also demonstrate that activating DGH GABAergic interneurons can significantly inhibit the spread of ictal seizures and largely rescue behavioural deficits in kainate-exposed animals. These findings may shed light on future therapeutic treatments of TLE

An Assessment of Anthropogenic CO_2 Emissions by Satellite-Based Observations in China

Carbon dioxide (CO_2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO_2 emissions. Quantifying anthropogenic CO_2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO_2 emissions by an artificial neural network using column-average dry air mole fraction of CO_2 (XCO_2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO_2 anomalies (dXCO_2) derived from XCO_2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO_2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO_2 emissions especially in the areas with high anthropogenic CO_2 emissions. Our results indicate that XCO_2 data from satellite observations can be applied in estimating anthropogenic CO_2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO_2 uptake and emissions, from satellite observations

Inhibitor selectivity between aldo–keto reductase superfamily members AKR1B10 and AKR1B1: Role of Trp112 (Trp111)

AbstractThe antineoplastic target aldo–keto reductase family member 1B10 (AKR1B10) and the critical polyol pathway enzyme aldose reductase (AKR1B1) share high structural similarity. Crystal structures reported here reveal a surprising Trp112 native conformation stabilized by a specific Gln114-centered hydrogen bond network in the AKR1B10 holoenzyme, and suggest that AKR1B1 inhibitors could retain their binding affinities toward AKR1B10 by inducing Trp112 flip to result in an “AKR1B1-like” active site in AKR1B10, while selective AKR1B10 inhibitors can take advantage of the broader active site of AKR1B10 provided by the native Trp112 side-chain orientation

Pharmacokinetic and Pharmacogenetic Factors Contributing to Platelet Function Recovery After Single Dose of Ticagrelor in Healthy Subjects

Objectives: This study aimed to elucidate the contribution of candidate single nucleotide polymorphisms (SNPs) related to pharmacokinetics on the recovery of platelet function after single dose of ticagrelor was orally administered to healthy Chinese subjects.Methods: The pharmacokinetic profiles of ticagrelor and its metabolite AR-C124910XX (M8), and the platelet aggregation (PA), were assessed after 180 mg of single-dose ticagrelor was orally administered to 51 healthy Chinese subjects. Effects of CYP2C19*2, CYP2C19*3, CYP3A5*3, UGT1A1*6, UGT1A1*28, UGT2B7*2, UGT2B7*3, SLCO1B1 388A>G, and SLCO1B1 521T>C, on the pharmacokinetics of ticagrelor and M8, and platelet function recovery were investigated.Results: The time to recover 50% of the maximum drug effect (RT50) ranging from 36 to 126 h with 46.9% CV had a remarkable individual difference and was positively associated with the half-life (t1/2) of M8 (r = 0.3901, P = 0.0067). The time of peak concentration (Tmax) of ticagrelor for CYP2C19*3 GG homozygotes was significantly higher than that of GA heterozygotes (P = 0.0027, FDR = 0.0243). Decreased peak concentration (Cmax) of M8 was significantly associated with SLCO1B1 388A>G A allele (P = 0.0152, FDR = 0.1368). CYP2C19*2 A was significantly related to decreased Cmax of M8 (P = 0.0455, FDR = 0.2048). While, the influence of these nine SNPs on the recovery of platelet function was not significant.Conclusion: Our study suggests that the elimination of M8 is an important factor in determining the recovery of platelet function. Although CYP2C19 and SLCO1B1 genetic variants were related to the pharmacokinetics of ticagrelor or M8, they did not show a significant effect on the platelet function recovery in this study.Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT03092076, identifier: NCT0309207