High-dimensional A-learning for optimal dynamic treatment regimes

Precision medicine is a medical paradigm that focuses on finding the most effective treatment decision based on individual patient information. For many complex diseases, such as cancer, treatment decisions need to be tailored over time according to patients' responses to previous treatments. Such an adaptive strategy is referred as a dynamic treatment regime. A major challenge in deriving an optimal dynamic treatment regime arises when an extraordinary large number of prognostic factors, such as patient's genetic information, demographic characteristics, medical history and clinical measurements over time are available, but not all of them are necessary for making treatment decision. This makes variable selection an emerging need in precision medicine. In this paper, we propose a penalized multi-stage A-learning for deriving the optimal dynamic treatment regime when the number of covariates is of the nonpolynomial (NP) order of the sample size. To preserve the double robustness property of the A-learning method, we adopt the Dantzig selector, which directly penalizes the A-leaning estimating equations. Oracle inequalities of the proposed estimators for the parameters in the optimal dynamic treatment regime and error bounds on the difference between the value functions of the estimated optimal dynamic treatment regime and the true optimal dynamic treatment regime are established. Empirical performance of the proposed approach is evaluated by simulations and illustrated with an application to data from the STAR∗D study

MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.Comment: Accepted by AAAI 202

Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively.Comment: Accepted to AAAI2023, Ora

Metal-Organic Framework-Based Sensor for Bacterial Trehalose in Beef Products

Objective The objective is to detect trehalose from pathogenic bacteria using an electrochemical sensor based on alkali earth metal-organic frameworks (AE-MOFS)

The Association between ATM IVS 22-77 T>C and Cancer Risk: A Meta-Analysis

BACKGROUND AND OBJECTIVES: It has become increasingly clear that ATM (ataxia-telangiectasia-mutated) safeguards genome stability, which is a cornerstone of cellular homeostasis, and ATM IVS 22-77 T>C affects the normal activity of ATM proteins. However, the association between the ATM IVS 22-77 T>C genetic variant and cancer risk is controversial. Therefore, we conducted a systematic meta-analysis to estimate the overall cancer risk associated with the polymorphism and to quantify any potential between-study heterogeneity. METHODS: A total of nine studies including 4,470 cases and 4,862 controls were analyzed for ATM IVS 22-77 T>C association with cancer risk in this meta-analysis. Heterogeneity among articles and their publication bias were also tested. RESULTS: Our results showed that no association reached the level of statistical significance in the overall risk. Interestingly, in the stratified analyses, we observed an inverse relationship in lung and breast cancer. CONCLUSION: Further functional research on the ATM mechanism should be performed to explain the inconsistent results in different cancer types

The TERT rs2736100 Polymorphism and Cancer Risk: A Meta-analysis Based on 25 Case-Control Studies

<p>Abstract</p> <p>Background</p> <p>The association between the <it>TERT rs2736100 </it>single nucleotide polymorphism (SNP) and cancer risk has been studied by many researchers, but the results remain inconclusive. To further explore this association, we performed a meta-analysis.</p> <p>Methods</p> <p>A computerized search of PubMed and Embase database for publications on the <it>TERT rs2736100 </it>polymorphism and cancer risk was performed and the genotype data were analyzed in a meta-analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated to assess the association. Sensitivity analysis, test of heterogeneity, cumulative meta-analysis and assessment of bias were performed in our meta-analysis.</p> <p>Results</p> <p>A significant association between the <it>TERT rs2736100 </it>polymorphism and cancer susceptibility was revealed by the results of the meta-analysis of the 25 case-control studies (GG versus TT: OR = 1.72, 95% CI: 1.58, 1.88; GT versus TT: OR = 1.38, 95% CI: 1.29, 1.47; dominant model-TG + GG versus TT: OR = 1.47, 95% CI: 1.37, 1.58; recessive model-GG versus TT + TG: OR = 1.37, 95% CI 1.31, 1.43; additive model-2GG + TG versus 2TT + TG: OR = 1.30, 95% CI: 1.25, 1.36). Moreover, increased cancer risk in all genetic models was found after stratification of the SNP data by cancer type, ethnicity and source of controls.</p> <p>Conclusions</p> <p>In all genetic models, the association between the <it>TERT rs2736100 </it>polymorphism and cancer risk was significant. This meta-analysis suggests that the <it>TERT rs2736100 </it>polymorphism may be a risk factor for cancer. Further functional studies between this polymorphism and cancer risk are warranted.</p

A reference-grade wild soybean genome

Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci

A reference-grade wild soybean genome

Efficient crop improvement depends on the application of accurate genetic information contained in diverse germplasm resources. Here we report a reference-grade genome of wild soybean accession W05, with a final assembled genome size of 1013.2 Mb and a contig N50 of 3.3 Mb. The analytical power of the W05 genome is demonstrated by several examples. First, we identify an inversion at the locus determining seed coat color during domestication. Second, a translocation event between chromosomes 11 and 13 of some genotypes is shown to interfere with the assignment of QTLs. Third, we find a region containing copy number variations of the Kunitz trypsin inhibitor (KTI) genes. Such findings illustrate the power of this assembly in the analysis of large structural variations in soybean germplasm collections. The wild soybean genome assembly has wide applications in comparative genomic and evolutionary studies, as well as in crop breeding and improvement programs

3-D geological modeling for tight sand gas reservoir of braided river facies

Considering the poor applicability of conventional geological modeling to tight sand gas reservoir in braided river facies, a modeling method of “multi-stage constraints, hierarchical facies control and multi-step modeling” was put forward taking Sulige gas field in Ordos Basin as the study object. The method obtains the GR field by seismic inversion constrained by logging data, and GR model is built under the control of the prior geological knowledge; the relation regression is realized between the GR model and the sandstone probability, sandstone probability model is built, and rock facies model is obtained by multi-point geostatistics theory; sedimentary microfacies model controlled by rock facies and braided-river-system is made; and eventually an effective sand body model is built by integrating sedimentary microfacies, effective sand body scale and reservoir properties distribution. The research method discussed in this paper has put geological constraints into the model as far as possible, enhanced the inter-well sand body predictability and improved the precision rate, thus it can provide a more reliable geological basis for gas reservoir development. Key words: Sulige gas field, tight sand gas, geological modeling, rock facies model, sedimentary microfacies model, multi-stage constraint, hierarchical facie