Weinreich Santiago (drydream4)

To achieve three-dimensional (3D) distortion-free apparent diffusion coefficient (ADC) maps for prostate imaging using a multishot diffusion prepared-gradient echo (msDP-GRE) sequence and ADC dictionary matching. The msDP-GRE sequence is combined with a 3D Cartesian, centric k-space trajectory with center oversampling. Oversampled k-space center averaging and phase cycling are used to address motion- and eddy current-induced magnitude corruption. Extended-phase-graph (EPG) simulations and ADC dictionary matching are used to compensate for T effects. To shorten the acquisition time, each volume is undersampled by a factor of two and reconstructed using iterative sensitivity encoding. The proposed approach is characterized using simulations and validated in a kiwifruit phantom, comparing the msDP-GRE ADC maps obtained using both standard monoexponential fitting and dictionary matching with the clinical standard single-shot diffusion weighted-echo planar imaging (ssDW-EPI) ADC. Initial in vivo feasibility is tested in three healthy subjects, and geometric distortion is compared with anatomical T -weighted-turbo spin echo. In the kiwifruit phantom experiment, the signal magnitude could be recovered using k-space center averaging and phase cycling. No statistically significant difference was observed in the ADC values estimated using msDP-GRE with dictionary matching and clinical standard DW-EPI (P < .05). The in vivo prostate msDP-GRE scans were free of geometric distortion caused by off-resonance susceptibility, and the ADC values in the prostate were in agreement with values found in the published literature. Nondistorted 3D ADC maps of the prostate can be achieved using a msDP sequence and dictionary matching. Nondistorted 3D ADC maps of the prostate can be achieved using a msDP sequence and dictionary matching.In this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Model-based Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of "drop-out" with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same individual via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific conclusions. TWO-SIGMA is implemented in the R package twosigma available at https//github.com/edvanburen/twosigma. Few studies have investigated the impact of active surveillance on pathological outcome ground-glass nodules (GGNs). We focused on GGNs that needed preoperative localization before resection and compared the pathological results between GGNs that underwent early resection or active surveillance. We retrospectively reviewed data of resected GGNs between January 2017 and December 2018. GGNs were classified by early res