An Uncertainty Aided Framework for Learning based Liver T1ρT_1\rho Mapping and Analysis

Objective: Quantitative $T_1\rho$ imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitative $T_1\rho$ imaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of the predicated $T_1\rho$ values to provide the confidence level of the quantification results. The uncertainty should also be utilized to aid the post-hoc quantitative analysis and model learning tasks. Approach: To address this need, we propose a parametric map refinement approach for learning-based $T_1\rho$ mapping and train the model in a probabilistic way to model the uncertainty. We also propose to utilize the uncertainty map to spatially weight the training of an improved $T_1\rho$ mapping network to further improve the mapping performance and to remove pixels with unreliable $T_1\rho$ values in the region of interest. The framework was tested on a dataset of 51 patients with different liver fibrosis stages. Main results: Our results indicate that the learning-based map refinement method leads to a relative mapping error of less than 3% and provides uncertainty estimation simultaneously. The estimated uncertainty reflects the actual error level, and it can be used to further reduce relative $T_1\rho$ mapping error to 2.60% as well as removing unreliable pixels in the region of interest effectively. Significance: Our studies demonstrate the proposed approach has potential to provide a learning-based quantitative MRI system for trustworthy $T_1\rho$ mapping of the liver

Uncertainty-weighted Multi-tasking for T1ρT_{1\rho} and T2_2 Mapping in the Liver with Self-supervised Learning

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T$T_{1\rho}$ and T$_2$ simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging

ZrTe2/CrTe2: an epitaxial van der Waals platform for spintronics

The rapid discovery of two-dimensional (2D) van der Waals (vdW) quantum materials has led to heterostructures that integrate diverse quantum functionalities such as topological phases, magnetism, and superconductivity. In this context, the epitaxial synthesis of vdW heterostructures with well-controlled interfaces is an attractive route towards wafer-scale platforms for systematically exploring fundamental properties and fashioning proof-of-concept devices. Here, we use molecular beam epitaxy to synthesize a vdW heterostructure that interfaces two material systems of contemporary interest: a 2D ferromagnet (1T-CrTe2) and a topological semimetal (ZrTe2). We find that one unit-cell (u.c.) thick 1T-CrTe2 grown epitaxially on ZrTe2 is a 2D ferromagnet with a clear anomalous Hall effect. In thicker samples (12 u.c. thick CrTe2), the anomalous Hall effect has characteristics that may arise from real-space Berry curvature. Finally, in ultrathin CrTe2 (3 u.c. thickness), we demonstrate current-driven magnetization switching in a full vdW topological semimetal/2D ferromagnet heterostructure device.Comment: Includes new data (ST-FMR) and calculations (spin Hall conductivity

Sources, Pollution Characteristics, and Ecological Risk Assessment of Steroids in Beihai Bay, Guangxi

Steroids are environmental endocrine disruptors that are discharged from vertebrates and are also byproducts of aquaculture. They have strong endocrine disrupting effects and are extremely harmful to the environment. The pollution of steroids in Beihai Bay was assessed through analyzing sources from rivers entering the bay. Six different types of steroids were detected in seagoing rivers, seagoing discharge outlets, and marine aquaculture farms, ranging from 0.12 (methyltestosterone) to 2.88 ng/L (estrone), from 0.11 (cortisol) to 5.41 ng/L (6a-methylprednisone (Dragon)), and from 0.13 (estradiol) to 2.51 ng/L (nandrolone), respectively. Moreover, 5 steroids were detected in 13 of the 19 seawater monitoring stations, accounting for 68.4% of the samples, and their concentrations ranged from 0.18 (methyltestosterone) to 4.04 ng/L (estrone). Furthermore, 7 steroids were detected in 15 of the 19 sediment monitoring stations, accounting for 78.9% of the samples, with concentrations ranging from 26 (estrone) to 776 ng/kg(androsterone). Thus, the main source of marine steroids were the discharging rivers and pollution sources entering the sea. An ecological risk assessment indicated that estrone and methyltestosterone were at high risk in this region; 17β estradiol (E2β) was medium risk, and other steroids were of low or no risk. This study provides a scientific basis for ecological risk assessment and control

A low-complexity energy minimization based SCMA detector and its convergence analysis

Sparse code multiple access (SCMA) has emerged as a promising non-orthogonal multiple access (NOMA) technique for next generation wireless communication systems. Since the signal of multiple users is mapped to the same resources in SCMA, its detection imposes a higher complexity than that of the orthogonal schemes, where each resource slot is dedicated to a single user. In this paper, we propose a low complexity receiver for SCMA systems based on the radical variational free energy framework. By exploiting the pairwise structure of the likelihood function, the Bethe approximation is utilized for estimating the data symbols. The complexity of the proposed algorithm only increases linearly with the number of users, which is much lower than that of the maximum a posteriori (MAP) detector associated with exponentially increased complexity. Furthermore, the convergence of the proposed algorithm is analyzed and its convergence conditions are derived. Simulation resultsdemonstrate that the proposed receiver is capable of approaching the error probability performance of the conventional message passing based receiver.</p

Additional file 2: of A microrna screen to identify regulators of peritoneal fibrosis in a rat model of peritoneal dialysis

Sequences of primers used in real-time PCR analysis of miRNA