An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile, manual inspection of each case to locate the failures becomes infeasible due to the trend of large data scale and limited human resource. Therefore, we build an alarm system that will set off alerts when the segmentation result is possibly unsatisfactory, assuming no corresponding ground truth mask is provided. One plausible solution is to project the segmentation results into a low dimensional feature space; then learn classifiers/regressors to predict their qualities. Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data.The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks. During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values. Thus, the VAE is able to evaluate the quality of segmentation result on unseen data, without using ground truth. Finally, we learn a regressor in the one-dimensional feature space to predict the qualities of segmentation results. Our alarm system is evaluated on several recent state-of-art segmentation algorithms for 3D medical segmentation tasks. Compared with other standard quality assessment methods, our system consistently provides more reliable prediction on the qualities of segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures

Development and characterization of EST-SSRs for Muscidae (Diptera) and their cross-genera transferability

EST-SSR (Expressed Sequence Tag-Simple Sequence Repeat)markers were developed and used to examine genetic diversity of species assemblages of ten genera of Muscidae (Diptera), and specifically the genetic diversity of Phaonia Robineau species, from ten regions in China. 18 EST-SSR markers with high polymorphism and clear bands were screened out from 216 tested markers, with 219 alleles in total (95–280 bp) with an average of 6.1 alleles per locus. In various regions in China such as Tibet, Sichuan, and Yunnan, Muscidae species assemblages exhibited rich genetic diversity, with the polymorphism information content (PIC) values ranging from 0.831 to 0.934, while Hainan region showed a relatively low genetic diversity (PIC = 0.511). Low gene flow (Nm = 0.842) was found among the regions, and the average genetic differentiation coefficient (Fst) was 0.238, indicating a high degree of genetic differentiation among the ten surveyed assemblages. Accordingly, the ten genera of Muscidae exhibited high genetic diversity and genetic differentiation

Stepping-stone or stumbling block: impact of the economic system on China’s OFDI

With the proposal of the One Belt, One Road initiative, China’s OFDI has grown rapidly, with institutions playing key roles. This study uses residualization and threshold regression methods to analyse the 2003–2019 panel data of 31 provinces and cities in China to study the impact of property rights protection, market operation, and financing systems on China’s foreign outward direct investment (OFDI). Overall, the results show that only the market operation system is key in promoting OFDI. Regarding regional heterogeneity, the property rights protection system has a solely positive incentive effect on OFDI in the central region. Moreover, the market operation system only plays a positive role in promoting OFDI in the eastern developed and central regions. The financing system has a threshold effect, which has significant negative and positive impacts on OFDI in the eastern and central regions, respectively. Conversely, the financing system’s impact changes from significant to insignificant in the western region before and after the threshold value. This study provides a theoretical reference for economic system reform in the process of implementing the strategy of “going out” in various regions

Preliminary study of beta-blocker therapy on modulation of interleukin-33/ST2 signaling during ventricular remodeling after acute myocardial infarction

Background: This study aimed to evaluate the role of b-blocker therapy on modulating interleukin (IL)-33/ST2 (interleukin-1 receptor-like 1) signaling during ventricular remodeling related to heart failure (HF) after acute myocardial infarction (AMI). Methods: Sprague-Dawley rats that survived surgery to induce AMI were randomly divided into the placebo group and the b-blocker treatment group. A sham group was used as a control. Left ventricular (LV) function variables, the myocardial infarct size, fibrosis and IL-33/ST2 protein expression was measured. Results: Compared with the placebo group, b-blocker treatment significantly improved LV function and reduced infarct size (p < 0.05). There was higher protein expression of IL-33 (p < 0.05) and sST2 (p < 0.05), as well as higher expression of fibrosis (p < 0.05), compared to the sham group. Notably, the high expression of cardioprotective IL-33 was not affected by b-blocker treatment (p > 0.05), however, treatment with b-blocker enhanced IL-33/ST2 signaling, with lower expression of sST2 (p < 0.05) and significantly attenuated fibrosis (p < 0.05). Conclusions: Our study suggested that b-blocker therapy might play a beneficial role in the modula­tion of IL-33/ST2 signaling during ventricular remodeling. These results may be helpful in identifying IL-33/ST2 systems as putative b-blocker targets at an early stage after AMI. (Cardiol J 2017; 24, 2: 188–194

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical Imaging (MLMI2019

Effect of α-linolenic acid on endoplasmic reticulum stress-mediated apoptosis of palmitic acid lipotoxicity in primary rat hepatocytes

<p>Abstract</p> <p>Background</p> <p>Hepatic inflammation and degeneration induced by lipid depositions may be the major cause of nonalcoholic fatty liver disease (NAFLD). In this study, we investigated the effects of saturated and unsaturated fatty acids (FA) on apoptosis in primary rat hepatocytes.</p> <p>Methods</p> <p>The primary rat hepatocytes were treated with palmitic acid and/or α-linolenic acid in vitro. The expression of proteins associated with endoplasmic reticulum (ER) stress, apoptosis, caspase-3 levels were detected after the treatment.</p> <p>Results</p> <p>The treatment with palmitic acid produced a significant increase in cell death. The unfolded protein response (UPR)-associated genes CHOP, GRP78, and GRP94 were induced to higher expression levels by palmitic acid. Co-treatment with α-linolenic acid reversed the apoptotic effect and levels of all three indicators of ER stress exerted by palmitic acid. Tunicamycin, which induces ER stress produced similar effects to those obtained using palmitic acid; its effects were also reversed by α-linolenic acid.</p> <p>Conclusions</p> <p>α-Linolenic acid may provide a useful strategy to avoid the lipotoxicity of dietary palmitic acid and nutrient overload accompanied with obesity and NAFLD.</p

Self-Powered Gesture Recognition with Ambient Light

We present a self-powered module for gesture recognition that utilizes small, low-cost photodiodes for both energy harvesting and gesture sensing. Operating in the photovoltaic mode, photodiodes harvest energy from ambient light. In the meantime, the instantaneously harvested power from individual photodiodes is monitored and exploited as a clue for sensing finger gestures in proximity. Harvested power from all photodiodes are aggregated to drive the whole gesture-recognition module including a micro-controller running the recognition algorithm. We design robust, lightweight algorithm to recognize finger gestures in the presence of ambient light fluctuations. We fabricate two prototypes to facilitate user’s interaction with smart glasses and smart watches. Results show 99.7%/98.3% overall precision/recall in recognizing five gestures on glasses and 99.2%/97.5% precision/recall in recognizing seven gestures on the watch. The system consumes 34.6 µW/74.3 µW for the glasses/watch and thus can be powered by the energy harvested from ambient light. We also test system’s robustness under various light intensities, light directions, and ambient light fluctuations. The system maintains high recognition accuracy (\u3e 96%) in all tested settings