ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image

In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable in making the treatment plan. However, acquiring tooth root information to obtain the full tooth model from CBCT images is sometimes restricted due to the massive radiation of CBCT scanning. Thus, reconstructing the full tooth shape from the ready-to-use input, e.g., the partial intra-oral scan and the 2D panoramic image, is an applicable and valuable solution. In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic image and reconstructs the full tooth model with high-quality root(s). Technically, we utilize the implicit representation for both the 3D and 2D inputs, and learn a latent space of the full tooth shapes. At test time, given an input, we successfully project it to the learned latent space via neural optimization to obtain the full tooth model conditioned on the input. To help find the robust projection, a novel adversarial learning module is exploited in our pipeline. We extensively evaluate our method on a dataset collected from real-world clinics. The evaluation, comparison, and comprehensive ablation studies demonstrate that our approach produces accurate complete tooth models robustly and outperforms the state-of-the-art methods

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively.Comment: 10 pages, 9 figures, 5 tables, Fetal MRI, Brain tissue segmentation, Unsupervised domain adaptation, Cycle-consistenc

The effects of intake of bread with treated corn bran inclusion on postprandial glycaemic response

In the current study, corn bran was treated with hydrothermal processing and then incorporated into bread. The consumption of bread with inclusion of treated corn bran (TCB) and control bread (CB) on postprandial glycaemic response was investigated in a randomised crossover intervention trial with eleven healthy participants and one hyperglycaemicparticipant, capillary blood samples were measured at 0, 15, 30, 45, 60, 75, 90, 105 and 120 minutes after consuming the bread. The results showed the baseline-adjusted peak value of postprandial blood glucose with consumption of CB, containing 75 g carbohydrate was 4.27 mmol/L at 60 min after meal, but with consumption of treated corn bran bread (TCBB), containing 75 g carbohydrate was 3.88 mmol/L at 45 min after meal. In addition, the postprandial blood glucose concentration with consumption of CB is consistently higher than that with the consumption of TCBB since the peak time to 120 min. However, there was no significant differences, in turn, the incremental area under the curves (IAUC) with baseline-adjusted for CB consumption is consistently higher than that of TCBB consumption, but not any significant difference either (p>0.05). However, it is interesting to notice that more considerable difference in rise of blood sugar at peak time and thereafter for hyperglycaemicparticipant between the consumptions of TCBB and CB. In conclusion, the consumption of bread with inclusion of TCB is able to reduce the postprandial glycaemic response to a lower level compared with the consumption of CB and the more obvious difference was observed with the hyperglycaemicparticipant and healthy group Key words: Corn bran, hydrothermal treatment, postprandial glycaemic response. Intervention trial

A Spread Willingness Computing-Based Information Dissemination Model

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user’s spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network

Proteomic analysis of differentially expressed proteins in hepatitis B virus-related hepatocellular carcinoma tissues

<p>Abstract</p> <p>Background</p> <p>Hepatocellular carcinoma (HCC), a major cause of cancer death in China, is preceded by chronic hepatitis and liver cirrhosis (LC). Although hepatitis B virus (HBV) has been regarded as a clear etiology of human hepatocarcinogenesis, the mechanism is still needs to be further clarified. In this study, we used a proteomic approach to identify the differential expression protein profiles between HCC and the adjacent non-tumorous liver tissues.</p> <p>Methods</p> <p>Eighteen cases of HBV-related HCC including 12 cases of LC-developed HCC and 6 cases of chronic hepatitis B (CHB)-developed HCC were analyzed by two-dimensional electrophoresis (2-DE) combined with matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS), and the results were compared to those of paired adjacent non-tumorous liver tissues.</p> <p>Results</p> <p>A total of 17 differentially expressed proteins with diverse biological functions were identified. Among these, 10 proteins were up-regulated, whereas the other 7 proteins were down-regulated in cancerous tissues. Two proteins, c-Jun N-terminal kinase 2 and ADP/ATP carrier protein were found to be up-regulated only in CHB-developed HCC tissues. Insulin-like growth factor binding protein 2 and Rho-GTPase-activating protein 4 were down-regulated in LC-developed and CHB-developed HCC tissues, respectively. Although 11 out of these 17 proteins have been already described by previous studies, or are already known to be involved in hepatocarcinogenesis, this study revealed 6 new proteins differentially expressed in HBV-related HCC.</p> <p>Conclusion</p> <p>These findings elucidate that there are common features between CHB-developed HCC and LC-developed HCC. The identified proteins are valuable for studying the hepatocarcinogenesis, and may be potential diagnostic markers or therapeutic targets for HBV-related HCC.</p

Multi-View Vertebra Localization and Identification from CT Images

Accurately localizing and identifying vertebrae from CT images is crucial for various clinical applications. However, most existing efforts are performed on 3D with cropping patch operation, suffering from the large computation costs and limited global information. In this paper, we propose a multi-view vertebra localization and identification from CT images, converting the 3D problem into a 2D localization and identification task on different views. Without the limitation of the 3D cropped patch, our method can learn the multi-view global information naturally. Moreover, to better capture the anatomical structure information from different view perspectives, a multi-view contrastive learning strategy is developed to pre-train the backbone. Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae. Evaluation results demonstrate that, with only two 2D networks, our method can localize and identify vertebrae in CT images accurately, and outperforms the state-of-the-art methods consistently. Our code is available at https://github.com/ShanghaiTech-IMPACT/Multi-View-Vertebra-Localization-and-Identification-from-CT-Images.Comment: MICCAI 202

Soil phosphorus budget in global grasslands and implications for management

Grasslands, accounting for one third of the world terrestrial land surface, are important in determining phosphorus (P) cycle at a global scale. Understanding the impacts of management on P inputs and outputs in grassland ecosystem is crucial for environmental management since a large amount of P is transported through rivers and groundwater and detained by the sea reservoir every year. To better understand P cycle in global grasslands, we mapped the distribution of different grassland types around the world and calculated the corresponding P inputs and outputs for each grassland type using data from literature. The distribution map of P input and output revealed a non-equilibrium condition in many grassland ecosystems, with: (i) a greater extent of input than output in most managed grasslands, but (ii) a more balanced amount between input and output in the majority of natural grasslands. Based on the mass balance between P input and output, we developed a framework to achieve sustainable P management in grasslands and discussed the measures targeting a more balanced P budget. Greater challenge is usually found in heavily-managed than natural grasslands to establish the optimum amount of P for grass and livestock production while minimizing the adverse impacts on surface waters. This study provided a comprehensive assessment of P budget in global grasslands and such information will be critical in determining the appropriate P management measures for various grassland types across the globe