CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidence-based Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper will be released

A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules

Traditional techniques for smart contract vulnerability detection rely on fixed expert criteria to discover vulnerabilities, which are less generalizable, scalable, and accurate. Deep learning algorithms help to address these issues, but most fail to encode true expert knowledge and remain interpretable. In this paper, we present a smart contract vulnerability detection mechanism that operates in phases with graph neural networks and expert patterns in deep learning to mutually address the deficiencies of the two detection approaches and improve smart contract vulnerability detection capabilities. Experiments show that our vulnerability detection mechanism outperforms the original deep learning model by an average of 6 points in detecting vulnerabilities and that the second stage of the checking mechanism can also block contract transactions containing dangerous actions at the Ethernet Virtual Machine (EVM) level and generate error reports for submission. This strategy helps to construct more stable smart contracts and to create a secure environment for smart contracts

Association between interleg systolic blood pressure difference and apparent peripheral neuropathy in US adults with diabetes: a cross-sectional study

Abstract Background Interleg systolic blood pressure difference (ILSBPD) is associated with peripheral artery disease, but the relationship between ILSBPD and apparent peripheral neuropathy in diabetic patients remains unclear. We explored the relationship between ILSBPD and apparent peripheral neuropathy and examined the possible effect modifiers in US adults with diabetes. Methods One thousand and fifty-one diabetic participants were included in the study with complete data on systolic blood pressure of the lower extremities and Semmes–Weinstein 10-g monofilament testing from the 1999–2004 National Health and Nutritional Examination Surveys. Systolic blood pressure in the lower extremities was measured using an oscillometric blood pressure device with the patient in the supine position. Apparent peripheral neuropathy was defined as the presence of monofilament insensitivity. Results Every 5-mmHg increment in ILSBPD is associated with an about 14% increased risk of apparent peripheral neuropathy in crude model, but after adjustment for covariates, the correlation became nonsignificant (P = 0.160). When participants were divided into groups based on ILSBPD cutoffs of 5, 10 and 15 mmHg in different analyses, there was a significantly increased risk of apparent peripheral neuropathy in the ILSBPD ≥ 15 mmHg group (OR 1.79, 95% CI 1.11–2.91, P = 0.018), even after adjusting for confounders. In subgroup analysis, no interaction effect was found (all P for interaction > 0.05). Conclusions In US adults with diabetes, an increase in the ILSBPD (≥ 15 mmHg) was associated with a higher risk of apparent peripheral neuropathy

Polymerase I and transcript release factor acts as an essential modulator of glioblastoma chemoresistance.

OBJECTIVES: This study is to investigate if polymerase I and transcript release factor (PTRF) acts as a modulator in glioblastoma (GBM) chemoresistance. METHODS: Multidrug resistant (MDR) GBM cell line U251AR was established by exposing the U251 cell line to imatinib. The 2D-DIGE and MALDI-TOF/TOF-MS were performed on U251 and U251AR cell lines to screen MDR-related proteins. The expression of PTRF was determined by Western blot and quantitative RT-PCR analyses. RESULTS: When compared with the parental U251 cells, expression of 21 proteins was significantly altered in U251AR cells. Among the 21 differentially expressed proteins, the expression of PTRF was up-regulated by 2.14 folds in U251AR cells when compared with that in the parental U251 cells. Knockdown of PTRF in GBM cell lines significantly increased chemosensitivity of cells to various chemical drugs and decreased the expression levels of caveolin1, a major structural component of caveolae. Expression levels of PTRF and caveolin1 were significantly up-regulated in the relapsed GBM patients. The mRNA level of PTRF and caveolin1 showed a positive correlation in the same GBM specimens. CONCLUSIONS: Our results indicate that PTRF acts as a modulator in GBM chemoresistance

Self-Templated Synthesis of Triphenylene-Based Uniform Hollow Spherical Two-Dimensional Covalent Organic Frameworks for Drug Delivery

Constructing two-dimensional covalent organic frameworks (2DCOFs) with a desirable crystalline structure and morphology is promising but remains a significant challenge. Herein, we report self-templated synthesis of uniform hollow spherical 2DCOFs based on 2,3,6,7,10,11-hexakis(4-aminophenyl) triphenylene. A detailed time-dependent study of hollow sphere formation reveals an intriguing transformation from initial homogeneous solid spheres into uniform hollow spheres with the Ostwald ripening mechanism. Impressively, the resultant spherical 2DCOFs are composed of high crystallinity nanosheets and even hexagonal single crystals, as demonstrated by transmission electron microscopy. Thanks to its uniform morphology and high crystallinity, the pore volume of the obtained 2DCOFs is up to 1.947 cm3 g–1, which makes it function as superior nanocarriers for efficient controlled drug delivery. This result provides an avenue for improving COFs’ performance by regulating their morphology

Twenty-one differentially expressed proteins in U251 cell line versus U251AR cell line.

<p>Note:</p>a<p>Protein MW, Protein molecular weight;</p>b<p>Fold changes (mean ± SD) of U251 cell line vs. U251AR cell line, which were calculated from the DeCyder/spot volume analysis;</p>c<p>up, up-regulated in the U251AR cell line;</p>d<p>down, down-regulated in the U251AR cell line.</p

Proteomic analysis of GBM cells by 2D-DIGE.

<p>A representative 2D-DIGE image (merged image) showed the protein profile of U251AR and U251 cells, labeled with Cy3 (green spots) and Cy5 (red spots), respectively. The approximate molecular weight range in the vertical dimension was from 10 to 150 kD. The PI of proteins ranged from 3 to 10. The differently expressed protein spot ID were indicated with white arrows. The protein spot 2421 and 1737 was PTRF and VIM, respectively.</p