Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans

The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions. The overlapping area among the intensity distribution of healthy, enhancing, non-enhancing, and edema regions makes the automatic segmentation a challenging task. Here, we show that a convolutional neural network trained on high-contrast images can transform the intensity distribution of brain lesions in its internal subregions. Specifically, a generative adversarial network (GAN) is extended to synthesize high-contrast images. A comparison of these synthetic images and real images of brain tumor tissue in MR scans showed significant segmentation improvement and decreased the number of real channels for segmentation. The synthetic images are used as a substitute for real channels and can bypass real modalities in the multimodal brain tumor segmentation framework. Segmentation results on BraTS 2019 dataset demonstrate that our proposed approach can efficiently segment the tumor areas. In the end, we predict patient survival time based on volumetric features of the tumor subregions as well as the age of each case through several regression models

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.Comment: Accepted at MICCAI BrainLes 201

Basel risk weights, asset correlations, and book-to-market equity: evidence from Asian countries

We examine the effect of firm book-to-market equity values (BE/ME) on asset correlations which play an important role in determining risk weights under the current Basel capital requirements. Using firms in China, Hong Kong, Japan, Korea, Singapore and Taiwan over a sample period from 1988 to 2013, we find that BE/ME has a negative effect on asset correlations. This suggests a role for BE/ME as an additional factor in determining asset correlations, and thus risk weights, also potentially reducing incentives for regulatory capital arbitrage

Band and scattering tuning for high performance thermoelectric Sn1-xMnxTe alloys

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Identification and Characterization of MicroRNAs in Asiatic Cotton (Gossypium arboreum L.)

To date, no miRNAs have been identified in the important diploid cotton species although there are several reports on miRNAs in upland cotton. In this study, we identified 73 miRNAs, belonging to 49 families, from Asiatic cotton using a well-developed comparative genome-based homologue search. Several of the predicted miRNAs were validated using quantitative real time PCR (qRT-PCR). The length of miRNAs varied from 18 to 22 nt with an average of 20 nt. The length of miRNA precursors also varied from 46 to 684 nt with an average of 138 ±120 nt. For a majority of Asiatic cotton miRNAs, there is only one member per family; however, multiple members were identified for miRNA 156, 414, 837, 838, 1044, 1533, 2902, 2868, 5021 and 5142 families. Nucleotides A and U were dominant, accounted for 62.95%, in the Asiatic cotton pre-miRNAs. The Asiatic cotton pre-miRNAs had high negative minimal folding free energy (MFE) and adjusted MFE (AMFE) and high MFE index (MFEI). Many miRNAs identified in Asiatic cotton suggest that miRNAs also play a similar regulatory mechanism in diploid cotton

SYNTHESIS, CHARACTERIZATION AND AQUATION KINETICS OF A NEW TRANS DIASTEREOISOMER OF DICHLORO(MESO-5,12-DIMETHYL-1,4,8,11-TETRAAZACYCLOTETRADECANE)COBALT(III) PERCHLORATE

[[abstract]]A new trans diastereoisomer of dichloro(meso-5,12-dimethyl-1,4,8,11-tetraazacyclotetradecane)cobalt(III) perchlorate has been prepared, and the electronic absorption and infrared spectral data studied. The assignments of the structure of the isomer have been accomplished by thorough stereochemical analysis combined with H-1 and C-13 NMR spectral data. Aquation kinetics have been studied at various temperatures and activation parameters determined. The results indicate a dissociative activation with retention of configuration and a distorted-square pyramidal activated complex[[fileno]]2010337010123[[department]]化學

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Aristolochic acid exposure in Romania and implications for renal cell carcinoma

Background: Aristolochic acid (AA) is a nephrotoxicant associated with AA nephropathy (AAN) and upper urothelial tract cancer (UUTC). Whole-genome sequences of 14 Romanian cases of renal cell carcinoma (RCC) recently exhibited mutational signatures consistent with AA exposure, although RCC had not been previously linked with AAN and AA exposure was previously reported only in localised rural areas. Methods: We performed mass spectrometric measurements of the aristolactam (AL) DNA adduct 7-(deoxyadenosin-N6-yl) aristolactam I (dA-AL-I) in nontumour renal tissues of the 14 Romanian RCC cases and 15 cases from 3 other countries. Results: We detected dA-AL-I in the 14 Romanian cases at levels ranging from 0.7 to 27 adducts per 108 DNA bases, in line with levels reported in Asian and Balkan populations exposed through herbal remedies or food contamination. The 15 cases from other countries were negative. Interpretation: Although the source of exposure is uncertain and likely different in AAN regions than elsewhere, our results demonstrate that AA exposure in Romania exists outside localised AAN regions and provide further evidence implicating AA in RCC

Knocking down 10-formyltetrahydrofolate dehydrogenase increased oxidative stress and impeded zebrafish embryogenesis by obstructing morphogenetic movement

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