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

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

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

Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

No evidence for substrate accumulation in Parkinson brains with GBA mutations

To establish whether Parkinson's disease (PD) brains previously described to have decreased glucocerebrosidase activity exhibit accumulation of the lysosomal enzyme's substrate, glucosylceramide, or other changes in lipid composition

Effect of acute physiological free fatty acid elevation in the context of hyperinsulinemia on fiber type-specific IMCL accumulation.

It is well described that increasing free fatty acids (FFAs) to high physiological levels reduces insulin sensitivity. In sedentary humans, intramyocellular lipid (IMCL) is inversely related to insulin sensitivity. Since muscle fiber composition affects muscle metabolism, whether FFAs induce IMCL accumulation in a fiber type-specific manner remains unknown. We hypothesized that in the setting of acute FFA elevation by lipid infusion within the context of a hyperinsulinemic-euglycemic clamp, IMCL will preferentially accumulate in type 1 fibers. Normal-weight participants (n = 57, mean ± SE: age 24 ± 0.6 yr, BMI 22.2 ± 0.3 kg/m(2)) who were either endurance trained or sedentary by self-report were recruited from the University of Minnesota (n = 31, n = 15 trained) and University of Pittsburgh (n = 26, n = 14 trained). All participants underwent a hyperinsulinemic-euglycemic clamp in the context of a 6-h infusion of either lipid or glycerol control. A vastus lateralis muscle biopsy was obtained at baseline and end-infusion (6 h). The muscle biopsies were processed and analyzed at the University of Pittsburgh for fiber type-specific IMCL accumulation by Oil-Red-O staining. Regardless of training status, acute elevation of FFAs to high physiological levels (~400-600 meq/l) increased IMCL preferentially in type 1 fibers (+35 ± 11% compared with baseline, +29 ± 11% compared with glycerol control: P < 0.05). The increase in IMCL correlated with a decline in insulin sensitivity as measured by the hyperinsulinemic-euglycemic clamp (r = -0.32, P < 0.01) independent of training status. Regardless of training status, increase of FFAs to a physiological range within the context of hyperinsulinemia shows preferential IMCL accumulation in type 1 fibers.NEW & NOTEWORTHY This novel human study examined the effects of FFA elevation in the setting of hyperinsulinemia on accumulation of fat in specific types of muscle fibers. Within the context of the hyperinsulinemic-euglycemic clamp, we found that an increase of FFAs to a physiological range sufficient to reduce insulin sensitivity is associated with preferential IMCL accumulation in type 1 fibers

An endophytic Taxol-producing fungus BT2 isolated from Taxus chinensis var. mairei

BT2, a newly isolated endophytic fungus from Taxus chinensis var. mairei, was observed to produce Taxol. Besides Taxol, a potent anticancer drug, BT2 could also yield taxane baccatin III, which was an important intermediate for Taxol and semi-synthesis of Taxol in industry. The isolation of such a fungusmay provide a promising alterative approach to produce Taxol, and BT2 can serve as a potential material for fungus engineering to improve Taxol productio

Global financial crisis and job satisfaction of atypical workers : the case of Taiwan

Author name used in this publication: Bih-Hearn Virginia LeeAuthor name used in this publication: David Fu-Keung Ip2010-2011 > Academic research: not refereed > Publication in policy or professional journalAccepted ManuscriptPublishe