Exploring Radiologic Criteria for Glioma Grade Classification on the BraTS Dataset

ObjectivesGlioma grading using maching learning on magnetic resonance data is a growing topic. According to the World Health Organization (WHO), the classification of glioma discriminates between low grade gliomas (LGG), grades I, II; and high grade gliomas (HGG), grades III, IV, leading to major issues in oncology for therapeutic management of patients. A well-known dataset for machine-based grade prediction is the MICCAI Brain Tumor Segmentation (BraTS) dataset. However this dataset is not divided into WHO-defined LGG and HGG, since it combines grades I, II and III as “lower grades gliomas”, while its HGG category only presents grade IV glioblastoma multiform. In this paper we want to train a binary grade classifier and investigate the consistency of the original BraTS labels with radiologic criteria using machine-aided predictions.Material and methodsUsing WHO-based radiomic features, we trained a SVM classifier on the BraTS dataset, and used the prediction score histogram to investigate the behaviour of our classifier on the lower grade population. We also asked 5 expert radiologists to annotate BraTS images between low (as opposed to lower) grade and high grade glioma classes, resulting in a new groundtruth.ResultsOur first training reached 84.1% accuracy. The prediction score histogram allows us to identify the radiologically high grade patients among the original lower grade population of the BraTS dataset. Training another SVM on our new radiologically WHO-aligned groundtruth shows robust performances despite important class imbalance, reaching 82.4% accuracy.ConclusionOur results highlight the coherence of radiologic criteria for low grade versus high grade classification under WHO terms. We also show how the histogram of prediction scores and crossed prediction scores can be used as tools for data exploration and performance evaluation. Therefore, we propose to use our radiological groundtruth for future development on binary glioma grading.     

Automatic particle selection from electron micrographs using machine learning techniques

The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.     

Improved spark and ember detection using stationary wavelet transforms

Calcium sparks and embers are localized intracellular events of calcium release in muscle cells studied frequently by confocal microscopy using line-scan imaging. The large quantity of images and large number of events require automatic detection procedures based on signal processing methods. In the past decades these methods were based on thresholding procedures. Although, recently, wavelet transforms were also introduced, they have not become widespread. We have implemented a set of algorithms based on one- and two-dimensional versions of the à trous wavelet transform. The algorithms were used to perform spike filtering, denoising and detection procedures. Due to the dependence of the algorithms on user adjustable parameters, their effect on the efficiency of the algorithm was studied in detail. We give methods to avoid false positive detections which are the consequence of the background noise in confocal images. In order to establish the efficiency and reliability of the algorithms, various tests were performed on artificial and experimental images. Spark parameters (amplitude, full width-at-half maximum) calculated using the traditional and the wavelet methods were compared. We found that the latter method is capable of identifying more events with better accuracy on experimental images. Furthermore, we extended the wavelet-based transform from calcium sparks to long-lasting small-amplitude events as calcium embers. The method not only solved their automatic detection but enabled the identification of events with small amplitude that otherwise escaped the eye, rendering the determination of their characteristic parameters more accurate.     

Automatic assignment of protein backbone resonances by direct spectrum inspection in targeted acquisition of NMR data

The necessity to acquire large multidimensional datasets, a basis for assignment of NMR resonances, results in long data acquisition times during which substantial degradation of a protein sample might occur. Here we propose a method applicable for such a protein for automatic assignment of backbone resonances by direct inspection of multidimensional NMR spectra. In order to establish an optimal balance between completeness of resonance assignment and losses of cross-peaks due to dynamic processes/degradation of protein, assignment of backbone resonances is set as a stirring criterion for dynamically controlled targeted nonlinear NMR data acquisition. The result is demonstrated with the 12 kDa ¹³C,¹⁵ N-labeled apo-form of heme chaperone protein CcmE, where hydrolytic cleavage of 29 C-terminal amino acids is detected. For this protein, 90 and 98% of manually assignable resonances are automatically assigned within 10 and 40 h of nonlinear sampling of five 3D NMR spectra, respectively, instead of 600 h needed to complete the full time domain grid. In addition, resonances stemming from degradation products are identified. This study indicates that automatic resonance assignment might serve as a guiding criterion for optimal run-time allocation of NMR resources in applications to proteins prone to degradation. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Orientation of the green flagellate, Euglena gracilis, in a vertical column of water

Abstract The orientation of the green flagellate, Euglena gracilis , in a vertical column immersed in a pond was studied using automatic cell counting based on computerized image analysis. When exposed to solar radiation, the population moved downward in the column, probably guided by negative phototaxis, and formed a dense layer at the bottom. It is suggested that this behavior provides an opportunity for the organisms to escape from detrimental bright light. The downward movement is faster than the swimming speed of the cells allows and could be accelerated by a fluid mechanic effect. The upward movement observed at night may be due to the precise negative gravitaxis observed in the organisms. These antagonistic types of behavior allow the organisms to actively search for and to stay in areas with suitable conditions.     

Pollen-limited seed set in pseudogamous blackberries (Rubus L. subgen. Rubus)

Summary Pseudogamous blackberry species are polyploid and usually exhibit meiotic irregularities causing severe reduction in pollen viability. When species means were compared, relative seed set was strongly correlated with the number of good pollen grains produced per flower divided by the number of ovules (good-pollen/ovule ratio).Partial correlation analysis for percentage viable pollen and relative seed set, controlling for good-pollen/ovule ratio, revealed an almost significant relationship, presumably because meiotic irregularities have a similar effect on both pollen viability and the viability of meiotically derived embryo sacs. Seed germination, on the other hand, showed no relationship with relative seed set or with pollen viability.The automatic selfing capacity exhibited by most of these blackberry species does not appear to be related to either pollen production or relative seed set.     

中药鳖甲及两种炮制品的氨基酸营养学

本文报导了鳖甲及其炮制品的氨基酸营养分析情况。结果表明,生品含17种游离氨基酸,传统炮制品含15种游离氨基酸,食用菌炮制品含16种游离氨基酸,且生品总氨基酸的含量明显高于炮制品,经盐酸水解后,炮制前后的样品均测得17种氨基酸,生品的含量略高于炮制品,炮制品前后样品中所测游离氨基酸和水解后氨基酸均含有8种人体必需氨基酸。     

Clean-in-place systems for industrial bioreactors: Design,validation and operation

Summary Guidelines for design, validation and operation of clean-in-place systems for industrial fermentation plant are presented. Design of vessels, surface finishes, materials of construction, types and locations of valves are some of the considerations addressed. Requisite levels of turbulence for cleaning of pipes and vessels are discussed as well as typical cleaning sequences. Recommendations for validation of cleaning are presented and the significance of design of cleaning systems in ensuring satisfactory validation is pointed out. To the extent possible, validation of cleaning should be carried out with real process soil or soil closely simulating actual fermentation broths.