Drosophila JIL-1 kinase mediates histone H3 Ser10 phosphorylation, maintains higher order chromatin structure, and is implicated in dosage compensation

The Drosophila JIL-1 tandem Ser/Thr kinase is associated with chromosomes/chromatin throughout the cell cycle in early embryos and localized to hundreds of sites on the open interband regions on third instar larvae polytene chromosomes. Interestingly, the level of JIL-1 is upregulated on the male X chromosome, which is hypertranscribed for dosage compensation. The distribution of JIL-1 overlaps with that of MSL (male specific lethal) proteins and JIL-1 is associated with the MSL complex.;To further study the function of JIL-1 in vivo, a series of JIL-1 mutants from hypomorphs to null were generated. Analyzing the phenotypes of JIL-1 mutants, we found that JIL-1 is required for the viability of both females and males. Moreover, in the surviving flies of JIL-1 hypomorphic mutants, the number of males is less than that of females, implicating that JIL-1 plays a role in dosage compensation. JIL-1 is also required during early embryonic development and for the maintenance of normal higher order polytene chromosome structure in third instar larvae.;Our studies also showed that JIL-1 is involved in the histone H3 Ser10 phosphorylation signaling pathway. The JIL-1 immunocomplex can phosphorylate the Ser10 site in a synthetic H3 N-terminal peptide in vitro. In vivo, Ser10 phosphorylation levels in the third instar larvae of JIL-1 mutants are dramatically reduced. In addition, the decreased H3 Ser10 phosphorylation level can be restored with a GFP-JIL-1 transgene. Moreover, the phosphorylated H3 Ser10 is elevated and colocalized with JIL-1 on the male X chromosome.;Thus, our data suggest a model whereby JIL-1 is involved in a signaling pathway that regulates histone H3 Ser10 phosphorylation in D. melanogaster , which is required to maintain the appropriate higher order chromatin structure to facilitate chromatin functions, such as gene expression, dosage compensation, and so on

Brain Tumor Detection Based on a Novel and High-Quality Prediction of the Tumor Pixel Distributions

In this paper, we propose a system to detect brain tumor in 3D MRI brain scans of Flair modality. It performs 2 functions: (a) predicting gray-level and locational distributions of the pixels in the tumor regions and (b) generating tumor mask in pixel-wise precision. To facilitate 3D data analysis and processing, we introduced a 2D histogram presentation that comprehends the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, particular 2D histograms, in which tumor-related feature data get concentrated, are established by exploiting the left-right asymmetry of a brain structure. A modulation function is generated from the input data of each patient case and applied to the 2D histograms to attenuate the element irrelevant to the tumor regions. The prediction of the tumor pixel distribution is done in 3 steps, on the axial, coronal and sagittal slice series, respectively. In each step, the prediction result helps to identify/remove tumor-free slices, increasing the tumor information density in the remaining data to be applied to the next step. After the 3-step removal, the 3D input is reduced to a minimum bounding box of the tumor region. It is used to finalize the prediction and then transformed into a 3D tumor mask, by means of gray level thresholding and low-pass-based morphological operations. The final prediction result is used to determine the critical threshold. The proposed system has been tested extensively with the data of more than one thousand patient cases in the datasets of BraTS 2018~21. The test results demonstrate that the predicted 2D histograms have a high degree of similarity with the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs, which is achieved at an extremely low computation cost and no need for training

A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

In this paper, a Convolutional Neural Network (CNN) system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN(ASCNN) to segment tumor areas precisely, and a refinement block to detect/remove false positive pixels. The CNN, designed specifically for the task, has 7 convolution layers, 16 channels per layer, requiring only 11716 parameters. The convolutions combined with max-pooling in the first half of the CNN are performed to localize tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. For a fine classification of pixel-wise precision in the second half of the CNN, the feature maps are modulated by adding the individually weighted local feature maps generated in the first half of the CNN. The performance of the proposed system has been evaluated by an online platform with dataset of Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and also by the median Dice scores of 0.85, 0.92, and 0.86. The consistency in system performance has also been measured, demonstrating that the system is able to reproduce almost the same output to the same input after retraining. The simple structure of the proposed system facilitates its implementation in computation restricted environment, and a wide range of applications can thus be expected