Neurodegenerative diseases encompass a wide variety of pathological conditions caused by a loss of neurons in the central nervous system (CNS) and are severely debilitating. Exosome contains bio-signatures of great diagnostic and therapeutic value. There is proof that exosomal proteins can be biomarkers for Alzheimer's disease (AD) and Parkinson's disease (PD). MicroRNAs in exosome has potential to be an important source of biomarkers for neurodegenerative diseases. Here, we report exosomal microRNA performance of human plasma in neurodegenerative diseases by small RNA sequencing. A wide range of altered exo-miRNA expression levels were detected in both AD and PD patients. Down-regulated miRNAs in AD samples were enriched in ECMreceptor interaction pathway and both up-/down-regulated miRNAs in PD samples were enriched in fatty acid biosynthesis pathway. Compared to the control, 8 miRNAs were found to be significantly elevated/declined in AD and PD samples, of which 4 miRNAs were newly identified. Additionally, two exosome isolating methods were compared and the reproducibility of plasma exo-miRNA expression was confirmed, suggesting the feasibility of large-scale clinical application of this method. This study revealed exo-miRNA expression levels in neurodegenerative diseases, proposed new biomarkers and their potential functional pathway for AD and PD, confirmed the reproductivity of exo-miRNA profiles by using a different exosome isolating method, and compared the results with plasma miRNA expression. Therefore, this study also provides a precedent for identifying exosomal biomarkers of neurodegenerative diseases in plasma by high-throughput sequencing and it could extend the therapeutic repertoire of exosomal biomarkers.

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To evaluate the diagnostic value of genome sequencing in children with epilepsy, and to provide genome sequencing-based insights into the molecular genetic mechanisms of epilepsy to help establish accurate diagnoses, design appropriate treatments, and assist in genetic counseling. We performed genome sequencing on 320 Chinese children with epilepsy, and interpreted single nucleotide variants and copy number variants of all samples. The complete pedigree and clinical data of the probands were established and followed up. The clinical phenotypes, treatments, prognoses, and genotypes of the patients were analyzed. Age at seizure onset ranged from 1 day to 17 years, with a median of 4.3 years. Pathogenic/likely pathogenic variants were found in 117 of the 320 children (36.6%), of whom 93 (29.1%) had single nucleotide variants, 22 (6.9%) had copy number variants, and 2 had both single nucleotide variants and copy number variants. Single nucleotide variants were most frequently found in SCN1A (10/95, 10.5%), which is associated with Dravet syndrome, followed by PRRT2 (8/95, 8.4%), which is associated with benign familial infantile epilepsy, and TSC2 (7/95, 7.4%), which is associated with tuberous sclerosis. Among the copy number variants, there were 3 with a length < 25Kb. The most common recurrent copy number variants were 17p13.3 deletions (5/24, 20.8%), 16p11.2 deletions (4/24, 16.7%), and 7q11.23 duplications (2/24, 8.3%), which are associated with epilepsy, developmental retardation, and congenital abnormalities. Four particular 16p11.2 deletions and two 15q11.2 deletions were considered to be susceptibility factors contributing to neurodevelopmental disorders associated with epilepsy. The diagnostic yield was 75.0% in patients with seizure onset during the first postnatal month, and gradually decreased in patients with seizure onset at a later age. Forty-two patients (13.1%) were found to be specifically treatable for the underlying genetic cause identified by genome sequencing. Three of them received corresponding targeted therapies and demonstrated favorable prognoses. Genome sequencing provides complete genetic diagnosis, thus enabling individualized treatment and genetic counseling for the parents of the patients. Genome sequencing is expected to become the first choice of methods for genetic testing of patients with epilepsy.

Abstract.With the ongoing development of rendering technology, computer graphics (CG) are sometimes so photorealistic that to distinguish them from photographic images (PG) by human eyes has become difficult. To this end, many methods have been developed for automatic CG and PG classification. In this paper, we explore the statistical difference of uniform gray-scale invariant local binary patterns (LBP) to distinguish CG from PG with the help of support vector machines (SVM). We select YCbCr as the color model. The original JPEG coefficients of Y and Cr components, and their prediction errors are used for LBP calculation. From each 2-D array, we obtain 59 LBP features. In total, four groups of 59 features are obtained from each image. The proposed features have been tested with thousands of CG and PG. Classification accuracy reaches 98.3% with SVM and outperforms the state-of-the-art works.

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