When somatic cells acquire complex karyotypes, they often are removed by the immune system. Mutant somatic cells that evade immune surveillance can lead to cancer. Neurons with complex karyotypes arise during neurotypical brain development, but neurons are almost never the origin of brain cancers. Instead, somatic mutations in neurons can bring about neurodevelopmental disorders, and contribute to the polygenic landscape of neuropsychiatric and neurodegenerative disease. A subset of human neurons harbors idiosyncratic copy number variants (CNVs, "CNV neurons"), but previous analyses of CNV neurons are limited by relatively small sample sizes. Here, we develop an allele-based validation approach, SCOVAL, to corroborate or reject read-depth based CNV calls in single human neurons. We apply this approach to 2,125 frontal cortical neurons from a neurotypical human brain. SCOVAL identifies 226 CNV neurons, which include a subclass of 65 CNV neurons with highly aberrant karyotypes containing whole or substantial losses on multiple chromosomes. Moreover, we find that CNV location appears to be nonrandom. Recurrent regions of neuronal genome rearrangement contain fewer, but longer, genes.

When somatic cells acquire complex karyotypes, they are removed by the immune system. Mutant somatic cells that evade immune surveillance can lead to cancer. Neurons with complex karyotypes arise during neurotypical brain development, but neurons are almost never the origin of brain cancers. Instead, somatic mutations in neurons can bring about neurodevelopmental disorders, and contribute to the polygenic landscape of neuropsychiatric and neurodegenerative disease. A subset of human neurons harbors idiosyncratic copy number variants (CNVs, "CNV neurons"), but previous analyses of CNV neurons have been limited by relatively small sample sizes. Here, we developed an allele-based validation approach, SCOVAL, to corroborate or reject read-depth based CNV calls in single human neurons. We applied this approach to 2,125 frontal cortical neurons from a neurotypical human brain. This approach identified 226 CNV neurons, as well as a class of CNV neurons with complex karyotypes containing whole or substantial losses on multiple chromosomes. Moreover, we found that CNV location appears to be nonrandom. Recurrent regions of neuronal genome rearrangement contained fewer, but longer, genes.

2042 Background: Malignant gliomas are heterogeneous diseases in genetic basis. The development of sequencing techniques, such as RNA-Sequencing, has identified many gene rearrangements encoding novel oncogenic fusions. Gene fusion discovery can potentially lead to the development of novel treatments, however studies of gene fusions in glioma remain limited. Methods: The GLIOCAT project studied 139 patient samples of newly diagnosed glioblastoma who had received the standard first-line treatment from 2004 to 2015, to identify gene fusion events from glioblastoma transcriptome data (RNA-Seq). The molecular subtype could be studied in 124 cases. RNA-Seq reads were mapped against the reference human genome with STAR-fusion version 0.7.0, specifically, with FusionInspector validate ( http://star-fusion.github.io ). Two other platforms, FusionHub ( https://fusionhub.persistent.co.in ) and Oncofuse ( www.unav.es/genetica/oncofuse.html ), were applied to eliminate false positives or previously described in healthy tissue and to predict of the oncogenic potential each fusion. Results: A total of61 patients showed 103 different fusions, a median of two fusions by sample. The majority of gene fusions were intrachromosomal and most frequently implied chromosome was 12 followed by 7. In addition, fusions were more common in patients with MGMT promoter methylation, TCGA classical subtype and 18 IGS subtype. There were no differences in age, sex, type of surgery or long survivors ( > 30 months). Ten fusions were already described in cancer, including three in gliomas (FRS2-KIF5A, EGFR-SEPT14 and FGFR3-TACC3). From the detected fusions, 22 of them included an oncogene or protooncogene. Conclusions: In our study, we report the landscape of gene fusions from a large data set of glioblastomas analyzed by RNA-seq. The majority of the fusions were private fusions. A minority of these recur in a low frequency but as many as a quarter of them included an oncogene or protooncogene. RNA-seq of GBM patient samples it is an important tool for the identification of patient-specific fusions that could drive personalized therapy. Furtherless, we will plan to validate this gene fusions.

This paper proposed a deep learning model for automatic evaluation of academic engagement based on video data analysis. A coding system based on the BROMP standard for behavioral, emotional, and cognitive states was defined to code typical videos in an autonomous learning environment. Then after the key points of human skeletons were extracted from these videos using pose estimation technology, deep learning methods were used to realize the effective recognition and judgment of motion and emotions. Based on this, an analysis and evaluation of learners' learning states was accomplished, and a prototype of academic engagement evaluation system was successfully established eventually.

In this paper a side-channel-attack resistant AES system with a variation-tolerant true Random Number Generator (tRNG) is implemented using IBM 0.13μm CMOS technology. As the random source for the AES, a meta-stability based tRNG takes advantage of an all-digital self-calibration method to compensate Process-Voltage-Temperature (PVT) variations, and thus guarantees output with extremely high randomness. To quantify performance of the proposed tRNG, NIST tests along with other specification measures are performed. The results show improvements over previous work in bit rate, energy efficiency, and pattern randomness. The proposed AES adopts a rotating S-boxes masking technique to gain high resistance against first and zero-offset second order side channel attacks, including Differential Power Analysis (DPA). The masked AES is simulated under first order and zero-offset second order DPA, showing significant resistance against these attacks.