CAS9 is a genome mutator by directly disrupting DNA-PK dependent DNA repair pathway.

With its high efficiency for site-specific genome editing and easy manipulation, the clustered regularly interspaced short palindromic repeats (CRISPR)/ CRISPR associated protein 9 (CAS9) system has become the most widely used gene editing technology in biomedical research. In addition, significant progress has been made for the clinical development of CRISPR/CAS9 based gene therapies of human diseases, several of which are entering clinical trials. Here we report that CAS9 protein can function as a genome mutator independent of any exogenous guide RNA (gRNA) in human cells, promoting genomic DNA double-stranded break (DSB) damage and genomic instability. CAS9 interacts with the KU86 subunit of the DNA-dependent protein kinase (DNA-PK) complex and disrupts the interaction between KU86 and its kinase subunit, leading to defective DNA-PK-dependent repair of DNA DSB damage via non-homologous end-joining (NHEJ) pathway. XCAS9 is a CAS9 variant with potentially higher fidelity and broader compatibility, and dCAS9 is a CAS9 variant without nuclease activity. We show that XCAS9 and dCAS9 also interact with KU86 and disrupt DNA DSB repair. Considering the critical roles of DNA-PK in maintaining genomic stability and the pleiotropic impact of DNA DSB damage responses on cellular proliferation and survival, our findings caution the interpretation of data involving CRISPR/CAS9-based gene editing and raise serious safety concerns of CRISPR/CAS9 system in clinical application

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data

5-{2-(4-Chloro­phen­yl)-1-[2-(4-chloro­phen­yl)-1-(3,4,5-trimeth­oxy­phen­yl)eth­oxy]eth­yl}-1,2,3-trimeth­oxy­benzene

The title compound, C34H36Cl2O7, is a by-product from the reaction of 4-chloro­benzyl­zinc chloride with 3,4,5-trimeth­oxy­benzaldehyde. In each of the two 1,2-diphenyl­ethyl moieties, the two benzene rings are arranged in a trans conformation and make Car—C—C—Car torsion angles of 163.64 (19) and 174.43 (18)°. The crystal structure is stabilized by van der Waals inter­actions only

Genetic approach to track neural cell fate decisions using human embryonic stem cells

With their capability to undergo unlimited self-renewal and to differentiate into all cell types in the body, human embryonic stem cells (hESCs) hold great promise in human cell therapy. However, there are limited tools for easily identifying and isolating live hESC-derived cells. To track hESC-derived neural progenitor cells (NPCs), we applied homologous recombination to knock-in the mCherry gene into the Nestin locus of hESCs. This facilitated the genetic labeling of Nestin positive neural progenitor cells with mCherry. Our reporter system enables the visualization of neural induction from hESCs both in vitro (embryoid bodies) and in vivo (teratomas). This system also permits the identification of different neural subpopulations based on the intensity of our fluorescent reporter. In this context, a high level of mCherry expression showed enrichment for neural progenitors, while lower mCherry corresponded with more committed neural states. Combination of mCherry high expression with cell surface antigen staining enabled further enrichment of hESC-derived NPCs. These mCherry(+) NPCs could be expanded in culture and their differentiation resulted in a down-regulation of mCherry consistent with the loss of Nestin expression. Therefore, we have developed a fluorescent reporter system that can be used to trace neural differentiation events of hESCs

Understanding the Multiple Dimensions of Residential Choice

At the time of publication, X. Fu was at the Shanghai Jiao Tong University, C.R. Bhat at the University of Texas at Austin, R.M. Pendyala at Georgia Institute of Technology, S. Vladlamani and V.M Garikapati at Arizona State University.Residential choice may be characterized as a household’s simultaneous decisions of location, neighborhood, and dwelling. Traditional models do not account for the latent unmeasured constructs which capture individuals’ preferences for and attitudes towards residence and mode choice. This paper employs Bhat’s (2014) Generalized Heterogeneous Data Model (GHMD) to accommodate five inter-related residential choice dimensions, including residential location, neighborhood land-use pattern, public transportation availability, housing type, and dwelling ownership. Four latent variables including pro-driving, pro-public transportation, facility availability, and residential spaciousness are constructed to capture individuals’ attitudes towards travel modes and preferences for residential features. The inclusion of these latent constructs helps account for self-selection effects in residential choice processes. The determination of relationships among multiple dimensions of residential choice behavior, socio-demographics, and latent attitudes and preferences is critical to integrated land use – transport modeling and the formulation of policies as well as urban residential and neighborhood environments that cater to individual preferences and enhance quality of life.Civil, Architectural, and Environmental Engineerin

EEG-based emotion classification using spiking neural networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications

MTR4 drives liver tumorigenesis by promoting cancer metabolic switch through alternative splicing.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC