Modulating the activity of CRISPR/Cas9 genome editing by small molecules

The use of the bacterial-derived CRISPR/Cas9 genome editing system offers enormous opportunities to treat human genetic diseases. However, the potency of CRISPR/Cas9 is limited by the low precise genome editing efficiency and continuous activity of Cas9. The development of cell permeable small molecules is promising approach for precise control of Cas9 activity and may greatly enhance the application of genome editing in academic, industrial, and clinical settings. In this thesis, we found that the efficiency of CRISPR/Cas9-mediated genome editing can be modulated by histone deacetylases (HDACs) inhibitors. In order to identify potential Cas9 inhibitors and enhancers we developed a living cell-based high-throughput screening platform. Using this assay, we identified two novel potent small molecule Cas9 inhibitors that switch off Cas9 activity as well as a new potent Cas9 enhancer. These molecules inhibited/enhanced the CRISPR/Cas9-mediated genome editing in biochemical, eukaryotic, and prokaryotic cell studies. Interestingly, the potent Cas9 enhancer was obtained by a minor variation from one of the Cas9 inhibitors. Next, we showed that the Cas9 inhibitors and enhancer can modify the Cas9 activity by binding at the RuvC active site of the Cas9 endonuclease. Our results provide a practical and possibly clinically applicable way to precisely modulate CRISPR/Cas9-mediated genome editing activity using small molecules. These findings also gain new insight into the interactions between small molecules and the Cas9 protein and may extend the application of genome editing techniques

Formation of Low-Molecular-Weight Dissolved Organic Nitrogen in two-stage and four-stage Pre-denitrification Biological Nutrient Removal Processes

To alleviate the eutrophication caused by excessive loading of nutrients, the upgrading of conventional activated sludge (CAS) process to biological nutrients removal (BNR) process has been widely applied in USA. In this study, we found that the dissolved inorganic nitrogen (DIN) release can be effectively controlled by this upgrading, but dissolved organic nitrogen (DON) especially LMW-DON, which now is regarded as another important N source for supporting growth of phytoplankton in coastal water, cannot be removed effectively by BNR systems, especially by four-stage BNR systems. Different pre-denitrification BNR processes have different LMW-DON production rates. A four-stage pre-denitrification BNR releases more LMW-DON in effluent than two-stage pre-denitrification BNR. The higher DON production may be caused by longer anaerobic time. Also, the characteristics of influent influence the formation of LMW-DON in BNR system. Influent with acetate and higher COD concentration can stimulate more DON and LMW-DON release in a BNR process. This suggests that relative regulation should be established to prevent the release of DON. A post-treatment method should be added to remove DON produced by the BNR process

Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201

Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males

Background Patterns of dyslipidemia and incidence of hypertension have been rarely reported in Asian populations with inconsistent findings. To accumulate further evidence in Asian populations, the study aimed to investigate the relationship between lipid profiles and hypertension in Chinese adult males. Methods We conducted a cross-sectional study based on the data from the DATADRYAD database. The overall population was divided into hypertensive and non-hypertensive groups based on baseline blood pressure levels. For continuous variables, Mann-Whitney test was performed between two groups, while Kruskal-Wallis and Dunn tests were used among multiple groups. The chi-square test was carried out for dichotomous variables. Spearman's correlation coefficient was employed to assess the association between systolic blood pressure (SBP), diastolic blood pressure (DBP) and lipid profiles, whereas the relationship between lipid profiles and the incidence of hypertension was evaluated using multivariate logistic regression. The Bayesian network (BN) model was adopted to investigate the relationship between clinical characteristics and hypertension, and the importance of related predictor to the incidence of hypertension was obtained to make conditional probability analysis. Results Finally, totally 62,957 participants were included in this study. In the lipid profiles, total cholesterol (TC), low-density cholesterol (LDL-c), and non- high-density lipoprotein cholesterol (non-HDL-c) were higher in the hypertensive population (p <0.001). In the fully multivariate model, for every 1 mg/dl increase in TC, LDL-c and non-HDL, the risk of hypertension increased by 0.2% [1.002 (1.001–1.003)], 0.1% [1.001 (1.000–1.002)], and 0.1% [1.001 (1.000–1.002)]. Meanwhile, HDL-c became positively associated with the incidence of hypertension (p for trend < 0.001) after adjusting for the body mass index (BMI), and 1 mg/dl increment in HDL-c increased the risk of hypertension by 0.2% [1.002 (1.000–1.002)] after fully adjusting for multiple variables. Furthermore, the BN showed that the importance of age, BMI, fasting plasma glucose (FPG), and TC to the effect of hypertension is 43.3, 27.2, 11.8, and 5.1%, respectively. Conclusion Elevated TC, LDL-c, and non-HDL-c were related to incidence of hypertension in Chinese adult males, whereas triglycerides (TG) was not significantly associated. The relationship between HDL-c and hypertension incidence shifted from no association to a positive correlation after adjusting for the BMI. Moreover, the BN model displayed that age, the BMI, FPG, and TC were strongly associated with hypertension incidence

Thompson Sampling for Combinatorial Semi-Bandits

We study the application of the Thompson sampling (TS) methodology to the stochastic combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm for the general CMAB, and obtain the first distribution-dependent regret bound of $O(mK_{\max}\log T / \Delta_{\min})$, where $m$ is the number of arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution. We also show that one cannot directly replace the exact offline oracle with an approximation oracle in TS algorithm for even the classical MAB problem. Then we expand the analysis to two special cases: the linear reward case and the matroid bandit case. When the reward function is linear, the regret of the TS algorithm achieves a better bound $O(m\sqrt{K_{\max}}\log T / \Delta_{\min})$. For matroid bandit, we could remove the independence assumption across arms and achieve a regret upper bound that matches the lower bound for the matroid case. Finally, we use some experiments to show the comparison between regrets of TS and other existing algorithms like CUCB and ESCB

Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation

Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper, HF to be estimated is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF), and we propose a novel image super-resolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively. Extensive experimental results on test images validate that by employing the proposed two-layer progressive scheme, more image details can be recovered and much better results can be achieved than the state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 4 pages, 4 figures, 1 table, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201

Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for AMI have not been identified. In order to explore the potential diagnostic biomarkers and possible regulatory targets of AMI, we used a network analysis-based approach to analyze microarray expression profiling of peripheral blood in patients with AMI. The significant differentially-expressed genes (DEGs) were screened by Limma and constructed a gene function regulatory network (GO-Tree) to obtain the inherent affiliation of significant function terms. The pathway action network was constructed, and the signal transfer relationship between pathway terms was mined in order to investigate the impact of core pathway terms in AMI. Subsequently, constructed the transcription regulatory network of DEGs. Weighted gene co-expression network analysis (WGCNA) was employed to identify significantly altered gene modules and hub genes in two groups. Subsequently, the transcription regulation network of DEGs was constructed. We found that specific gene modules may provide a better insight into the potential diagnostic biomarkers of AMI. Our findings revealed and verified that NCF4, AQP9, NFIL3, DYSF, GZMA, TBX21, PRF1 and PTGDR genes by RT-qPCR. TBX21 and PRF1 may be potential candidates for diagnostic biomarker and possible regulatory targets in AMI