MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

GNNMF: a multi-view graph neural network for ATAC-seq motif finding.

BACKGROUND: The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) utilizes the Transposase Tn5 to probe open chromatic, which simultaneously reveals multiple transcription factor binding sites (TFBSs) compared to traditional technologies. Deep learning (DL) technology, including convolutional neural networks (CNNs), has successfully found motifs from ATAC-seq data. Due to the limitation of the width of convolutional kernels, the existing models only find motifs with fixed lengths. A Graph neural network (GNN) can work on non-Euclidean data, which has the potential to find ATAC-seq motifs with different lengths. However, the existing GNN models ignored the relationships among ATAC-seq sequences, and their parameter settings should be improved. RESULTS: In this study, we proposed a novel GNN model named GNNMF to find ATAC-seq motifs via GNN and background coexisting probability. Our experiment has been conducted on 200 human datasets and 80 mouse datasets, demonstrated that GNNMF has improved the area of eight metrics radar scores of 4.92% and 6.81% respectively, and found more motifs than did the existing models. CONCLUSIONS: In this study, we developed a novel model named GNNMF for finding multiple ATAC-seq motifs. GNNMF built a multi-view heterogeneous graph by using ATAC-seq sequences, and utilized background coexisting probability and the iterloss to find different lengths of ATAC-seq motifs and optimize the parameter sets. Compared to existing models, GNNMF achieved the best performance on TFBS prediction and ATAC-seq motif finding, which demonstrates that our improvement is available for ATAC-seq motif finding.

Heterogeneous changes in gut and tumor microbiota in patients with pancreatic cancer: insights from clinical evidence.

BACKGROUND: Pancreatic cancer is the foremost contributor to cancer-related deaths globally, and its prevalence continues to rise annually. Nevertheless, the underlying mechanisms behind its development remain unclear and necessitate comprehensive investigation. METHODS: In this study, a total of 29 fresh stool samples were collected from patients diagnosed with pancreatic cancer. The gut microbial data of healthy controls were obtained from the SRA database (SRA data number: SRP150089). Additionally, 28 serum samples and diseased tissues were collected from 14 patients with confirmed pancreatic cancer and 14 patients with chronic pancreatitis. Informed consent was obtained from both groups of patients. Microbial sequencing was performed using 16s rRNA. RESULTS: The results showed that compared with healthy controls, the species abundance index of intestinal flora in patients with pancreatic cancer was increased (P < 0.05), and the number of beneficial bacteria at the genus level was reduced (P < 0.05). Compared with patients with chronic pancreatitis, the expression levels of CA242 and CA199 in the serum of patients with pancreatic cancer were increased (P < 0.05). The bacterial richness index of tumor microorganisms in patients with pancreatic cancer increased, while the diversity index decreased(P < 0.05). Furthermore, there was a change in the species composition at the genus level. Additionally, the expression level of CA242 was found to be significantly positively correlated with the relative abundance of Acinetobacter(P < 0.05). CONCLUSION: Over all, the expression levels of serum tumor markers CA242 and CA19-9 in patients with pancreatic cancer are increased, while the beneficial bacteria in the intestine and tumor microenvironment are reduced and pathogenic bacteria are increased. Acinetobacter is a specific bacterial genus highly expressed in pancreatic cancer tissue.

Retinal ganglion cell type-specific expression of synuclein family members revealed by scRNA-sequencing.

Synuclein family members (Snca, Sncb, and Scng) are expressed in the retina, but their precise locations and roles are poorly understood. We performed an extensive analysis of the single-cell transcriptome in healthy and injured retinas to investigate their expression patterns and roles. We observed the expression of all synuclein family members in retinal ganglion cells (RGCs), which remained consistent across species (human, mouse, and chicken). We unveiled differential expression of Snca across distinct clusters (highly expressed in most), while Sncb and Sncg displayed uniform expression across all clusters. Further, we observed a decreased expression in RGCs following traumatic axonal injury. However, the proportion of α-Syn-positive RGCs in all RGCs and α-Syn-positive intrinsically photosensitive retinal ganglion cells (ipRGCs) in all ipRGCs remained unaltered. Lastly, we identified changes in communication patterns preceding cell death, with particular significance in the pleiotrophin-nucleolin (Ptn-Ncl) and neural cell adhesion molecule signaling pathways, where communication differences were pronounced between cells with varying expression levels of Snca. Our study employs an innovative approach using scRNA-seq to characterize synuclein expression in health retinal cells, specifically focusing on RGC subtypes, advances our knowledge of retinal physiology and pathology.

Intravenous immunoglobulin for treatment of hospitalized COVID-19 patients: an evidence mapping and meta-analysis.

BACKGROUND: The clinical efficacy and safety of intravenous immunoglobulin (IVIg) treatment for COVID-19 remain controversial. This study aimed to map the current status and gaps of available evidence, and conduct a meta-analysis to further investigate the benefit of IVIg in COVID-19 patients. METHODS: Electronic databases were searched for systematic reviews/meta-analyses (SR/MAs), primary studies with control groups, reporting on the use of IVIg in patients with COVID-19. A random-effects meta-analysis with subgroup analyses regarding study design and patient disease severity was performed. Our outcomes of interest determined by the evidence mapping, were mortality, length of hospitalization (days), length of intensive care unit (ICU) stay (days), number of patients requiring mechanical ventilation, and adverse events. RESULTS: We included 34 studies (12 SR/MAs, 8 prospective and 14 retrospective studies). A total of 5571 hospitalized patients were involved in 22 primary studies. Random-effects meta-analyses of very low to moderate evidence showed that there was little or no difference between IVIg and standard care or placebo in reducing mortality (relative risk [RR] 0.91; 95% CI 0.78-1.06; risk difference [RD] 3.3% fewer), length of hospital (mean difference [MD] 0.37; 95% CI - 2.56, 3.31) and ICU (MD 0.36; 95% CI - 0.81, 1.53) stays, mechanical ventilation use (RR 0.92; 95% CI 0.68-1.24; RD 2.8% fewer), and adverse events (RR 0.98; 95% CI 0.84-1.14; RD 0.5% fewer) of patients with COVID-19. Sensitivity analysis using a fixed-effects model indicated that IVIg may reduce mortality (RR 0.76; 95% CI 0.60-0.97), and increase length of hospital stay (MD 0.68; 95% CI 0.09-1.28). CONCLUSION: Very low to moderate certainty of evidence indicated IVIg may not improve the clinical outcomes of hospitalized patients with COVID-19. Given the discrepancy between the random- and fixed-effects model results, further large-scale and well-designed RCTs are warranted.

Manipulating the Spin State of Spinel Octahedral Sites via a π-π Type Orbital Coupling to Boost Water Oxidation.

Spin state is often regarded as the crucial valve to release the reactivity of energy-related catalysts, yet it is also challenging to precisely manipulate, especially for the active center ions occupied at the specific geometric sites. Herein, a π-π type orbital coupling of 3d (Co)-2p (O)-4f (Ce) was employed to regulate the spin state of octahedral cobalt sites (CoOh) in the composite of Co3O4/CeO2. More specifically, the equivalent high-spin ratio of CoOh can reach to 54.7% via tuning the CeO2 content, thereby triggering the average eg filling (1.094) close to the theoretical optimum value. The corresponding catalyst exhibits a superior water oxidation performance with an overpotential of 251 mV at 10 mA cm-2, rivaling most cobalt-based oxides state-of-the-art. The π-π type coupling corroborated by the matched energy levels between Ce t1u/t2u-O and CoOh t2g-O π type bond in the calculated crystal orbital Hamilton population and partial density of states profiles, stimulates a π-donation between O 2p and π-symmetric Ce 4fyz2 orbital, consequently facilitating the electrons hopping from t2g to eg orbital of CoOh. This work offers an in-depth insight into understanding the 4f and 3d orbital coupling for spin state optimization in composite oxides.

MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data.

MOTIVATION: Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data. RESULTS: We develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools. AVAILABILITY AND IMPLEMENTATION: MMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Synthesis, Optical Properties, and Applications of Luminescent Benzothiazole: Base Promoted Intramolecular C-S Bond Formation.

A novel base-catalyzed method for the synthesis of luminescent benzothiazole derivatives had been developed under metal-free conditions via C-S bond formation, which provided an efficient, convenient, and mild alternative method for constructing substituted benzothiazoles. As-prepared benzothiazole derivatives thus produced emissions in solution with quantum yield up to 85%. In addition, they still exhibited fairly strong fluorescence in the solid state. Furthermore, the compounds were used as a facile "On-Off" fluorescence probe to create handy test strips for detecting NaClO by naked eyes.

Statistical Analysis of Two-Compartment Pharmacokinetic Models with Drug Non-adherence.

Poor drug adherence is considered one of major barriers to achieving the clinical and public health benefits of many pharmacotherapies. In the current paper, we aim to investigate the impact of dose omission on the plasma concentrations of two-compartment pharmacokinetic models with two typical routes of drug administration, namely the intravenous bolus and extravascular first-order absorption. First, we reformulate the classical two-compartment pharmacokinetic models with a new stochastic feature, where a binomial random model of dose intake is integrated. Then, we formalize the explicit expressions of expectation and variance for trough concentrations and limit concentrations, with the latter proved of the existence and uniqueness for steady-state distribution. Moreover, we mathematically demonstrate the strict stationarity and ergodicity of trough concentrations as a Markov chain. In addition, we numerically simulate the impact of drug non-adherence to different extents on the variability and regularity of drug concentration and compare the drug pharmacokinetic preference between one and two compartment pharmacokinetic models. The results of sensitivity analysis also suggest the drug non-adherence as one of the most sensitive model parameters to the expectation of limit concentration. Our modelling and analytical approach can be integrated into the chronic disease models to estimate or quantitatively predict the therapy efficacy with drug pharmacokinetics presumably affected by random dose omissions.

Filter design and color correction for the X-cube prism 3CCD camera.

For the X-cube prism three-charge-coupled-device (3CCD) camera, the spectra of the designed dichroic films in the X-cube prism shift with changes in the angle of incident light, producing non-uniformity of color on the image plane. We considered the influence of the incident angle on color performance in filter design and directly optimized the thin film to improve color consistency. An optical model was constructed to calculate the distribution of camera spectral sensitivity and independently correct the non-uniform color on the image plane. Results showed that the optimization and correction methods could significantly improve the color performance of the X-cube prism 3CCD camera.

Second Malignant Tumors and Non-Tumor Causes of Death for Patients With Penile Cancer During Their Survivorship.

BACKGROUND: The aim was to evaluate the causes of death for patients with localized, regional and metastatic penile cancer (PeCa) after diagnosis. METHODS: PeCa patients diagnosed during 2004-2018 in the Surveillance, Epidemiology, and End Results program database were identified. Causes of deaths including PeCa, second malignant tumors (SMTs) and non-tumor diseases were analyzed, as well as the standardized mortality ratio (SMR) of each cause. RESULTS: For localized PeCa, 800 of 2155 patients died during the follow-up. 24.9% of all deaths were due to PeCa. 18.0% and 57.1% deaths were due to SMTs and non-tumor causes. Main SMTs included cancers of lung and bronchus (n = 40) and skin (n = 11) with significantly increased SMRs of 1.71 (1.22-2.33) and 4.82 (2.41-8.63). Mortality risks of other SMTs were mostly similar with the general populations. Main causes of non-tumor diseases included diseases of heart [n = 172, SMR: 1.66 (1.42-1.93)], COPD and allied cond [n = 38, SMR: 1.63 (1.15-2.24)], and cerebrovascular diseases [n = 33, SMR: 1.71 (1.17-2.4)]. For regional PeCa, 679 of 1310 patients died including 43.5% PeCa, 14.8% SMTs and 26.6% non-tumor causes. The mortality risks of cancers from lung and bronchus [SMR: 2.41 (1.53-3.62)], skin [SMR: 6.41 (2.35-13.95)] and testis [SMR: 149.35 (18.09-539.5)] were significantly increased. Main non-tumor causes of death included diseases of heart [n = 71, SMR: 1.77 (1.38-2.23)], COPD and allied cond [n = 17, SMR: 1.85 (1.08-2.95)] and diabetes mellitus [n = 16, SMR: 3.62 (2.07-5.88)]. For distant diseases, 109 of 132 patients died including 76 (69.7%) died for PeCa itself, 24 (22.0%) died for SMTs and 9 (8.3%) died for non-tumor diseases. The majority of PeCa deaths (67.1%) and SMTs deaths (79.2%) occurred within 1 year after the diagnosis of PeCa. CONCLUSIONS: We firstly analyzed the SMTs and non-tumor causes of death and morality risks of each cause for PeCa patients, which provided valuable information for PeCa patients on disease prevention and health care during their survivorship.

An Analytical Approach of One-Compartmental Pharmacokinetic Models with Sigmoidal Hill Elimination.

In this study, we aim to develop the analytical solutions of one-compartment pharmacokinetic models with sigmoidal Hill elimination and quantitatively revisit some widely used pharmacokinetic indexes. For this purpose, we have first established the closed-form solutions of the model with intravenous bolus administration through introducing a transcendent H function, which is proved a generalized form of the Lambert W function. Then, in the case of a single dose, we have obtained the explicit formulas for several pharmacokinetic surrogates, such as the clearance, elimination half-life and partial/total drug exposure. All these surrogates are found concentration-dependent and sensitive to the Hill coefficient [Formula: see text]. Meanwhile, in establishing the closed-form formulas for multiple repeated dosing regimens, we have discovered phase transitions for steady states with different ranges of [Formula: see text] in function of the lengths of dosing intervals. Further, our results are illustrated with two drug examples. To conclude, the present findings elucidate the intrinsic quantitative structural properties of pharmacokinetic models with Hill elimination and provide new knowledge for nonlinear pharmacokinetics and guidance for rational drug designs.

Statistical analysis of one-compartment pharmacokinetic models with drug adherence.

Pharmacokinetics is a scientific branch of pharmacology that describes the time course of drug concentration within a living organism and helps the scientific decision-making of potential drug candidates. However, the classical pharmacokinetic models with the eliminations of zero-order, first-order and saturated Michaelis-Menten processes, assume that patients perfectly follow drug regimens during drug treatment, and the significant factor of patients' drug adherence is not taken into account. In this study, therefore, considering the random change of dosage at the fixed dosing time interval, we reformulate the classical deterministic one-compartment pharmacokinetic models to the framework of stochastic, and analyze their qualitative properties including the expectation and variance of the drug concentration, existence of limit drug distribution, and the stochastic properties such as transience and recurrence. In addition, we carry out sensitivity analysis of drug adherence-related parameters to the key values like expectation and variance, especially for the impact on the lowest and highest steady state drug concentrations (i.e. the therapeutic window). Our findings can provide an important theoretical guidance for the variability of drug concentration and help the optimal design of medication regimens. Moreover, The developed models in this paper can support for the potential study of the impact of drug adherence on long-term treatment for chronic diseases like HIV, by integrating disease models and the stochastic PK models.

Protective effect of Corynoline on the CFA induced Rheumatoid arthritis via attenuation of oxidative and inflammatory mediators.

Rheumatoid arthritis (RA) is a long-standing and growing autoimmune disease. Therefore, the present study was intended to investigate the effect of Corynoline (COR) on CFA induced rheumatoid arthritis in a rat model. Results suggested that COR causes significant reduction in paw swelling, edema, arthritis score, thymus and spleen indexes and neutrophil infiltration (p < 0.01). Moreover, the levels of inflammatory cytokines (interleukin- 1ß, -6, and -17, and TNF-α) and anti-collagen II-specific immunoglobulins (IgG1 and IgG2a) were decreased significantly (p < 0.01) together with increase in antioxidant enzymes (SOD, CAT, and GSH) (p < 0.01) in COR-treated group in dose-dependent manner. In western blot analysis, COR-treated group showed concentration-dependent reduction of expression of COX-2, 5-LOX and NF-p65 as compared to CFA rats. Moreover, COR-treated group showed mild inflammation of cartilage with fewer cartilage erosion and synovititis with most significant reversal of arthritic features in the rats treated with 30 mg/kg. It has been concluded that, COR alleviates oxidative stress and inflammation in arthritic rats, thus verifying its anti-rheumatoid arthritis property.

CRISPR/dual-FRET molecular beacon for sensitive live-cell imaging of non-repetitive genomic loci.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based genomic imaging systems predominantly rely on fluorescent protein reporters, which lack the optical properties essential for sensitive dynamic imaging. Here, we modified the CRISPR single-guide RNA (sgRNA) to carry two distinct molecular beacons (MBs) that can undergo fluorescence resonance energy transfer (FRET) and demonstrated that the resulting system, CRISPR/dual-FRET MB, enables dynamic imaging of non-repetitive genomic loci with only three unique sgRNAs.

Constant infusion case of one compartment pharmacokinetic model with simultaneous first-order and Michaelis-Menten elimination: analytical solution and drug exposure formula.

The main objective of this article is to propose the closed-form solution of one-compartment pharmacokinetic model with simultaneous first-order and Michaelis-Menten elimination for the case of constant infusion. For the case of bolus administration, we have previously established a closed-form solution of the model through introducing a transcendent X function. In the same vein, we found here a closed-form solution of constant infusion could be realized through introducing another transcendent Y function. For the general case of constant infusion of limited duration, the closed-form solution is then fully expressed using both X and Y functions. As direct results, several important pharmacokinetic surrogates, such as peak concentration [Formula: see text] and total drug exposure AUC[Formula: see text], are found the closed-form expressions and ready to be analyzed. The new pharmacokinetic knowledge we have gained on these parameters, which largely exhibits in a nonlinear feature, is in clear contrast to that of the linear case. Finally, with a pharmacokinetic model adapted from that formerly reported on phenytoin, we numerically analyzed and illustrated the roles of different model parameters and discussed their influence on drug exposure. To conclude, the present findings elucidate the intrinsic quantitative structural properties of such pharmacokinetic model and provide a new avenue for future modelling and rational drug designs.

RRG-GAN Restoring Network for Simple Lens Imaging System.

The simple lens computational imaging method provides an alternative way to achieve high-quality photography. It simplifies the design of the optical-front-end to a single-convex-lens and delivers the correction of optical aberration to a dedicated computational restoring algorithm. Traditional single-convex-lens image restoration is based on optimization theory, which has some shortcomings in efficiency and efficacy. In this paper, we propose a novel Recursive Residual Groups network under Generative Adversarial Network framework (RRG-GAN) to generate a clear image from the aberrations-degraded blurry image. The RRG-GAN network includes dual attention module, selective kernel network module, and residual resizing module to make it more suitable for the non-uniform deblurring task. To validate the evaluation algorithm, we collect sharp/aberration-degraded datasets by CODE V simulation. To test the practical application performance, we built a display-capture lab setup and reconstruct a manual registering dataset. Relevant experimental comparisons and actual tests verify the effectiveness of our proposed method.

Effects of ultrasound pretreatment on the drying kinetics, water status and distribution in scallop adductors during heat pump drying.

BACKGROUND: A material's physical and chemical properties during drying are influenced by water status and distribution. However, merely overall water removal is reported in many investigations, which hinders clarification of the drying mechanism. Therefore, the effects of ultrasound (US) pretreatment (0 W, CK; 90 W, US-90; 180 W, US-180) on the drying kinetics and quality of heat pump drying (HPD) scallop adductors was performed based on low-field nuclear magnetic resonance (LF-NMR). RESULTS: Compared with CK, effective moisture diffusion coefficient was increased by 12.43% and 23.35% for US-90 and US-180, respectively. The Weibull model satisfactorily described the drying characteristics with a high r2 (> 0.998), low rmse (< 0.0120) and χ2 (< 0.00008). LF-NMR revealed that the immobilized water was predominant in scallop adductors. As drying proceeded, the relaxation time of free and immobilized water was decreased sharply, whereas the relaxation time of bound water scarcely changed. The time required to reduce approximately two-fifths of the original peak area of immobilized water was 720, 630 and 540 min for CK, US-90 and US-180, respectively. The amplitude of immobilized water was decreased and bound water increased significantly, although free water was kept constant (ranging 1-2%). US pretreatment reduced total color difference and hardness, but enhanced the toughness of dried scallop adductors. However, US had no significant influence on the product rehydration rate and shrinkage rate. CONCLUSION: LF-NMR was successfully employed to evaluate the drying degree of scallop adductors. US facilitated the conversion of immobilized water to free water and, consequently, promoted water removal during HPD. © 2021 Society of Chemical Industry.

Metal-Free ß-Amino Alcohol Synthesis: A Two-step Smiles Rearrangement.

A novel method for the synthesis of ß-amino alcohols has been demonstrated under mild reaction conditions with a broad scope via a two-step Smiles rearrangement. What is more, theoretical calculations have been performed to confirm the rationality of the mechanism. The method has been proved to be notably effective for N-arylated amino alcohols, which are difficult to synthesize by traditional methods.

A CRISPR/molecular beacon hybrid system for live-cell genomic imaging.

The clustered regularly interspersed short palindromic repeat (CRISPR) gene-editing system has been repurposed for live-cell genomic imaging, but existing approaches rely on fluorescent protein reporters, making sensitive and continuous imaging difficult. Here, we present a fluorophore-based live-cell genomic imaging system that consists of a nuclease-deactivated mutant of the Cas9 protein (dCas9), a molecular beacon (MB), and an engineered single-guide RNA (sgRNA) harboring a unique MB target sequence (sgRNA-MTS), termed CRISPR/MB. Specifically, dCas9 and sgRNA-MTS are first co-expressed to target a specific locus in cells, followed by delivery of MBs that can then hybridize to MTS to illuminate the target locus. We demonstrated the feasibility of this approach for quantifying genomic loci, for monitoring chromatin dynamics, and for dual-color imaging when using two orthogonal MB/MTS pairs. With flexibility in selecting different combinations of fluorophore/quencher pairs and MB/MTS sequences, our CRISPR/MB hybrid system could be a promising platform for investigating chromatin activities.