Longitudinal Imaging of Tumor Perfusion After Preoperative Endovascular Embolization in Meningiomas: Surgical Time Window Selecting, Clinical Consideration, and Outcomes.

OBJECTIVE: To quantitatively investigate the longitudinal computed tomography perfusion (CTP) imaging in meningiomas preoperatively embolized using microcatheters. METHODS: This retrospective monocentric study included 27 patients with symptomatic supratentorial meningiomas. Quantitative computed tomography perfusion (CTP) images before and postembolization were evaluated and correlated with angiographic, immunohistochemical, and clinical data. RESULTS: The mean age of the patients was 45 ± 18 years, with a female-to-male ratio of 1.45:1. After embolization, both the embolized (Eb) and unembolized (UEb) regions showed hypoperfusion. A steady state was achieved on days 4-6 postembolization, during which differences in regional cerebral blood volume (rCBV) (Eb 0.5 ± 0.3 ml/100 mg, UEb 3.3 ± 1.4 ml/100 mg; P < 0.05), and mean transit time (MTT) (Eb 3.5 ± 1.8 s, UEb 3.1 ± 0.4 s) were observed. The cerebral blood flow (rCBF) and time to the peak (TTP) exhibited opposite patterns between Eb and UEb. A steady state was reached in rCBF (Eb 1.7 ± 1.2 ml/100 g/min, UEb 30 ± 5.4 ml/100 g/min; P < 0.01), and TTP (Eb 5 ± 4.8 s, UEb 1.8 ± 1.5 s; P < 0.01) within 4 to 6 days. Estimated blood loss (EBL) showed significant association with the surgical time interval among the 3 groups (P < 0.05). Tissue necrosis predominated over 7 days postembolization, indicating a correlation with the devascularization process. The overall incidence of postembolized headache, seizures, extremity weakness/paralysis, and postoperational headache was 11.1%, 7.4%, 3.7%; and 7.4%, respectively. All symptoms resolved by the last follow-up (3 months). CONCLUSION: Preoperative embolization of meningiomas using N-butyl cyanoacrylate effectively induced significant and sustained tissue transformation and decreased estimated blood loss (EBL) over 7 days. Hemodynamic fluctuations tended to stabilize within 4 to 6 days.

CoP/Co heterojunction on porous g-C3N4 nanosheets as a highly efficient catalyst for hydrogen generation.

Designing non-precious catalysts to synergistically achieve a facilitated exposure of abundant active sites is highly desired but remains a significant challenge. Herein, a hetero-structured catalyst CoP-Co supported on porous g-C3N4 nanosheets (CoP-Co/CN-I) was prepared by pyrolysis and P-inducing strategy. The optimal catalyst achieves a turnover frequency (TOF) of 26 min-1 at room temperature and the apparent activation energy (Ea) is 35.5 kJ·mol-1. The catalytic activity is ranked top among the non-precious metal phosphides or the other supports. Meanwhile, the catalytic activity has no significant decrease even after 5 cycles. The CoP/Co interfaces provide richly exposed active sites, optimize hydrogen/water absorption free energy via electronic coupling, and thus improve the catalytic activity. The experimental results reveal that the CoP/Co heterojunction improves the catalytic activity due to the construction of dual-active sites. This research facilitates the innovative construction of non-noble metal catalysts to meet industrial demand for heterogeneous catalysis.

Precise Design and Modification Engineering of Single-Atom Catalytic Materials for Oxygen Reduction.

Due to their unique electronic and structural properties, single-atom catalytic materials (SACMs) hold great promise for the oxygen reduction reaction (ORR). Coordinating environmental and engineering strategies is the key to improving the ORR performance of SACMs. This review summarizes the latest research progress and breakthroughs of SACMs in the field of ORR catalysis. First, the research progress on the catalytic mechanism of SACMs acting on ORR is reviewed, including the latest research results on the origin of SACMs activity and the analysis of pre-adsorption mechanism. The study of the pre-adsorption mechanism is an important breakthrough direction to explore the origin of the high activity of SACMs and the practical and theoretical understanding of the catalytic process. Precise coordination environment modification, including in-plane, axial, and adjacent site modifications, can enhance the intrinsic catalytic activity of SACMs and promote the ORR process. Additionally, several engineering strategies are discussed, including multiple SACMs, high loading, and atomic site confinement. Multiple SACMs synergistically enhance catalytic activity and selectivity, while high loading can provide more active sites for catalytic reactions. Overall, this review provides important insights into the design of advanced catalysts for ORR.

Urolithin B Attenuates Cerebral Ischemia-reperfusion Injury by Modulating Nrf2-regulated Anti-oxidation in Rats.

Ischemia-reperfusion (IR) induces a wide range of irreversible injuries. Cerebral IR injury (IRI) refers to additional brain tissue damage that occurs after blood flow is restored following cerebral ischemia. Currently, no established methods exist for treating IRI. Oxidative stress is recognized as a primary mechanism initiating IRI and a crucial focal target for its treatment. Urolithin B, a metabolite derived from ellagitannins, antioxidant polyphenols, has demonstrated protective effects against oxidative stress in various disease conditions. However, the precise mechanism underlying UB's effect on IRI remains unclear. In our current investigation, we assessed UB's ability to mitigate neurological functional impairment induced by IR using a neurological deficit score. Additionally, we examined cerebral infarction following UB administration through TTC staining and neuron Nissl staining. UB's inhibition of neuronal apoptosis was demonstrated through the TUNEL assay and Caspase-3 measurement. Additionally, we examined UB's effect on oxidative stress levels by analyzing malondialdehyde (MDA) concentration, superoxide dismutase (SOD) activity, and immunohistochemistry analysis of inducible nitric oxide synthase (iNOS) and 8-hydroxyl-2'-deoxyguanosine (8-OHdG). Notably, UB demonstrated a reduction in oxidative stress levels. Mechanistically, UB was found to stimulate the Nrf2/HO-1 signaling pathway, as evidenced by the significant reduction in UB's neuroprotective effects upon administration of ATRA, an Nrf2 inhibitor. In summary, UB effectively inhibits oxidative stress induced by IR through the activation of the Nrf2/HO-1 signaling pathway. These findings suggest that UB holds promise as a therapeutic agent for the treatment of IRI.

Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized [Formula: see text] nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as [Formula: see text]-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA-disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference.

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

Microbial characterization based on multifractal analysis of metagenomes.

Introduction: The species diversity of microbiomes is a cutting-edge concept in metagenomic research. In this study, we propose a multifractal analysis for metagenomic research. Method and Results: Firstly, we visualized the chaotic game representation (CGR) of simulated metagenomes and real metagenomes. We find that metagenomes are visualized with self-similarity. Then we defined and calculated the multifractal dimension for the visualized plot of simulated and real metagenomes, respectively. By analyzing the Pearson correlation coefficients between the multifractal dimension and the traditional species diversity index, we obtain that the correlation coefficients between the multifractal dimension and the species richness index and Shannon diversity index reached the maximum value when q = 0, 1, and the correlation coefficient between the multifractal dimension and the Simpson diversity index reached the maximum value when q = 5. Finally, we apply our method to real metagenomes of the gut microbiota of 100 infants who are newborn and 4 and 12 months old. The results show that the multifractal dimensions of an infant's gut microbiomes can distinguish age differences. Conclusion and Discussion: There is self-similarity among the CGRs of WGS of metagenomes, and the multifractal spectrum is an important characteristic for metagenomes. The traditional diversity indicators can be unified under the framework of multifractal analysis. These results coincided with similar results in macrobial ecology. The multifractal spectrum of infants' gut microbiomes are related to the development of the infants.

Emergency Angioplasty or Stenting for Stroke Patients with Intracranial Atherosclerotic Large Vessel Occlusion.

AIM: Mechanical thrombectomy (MT) has become the gold standard for the treatment of large vessel occlusion (LVO) in acute ischemic stroke. However, it remains controversial whether emergency angioplasty or stenting in patients with intracranial atherosclerotic stenosis (ICAS) should be adopted. Thus, we performed a retrospective analysis of clinical data to determine whether emergency angioplasty or stenting is necessary. METHODS: We retrospectively analyzed data from patients undergoing MT with ICAS-related LVO of the acute anterior circulation between 2017 and 2019. Eligible patients were divided into two treatment groups: those who received rescue angioplasty or stenting [Patients treated with rescue angioplasty or stenting (PTAS) group] and those who received thrombectomy alone (non-PTAS group). The primary outcomes were good prognosis at 90 days (mRS: 0-2). Mortality, symptomatic intracranial hemorrhage, and reocclusion rate were evaluated as secondary outcomes. RESULTS: A total of 184 patients with severe stenosis after MT were enrolled, including 64 patients receiving rescue angioplasty or stenting and 120 patients without rescue angioplasty or stenting. Compared with the non-PTAS group, a better functional outcome (mRS0-2) (51.6% vs. 35.0%, adjusted odds ratio: 2.11, 95% confidence interval [CI]: 1.22-4.29; P=0.02), lower 7-day National Institutes of Health Stroke Scale [6 (3-12.75) vs. 10 (4-16); P=0.04], lower 24-h neurological deterioration rate (7.8% vs. 21.7%, P=0.02), and lower 24-h reocclusion rate were observed in the PTAS group (6.3% vs. 17.5%, P=0.03). There were no significant differences in mortality or incidence of symptomatic intracerebral hemorrhage. CONCLUSION: Emergency angioplasty or stenting could be a safe and feasible therapeutic option with better outcomes for stroke patients with ICAS-related LVO.

A Study of the Impact of Strategic Human Resource Management on Organizational Resilience.

Organizational resilience is a key capability for modern firms to survive and thrive in the VUCA environment. The purpose of this study is to investigate the mechanism of strategic human resource management on organizational resilience and the mediating and moderating roles of self-efficacy and self-management, respectively, in the relationship between the two. A total of 379 valid questionnaires were obtained from employees of Chinese companies in August 2022, and the data were analyzed using SPSS 22.0 and Amos. The results showed that strategic HRM can effectively contribute to organizational resilience; self-efficacy plays a mediating role in the relationship between strategic HRM and organizational resilience; self-management can effectively contribute to the impact of self-efficacy on organizational resilience; and self-management can hinder the ability of strategic HRM to contribute to organizational resilience. This paper breaks with the previous literature that studied organizational resilience from a single perspective by studying organizational resilience from the perspective of strategic human resource management (SHRM) and verifies that SHRM can be a possible path for Chinese firms to improve organizational resilience.

Nucleosome positioning based on DNA sequence embedding and deep learning.

BACKGROUND: Nucleosome positioning is the precise determination of the location of nucleosomes on DNA sequence. With the continuous advancement of biotechnology and computer technology, biological data is showing explosive growth. It is of practical significance to develop an efficient nucleosome positioning algorithm. Indeed, convolutional neural networks (CNN) can capture local features in DNA sequences, but ignore the order of bases. While the bidirectional recurrent neural network can make up for CNN's shortcomings in this regard and extract the long-term dependent features of DNA sequence. RESULTS: In this work, we use word vectors to represent DNA sequences and propose three new deep learning models for nucleosome positioning, and the integrative model NP_CBiR reaches a better prediction performance. The overall accuracies of NP_CBiR on H. sapiens, C. elegans, and D. melanogaster datasets are 86.18%, 89.39%, and 85.55% respectively. CONCLUSIONS: Benefited by different network structures, NP_CBiR can effectively extract local features and bases order features of DNA sequences, thus can be considered as a complementary tool for nucleosome positioning.

Engineering Bimodal Oxygen Vacancies and Pt to Boost the Activity Toward Water Dissociation.

Water dissociation is the rate-limiting step of several energy-related reactions due to the high energy barrier required for breaking the oxygen-hydrogen bond. In this work, a bimodal oxygen vacancy (VO ) catalysis strategy is adopted to boost the efficient water dissociation on Pt nanoparticles. The single facet-exposed TiO2 surface and NiOx nanocluster possess two modes of VO different from each other. In ammonia borane hydrolysis, the highest catalytic activity among Pt-based materials is achieved with the turnover frequency of 618 min-1 under alkaline-free conditions at 298 K. Theoretical simulation and characterization analyses reveal that the bimodal VO significantly promotes the water dissociation in two ways. First, an ensemble-inducing effect of Pt and VO in TiO2 drives the activation of water molecules. Second, an electron promoter effect induced by the electron transfer from VO in NiOx to Pt further enhances the ability of Pt to dissociate water and ammonia borane. This insight into bimodal VO catalysis establishes a new avenue to rationally design heterogeneous catalytic materials in the energy chemistry field.

An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction.

Alignment methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational costs in handling time and space complexity. On the other hand, alignment-free methods incur low computational costs and have recently gained popularity in the field of bioinformatics. Here we propose a new alignment-free method for phylogenetic tree reconstruction based on whole genome sequences. A key component is a measure called information-entropy position-weighted k-mer relative measure (IEPWRMkmer), which combines the position-weighted measure of k-mers proposed by our group and the information entropy of frequency of k-mers. The Manhattan distance is used to calculate the pairwise distance between species. Finally, we use the Neighbor-Joining method to construct the phylogenetic tree. To evaluate the performance of this method, we perform phylogenetic analysis on two datasets used by other researchers. The results demonstrate that the IEPWRMkmer method is efficient and reliable. The source codes of our method are provided at https://github.com/ wuyaoqun37/IEPWRMkmer.

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.

Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It is even more important to reveal associations for currently unassociated miRNAs and diseases. Methods have been recently proposed to make predictions on the association types of miRNA-disease pairs through restricted Boltzman machines, label propagation theories and tensor completion algorithms. None of them has exploited the non-linear characteristics in the miRNA-disease association network to improve the performance. We propose to use attributed multi-layer heterogeneous network embedding to learn the latent representations of miRNAs and diseases from each association type and then to predict the existence of the association type for all the miRNA-disease pairs. The performance of our method is compared with two newest methods via 10-fold cross-validation on the database HMDD v3.2 to demonstrate the superior prediction achieved by our method under different settings. Moreover, our real predictions made beyond the HMDD database can be all validated by NCBI literatures, confirming that our method is capable of accurately predicting new associations of miRNAs with diseases and their association types as well.

Wood-Derived Integral Air Electrode for Enhanced Interfacial Electrocatalysis in Rechargeable Zinc-Air Battery.

Zinc-air batteries (ZABs) are promising as energy storage devices owing to their high energy density and the safety of electrolytes. Construction of abundant triple-phase boundary (TPB) effectively facilitates cathode reactions occurring at TPB. Herein, a wood-derived integral air electrode containing Co/CoO nanoparticles and nitrogen-doped carbonized wood (Co/CoO@NWC) is constructed with a dual catalytic function. The potential gap between oxygen reduction and evolution is shortened to 0.77 V. Liquid ZABs using Co/CoO@NWC as cathode exhibit high discharge specific capacity (800 mAh gZn-1 ), low charge-discharge gap (0.84 V), and long-term cycling stability (270 h). Co/CoO@NWC also shows distinguished catalytic activity and stability in all-solid-state ZABs. The inherent layered porous and pipe structures of wood are well maintained in catalytically active carbon. The different hydrophilicity of carbonized wood and Co/CoO endow abundant TPBs for battery reaction. The Co/CoO located on TPB provides main active sites for oxygen reactions. The inherent pipe structures of wood carbon and the interaction between Co/CoO and NWC effectively prevent nanoparticles from aggregation. The design and preparation of this monolithic electrocatalyst contribute to the broad-scale application of ZABs and promote the development of next-generation biomass-based storage devices.

The Impact of Mental Health Status on Health Consumption of the Elderly in China.

Based on the national baseline survey data of the CHARLS2015, the comprehensive evaluation index of depression degree of the elderly in China is calculated using a principal component analysis method. The Tobit model is used to investigate the influence of mental health status with depression degree as a proxy variable on the health consumption of the elderly in China. The results show that the overall effect and the phased effect of depression on the health consumption of the elderly are positive. In other words, high levels of depression lead to a higher probability and amount of health consumption. Research findings show that chronic illness and increased social activity can weaken the effect of depression on the health spending of the elderly. Fully considering the factors affecting the mental health of elderly people is required to improve their level of mental health. Importantly, the quality audit system of health products and the supervision and management system of the health consumption market should be improved to realize the benign operation of the health consumption market.

Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms.

BACKGROUND: Nucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning. RESULTS: Here, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved. CONCLUSIONS: Comparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.

Multiscale adaptive multifractal analysis and its applications.

To precisely analyze the fractal nature of a short-term time series under the multiscale framework, this study introduces multiscale adaptive multifractal analysis (MAMFA) combining the adaptive fractal analysis method with the multiscale multifractal analysis (MMA). MAMFA and MMA are both applied to the two kinds of simulation sequences, and the results show that the MAMFA method achieves better performances than MMA. MAMFA is also applied to the Chinese and American stock indexes and the R-R interval of heart rate data. It is found that the multifractal characteristics of stock sequences are related to the selection of the scale range s. There is a big difference in the Hurst surface's shape of Chinese and American stock indexes and Chinese stock indexes have more obvious multifractal characteristics. For the R-R interval sequence, we find that the subjects with abnormal heart rate have significant shape changes in three areas of Hurst surface compared with healthy subjects, thereby patients can be effectively distinguished from healthy subjects.

Phylogenetic Analysis of HIV-1 Genomes Based on the Position-Weighted K-mers Method.

HIV-1 viruses, which are predominant in the family of HIV viruses, have strong pathogenicity and infectivity. They can evolve into many different variants in a very short time. In this study, we propose a new and effective alignment-free method for the phylogenetic analysis of HIV-1 viruses using complete genome sequences. Our method combines the position distribution information and the counts of the k-mers together. We also propose a metric to determine the optimal k value. We name our method the Position-Weighted k-mers (PWkmer) method. Validation and comparison with the Robinson-Foulds distance method and the modified bootstrap method on a benchmark dataset show that our method is reliable for the phylogenetic analysis of HIV-1 viruses. PWkmer can resolve within-group variations for different known subtypes of Group M of HIV-1 viruses. This method is simple and computationally fast for whole genome phylogenetic analysis.

A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies.

Genome-wide association study (GWAS) has turned out to be an essential technology for exploring the genetic mechanism of complex traits. To reduce the complexity of computation, it is well accepted to remove unrelated single nucleotide polymorphisms (SNPs) before GWAS, e.g., by using iterative sure independence screening expectation-maximization Bayesian Lasso (ISIS EM-BLASSO) method. In this work, a modified version of ISIS EM-BLASSO is proposed, which reduces the number of SNPs by a screening methodology based on Pearson correlation and mutual information, then estimates the effects via EM-Bayesian Lasso (EM-BLASSO), and finally detects the true quantitative trait nucleotides (QTNs) through likelihood ratio test. We call our method a two-stage mutual information based Bayesian Lasso (MBLASSO). Under three simulation scenarios, MBLASSO improves the statistical power and retains the higher effect estimation accuracy when comparing with three other algorithms. Moreover, MBLASSO performs best on model fitting, the accuracy of detected associations is the highest, and 21 genes can only be detected by MBLASSO in Arabidopsis thaliana datasets.