Functional bugs within blockchain systems have led to financial losses exceeding millions of dollars. Blockchain systems, such as Ethereum, play a critical role in supporting decentralized applications and managing significant financial assets. Despite the urgent need for enhanced security, relatively few studies have systematically investigated the functional bugs specific to blockchain systems. Unlike in conventional software, functional bugs in blockchain systems are often domain-specific and linked to core blockchain functionalities, necessitating a comprehensive understanding.In this study, we conduct a systematic analysis of functional bugs in Ethereum Virtual Machine (EVM)-based blockchain systems. We focus on the EVM-based architecture, as it is one of the most widely adopted models for blockchain systems. Specifically, we analyze bug-related issues reported in the GitHub repositories of leading blockchain systems, building a dataset of 205 real-world bugs classified into 18 categories. We investigate these collected bugs with respect to their taxonomies, root causes, and detection methods. From this analysis, we summarize eight key findings for enhancing blockchain security. The discovery of seven previously unknown bugs, yielding approximately $12,000 in bug bounties, demonstrates the practical impact of this work. A further investigation of state-of-the-art tools highlights the limitations of existing detection and analysis research.

Ethereum has officially provided a set of system-level cryptographic APIs to enhance smart contracts with cryptographic capabilities. These APIs have been utilized in over 10% of Ethereum transactions, motivating developers to implement various on-chain cryptographic tasks, such as digital signatures. However, since developers may not always be cryptographic experts, their ad-hoc and potentially defective implementations could compromise the theoretical guarantees of cryptography, leading to real-world security issues. To mitigate this threat, we conducted the first study aimed at demystifying and detecting cryptographic defects in smart contracts. Through the analysis of 2,406 real-world security reports, we defined nine types of cryptographic defects in smart contracts with detailed descriptions and practical detection patterns. Based on this categorization, we proposed Crysol, a fuzzing-based tool to automate the detection of cryptographic defects in smart contracts. It combines transaction replaying and dynamic taint analysis to extract fine-grained crypto-related semantics and employs crypto-specific strategies to guide the test case generation process. Furthermore, we collected a large-scale dataset containing 25,745 real-world crypto-related smart contracts and evaluated CRYSOL's effectiveness on it. The result demonstrated that CRySOL achieves an overall precision of 95.4% and a recall of 91.2%. Notably, CRySOL revealed that 5,847 (22.7%) out of 25,745 smart contracts contain at least one crvptographic defect” hiahlighting the prevalence of these defects.

Cross-chain bridges are essential decentralized applications (DApps) to facilitate interoperability between different blockchain networks. Unlike regular DApps, the functionality of cross-chain bridges relies on the collaboration of information both on and off the chain, which exposes them to a wider risk of attacks. According to our statistics, attacks on cross-chain bridges have resulted in losses of nearly 4.3 billion since 2021. Therefore, it is particularly necessary to understand and detect attacks on cross-chain bridges. In this paper, we collect the largest number of cross-chain bridge attack incidents to date, including 49 attacks that occurred between June 2021 and September 2024, of which 22 were attacks on cross-chain bridge business logic. Our analysis reveal that attacks against cross-chain business logic cause significantly more damage than those that do not. These cross-chain attacks exhibit different patterns compared to normal transactions in terms of call structure, which effectively indicates potential attack behaviors. Given the significant losses in these cases and the scarcity of related research, this paper aims to detect attacks against cross-chain business logic, and propose the BridgeGuard tool. Specifically, BridgeGuard models cross-chain transactions from a graph perspective, and employs a two-stage detection framework comprising global and local graph mining to identify attack patterns in cross-chain transactions. We conduct multiple experiments on the datasets with 203 attack transactions and 40,000 normal cross-chain transactions. The results show that BridgeGuard's reported recall score is 36.32% higher than that of state-of-the-art tools and can detect unknown attack transactions.

The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks.Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types.To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum.MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks.We integrate a novel semantic lifting approach that extracts system behaviors relevant for various attacks, which can be analyzed using the monitoring tool MonPoly.Furthermore, we also equip MoE with quantitative capabilities to evaluate the similarity between a transaction and an attack formula to enhance its performance in identifying attacks, including near-miss attacks.We carry out extensive experiments with MoE on a labeled benchmark and a large-scale dataset containing over one million transactions.On the labeled benchmark, MoE successfully detects 92.0% attacks and achieves a 45.0% higher recall rate than competing state-of-the-art tool.MoE finds 3,319 attacks with 95.4% precision on the large dataset.Furthermore, MoE uses quantitative analysis to uncover 8% additional attacks.Finally, the average time for * Xinyao Xu and Ziyu Mao contributed equally.

Smart contracts are Turing-complete programs that run on blockchain technology, capable of managing on-chain assets according to predefined logic, and become immutable once deployed on the blockchain. In recent years, the value of smart contracts on blockchains, notably Ethereum, has been on the rise. However, the hiding vulnerabilities made the substantial value of smart contracts a target of many hackers, leading to numerous attack incidents. Therefore, vulnerability detection in smart contracts before deployment is essential. Currently, many fuzzers for detecting smart contract vulnerabilities can only identify vulnerabilities based on the execution patterns of the underlying opcodes, overlooking the financial semantic properties of the contracts, which leads to many vulnerabilities being difficult to detect or resulting in a high rate of false positives. To this end, we focus on the financial characteristics of contracts, define contract vulnerability patterns starting from the high-level semantic properties of contracts, and combine fuzzers using evolutionary algorithms and symbolic constraint solving to detect vulnerabilities, culminating in the development of FinanceFuzz . Specifically, FinanceFuzz defines invariant and equivalence properties of finance that contracts should satisfy. Utilizing these properties, FinanceFuzz can generate transaction sequences for testing and identify vulnerable contracts that violate the properties. We conducted experiments on a dataset containing 437 smart contracts from the real world, the experimental results demonstrating that our tool outperforms other state-of-the-art tools in detecting vulnerabilities, achieving higher recall rate without false positive.

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attention, showcasing great capabilities in code analysis tasks. In this article, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT’s effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT’s performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT’s F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities.

As decentralized applications (DApps) proliferate, the increased complexity and usage of smart contracts have heightened their susceptibility to security incidents and financial losses. Although various vulnerability detection tools have been developed to mitigate these issues, they often suffer poor performance in detecting vulnerabilities, as they either rely on simplistic and general-purpose oracles that may be inadequate for vulnerability detection, or require user-specified oracles, which are labor-intensive to create. In this paper, we introduce SmartOracle, a dynamic invariant detector that automatically generates fine-grained invariants as application-specific oracles for vulnerability detection. From historical transactions, SmartOracle uses pattern-based detection and advanced inference to construct comprehensive properties, and mines multi-layerlikelyinvariants to accommodate the complicated contract functionalities. After that, SmartOracle identifies smart contract vulnerabilities by hunting the violated invariants in new transactions. In the field of invariant detection, SmartOracle detects 50% more ERC20 invariants than existing dynamic invariant detection and achieves 96% precision rate. Furthermore, we build a dataset that contains vulnerable contracts from real-world security incidents. SmartOracle successfully detects 466 abnormal transactions with an acceptable precision rate 96%, involving 31 vulnerable contracts. The experimental results demonstrate its effectiveness in detecting smart contract vulnerabilities, especially those related to complicated contract functionalities.

MOTIVATION: The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy. RESULTS: We propose a novel beta mixture approach called scDMV for analyzing methylation differences in single-cell bisulfite sequencing data, which effectively handles excess zeros and ones and accommodates low-input sequencing. Our extensive simulation studies demonstrate that the scDMV approach outperforms several alternative methods in terms of sensitivity, precision, and controlling the false positive rate. Moreover, in real data applications, we observe that scDMV exhibits higher precision and sensitivity in identifying differentially methylated regions, even with low-input samples. In addition, scDMV reveals important information for GO enrichment analysis with single-cell whole-genome sequencing data that are often overlooked by other methods. AVAILABILITY AND IMPLEMENTATION: The scDMV method, along with a comprehensive tutorial, can be accessed as an R package on the following GitHub repository: https://github.com/PLX-m/scDMV.

The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open-source audit reports from 29 security teams. In total, we identified 9,154 weaknesses and developed two distinct datasets, i.e., DAPPSCAN-SOURCE and DAPPSCAN-BYTECODE. The DAPPSCAN-SOURCE dataset comprises 39,904 Solidity files, featuring 1,618 SWC weaknesses sourced from 682 real-world DApp projects. However, the Solidity files in this dataset may not be directly compilable for further analysis. To facilitate automated analysis, we developed a tool capable of automatically identifying dependency relationships within DApp projects and completing missing public libraries. Using this tool, we created DAPPSCAN-BYTECODE dataset, which consists of 6,665 compiled smart contract with 888 SWC weaknesses. Based on DAPPSCAN-BYTECODE, we conducted an empirical study to evaluate the performance of state-of-the-art smart contract weakness detection tools. The evaluation results revealed sub-par performance for these tools in terms of both effectiveness and success detection rate, indicating that future development should prioritize real-world datasets over simplistic toy contracts.

Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value().

Decentralized Finance (DeFi) apps have rapidly proliferated with the development of blockchain and smart contracts, whose maximum total value locked (TVL) has exceeded 100 billion dollars in the past few years. These apps allow users to interact and perform complicated financial activities. However, the vulnerabilities hiding in the smart contracts of DeFi apps have resulted in numerous security incidents, with most of them leading to funds (tokens) leaking and resulting in severe financial loss. In this paper, we summarize Token Leaking vulnerability of DeFi apps, which enable someone to abnormally withdraw funds that far exceed their deposits. Due to the massive amount of funds in DeFi apps, it is crucial to protect DeFi apps from Token Leaking vulnerabilities. Unfortunately, existing tools have limitations in addressing this vulnerability. To address this issue, we propose DeFiWarder, a tool that traces on-chain transactions and protects DeFi apps from Token Leaking vulnerabilities. Specifically, DeFiWarder first records the execution logs (traces) of smart contracts. It then accurately recovers token transfers within transactions to catch the funds flow between users and DeFi apps, as well as the relations between users based on role mining. Finally, DeFiWarder utilizes anomaly detection to reveal Token Leaking vulnerabilities and related attack behaviors. We conducted experiments to demonstrate the effectiveness and efficiency of DeFiWarder. Specifically, DeFi-Warder successfully revealed 25 Token Leaking vulnerabilities from 30 Defi apps. Moreover, its efficiency supports real-time detection of token leaking within on-chain transactions. In addition, we summarize five major reasons for Token Leaking vulnerability to assist DeFi apps in protecting their funds.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF’s performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).

As a computer program running on top of blockchain, smart contract not only proliferates the diversity of applications but also brings a myriad of security issues that lead to huge financial losses. As a result, security evaluation of smart contracts, such as vulnerability identification and attack detection, has received extensive attention in recent years. Given that various types of approaches have been proposed for smart contract security analysis, a systematization of knowledge for this domain is needed. To this end, in this paper, we systematically review the related literature in recent years and describe the mainstream approaches to the security evaluation of smart contracts. Specifically, we classify state-of-the-art analysis techniques for smart contract analysis into two categories, namely, code-based approaches and transaction-based approaches. Further, we elaborate on the key techniques adopted by these works respectively. We highlight and summarize the key challenges in future research for smart contract security analysis. Our research provides a more in-depth understanding of the state-of-the-art works for securing smart contracts, which may shed light on future research in this area.

Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged as a diagnostic and prognostic signature in many cancers. However, such DNA methylation signature available in UVM remains limited. In this study, we performed a genome-wide integrative analysis of methylome and transcriptome and identified 40 methylation-driven prognostic genes (MDPGs) associated with the tumorigenesis and progression of UVM. Then, we proposed a machine-learning-based discovery and validation strategy to identify a DNA methylation-driven signature (10MeSig) composing of 10 MDPGs (AZGP1, BAI1, CCDC74A, FUT3, PLCD1, S100A4, SCN8A, SEMA3B, SLC25A38 and SLC44A3), which stratified 80 patients of the discovery cohort into two risk subtypes with significantly different overall survival (HR = 29, 95% CI: 6.7-126, P < 0.001). The 10MeSig was validated subsequently in an independent cohort with 57 patients and yielded a similar prognostic value (HR = 2.1, 95% CI: 1.2-3.7, P = 0.006). Multivariable Cox regression analysis showed that the 10MeSig is an independent predictive factor for the survival of patients with UVM. With a prospective validation study, this 10MeSig will improve clinical decisions and provide new insights into the pathogenesis of UVM.

Long noncoding RNAs (lncRNAs) have been associated with cancer immunity regulation and the tumor microenvironment (TME). However, functions of lncRNAs of tumor-infiltrating B lymphocytes (TIL-Bs) and their clinical significance have not yet been fully elucidated. In the present study, a machine learning-based computational framework is presented for the identification of lncRNA signature of TIL-Bs (named 'TILBlncSig') through integrative analysis of immune, lncRNA and clinical profiles. The TILBlncSig comprising eight lncRNAs (TNRC6C-AS1, WASIR2, GUSBP11, OGFRP1, AC090515.2, PART1, MAFG-DT and LINC01184) was identified from the list of 141 B-cell-specific lncRNAs. The TILBlncSig was capable of distinguishing worse compared with improved survival outcomes across different independent patient datasets and was also independent of other clinical covariates. Functional characterization of TILBlncSig revealed it to be an indicator of infiltration of mononuclear immune cells (i.e. natural killer cells, B-cells and mast cells), and it was associated with hallmarks of cancer, as well as immunosuppressive phenotype. Furthermore, the TILBlncSig revealed predictive value for the survival outcome and immunotherapy response of patients with anti-programmed death-1 (PD-1) therapy and added significant predictive power to current immune checkpoint gene markers. The present study has highlighted the value of the TILBlncSig as an indicator of immune cell infiltration in the TME from a noncoding RNA perspective and strengthened the potential application of lncRNAs as predictive biomarkers of immunotherapy response, which warrants further investigation.

MOTIVATION: The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. RESULTS: A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). AVAILABILITY AND IMPLEMENTATION: The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

MOTIVATION: At present, a fundamental challenge in single-cell RNA-sequencing data analysis is functional interpretation and annotation of cell clusters. Biological pathways in distinct cell types have different activation patterns, which facilitates the understanding of cell functions using single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptome data analysis based on prior biological pathway knowledge. RESULTS: Here, we present scTPA, a web-based platform for pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from a pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Overall, scTPA is a comprehensive tool for the identification of pathway activation signatures for the analysis of single cell heterogeneity. AVAILABILITY AND IMPLEMENTATION: http://sctpa.bio-data.cn/sctpa. CONTACT: [email protected] or [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Increasing evidence has revealed the multiple roles of long noncoding RNAs (lncRNAs) in neurodevelopment, brain function and aging, and their dysregulation was implicated in many types of neurological diseases. However, expression pattern and diagnostic role of lncRNAs in Alzheimer's disease (AD) remain largely unknown and has gained significant attention. In this study, we performed a comparative analysis for lncRNA expression profiles in four brain regions in brain aging and AD. Our analysis revealed age- and disease-dependent region-specific lncRNA expression patterns in aging and AD. Moreover, we identified a panel of nine lncRNAs (termed LncSigAD9) in a discovery cohort of 114 samples using supervised machine learning and stepwise selection method. The LncSigAD9 was able to differentiate between AD and healthy controls with high diagnostic sensitivity and specificity both in the discovery cohort (86.3 and 89.5%) and the additional independent AD cohort (90.8 and 83.8%). The receiver operating characteristic curves for the LncSigAD9 were 0.863 and 0.939 for discovery and independent cohorts, respectively. Furthermore, the LncSigAD9 demonstrated higher diagnostic performance than nine-minus-one lncRNA signature and mRNA-based signature with a similar number of genes. In silico functional analysis indicated the involvement of lncRNA expression variation in brain development- and metabolism-related biological processes. Taken together, our study highlights the importance of lncRNAs in brain aging and AD, and demonstrated the utility of lncRNAs as a promising biomarker for early AD diagnosis and treatment.

Electroencephalography (EEG) source analysis is one of the most important noninvasive human brain imaging tools that provides millisecond temporal accuracy. However, discovering essential activated brain sources associated with different brain status is still a challenging problem. In this study, we propose for the first time that the ill-posed EEG inverse problem can be formulated and solved as a sparse over-complete dictionary learning problem. In particular, a novel supervised sparse dictionary learning framework was developed for EEG source reconstruction. A revised version of discriminative K-SVD (DK-SVD) algorithm is exploited to solve the formulated supervised dictionary learning problem. As the proposed learning framework incorporated the EEG label information of different brain status, it is capable of learning a sparse representation that reveal the most discriminative brain activity sources among different brain states. Compared to the state-of-the-art EEG source analysis methods, proposed sparse dictionary learning framework achieved significant superior performance in both computing speed and accuracy for the challenging EEG source reconstruction problem through extensive numerical experiments. More importantly, the experimental results also validated that the proposed sparse learning framework is effective to discover the discriminative task-related brain activation sources, which shows the potential to advance the high resolution EEG source analysis for real-time non-invasive brain imaging research.

EEG source imaging integrates temporal and spatial components of EEG to localize the generating source of electrical potentials based on recorded EEG data on the scalp. As EEG sensors can't directly measure activated brain sources, many approaches were proposed to estimate brain source activation pattern given EEG data. However, since most part of the brain activity is composed of the spontaneous non-task related activations, true task caused activation sources will be corrupted in strong background signal. For decades, the EEG inverse problem was solved in an unsupervised way without any utilization of the label information that represents different brain states. We propose that by leveraging label information, the task related discriminative sources can be much better retrieved among strong spontaneous background signals. A novel model for solving EEG inverse problem called Laplacian Graph Regularized Discriminative Source Reconstruction which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model can be generally extended with different assumptions. The extension of our framework is applied to VB-SCCD model which aim to estimate extended brain sources by including a spatial total variation regularization term. Simulated results show the effectiveness of the proposed framework.

Gene expression is modulated by multiple mechanisms, including genetic and/or epigenetic regulation, and associated with the processes of cellular differentiation and morphogenesis. Single nucleotide polymorphisms (SNPs) and DNA methylation play important roles in regulating gene expression. In this study, we focused on revealing the relationship between SNPs, DNA methylation and gene expression in two human populations genome-wide through proposing four regulation patterns and developed maximum likelihood estimate models. Using simulated data with different correlation coefficients between any two traits, the power of our approach showed a favourable performance and relative stability. In all, 6733 SNP-CpG-gene pairs including 957 genes were obtained in Northern European ancestry (CEU) population. As the results showed, SNPs and DNA methylation had approximately the same effect on expression regulation of 49% genes, which was termed cooperative/antagonistic regulation pattern. Less than 30% of genes are controlled only by one of the factors (SNP/DNA methylation). The others showed SNPs that affect methylation have no consequent effects or crosstalk regulation on gene expression. Similar result was shown in Yourba (YRI) population. Specific genes were inferred using the different mechanisms of gene regulation involved in complex diseases by combining literature. This approach provides a method to comprehensively assess regulation patterns of gene expression in the whole genome.