Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their ...complex relationships. However, so far no method has been developed to assess statistical significance of intersections among three or more sets. Moreover, the state-of-the-art approaches for visualization of multi-set intersections are not scalable. Here, we first developed a theoretical framework for computing the statistical distributions of multi-set intersections based upon combinatorial theory, and then accordingly designed a procedure to efficiently calculate the exact probabilities of multi-set intersections. We further developed multiple efficient and scalable techniques to visualize multi-set intersections and the corresponding intersection statistics. We implemented both the theoretical framework and the visualization techniques in a unified R software package, SuperExactTest. We demonstrated the utility of SuperExactTest through an intensive simulation study and a comprehensive analysis of seven independently curated cancer gene sets as well as six disease or trait associated gene sets identified by genome-wide association studies. We expect SuperExactTest developed by this study will have a broad range of applications in scientific data analysis in many disciplines.
Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate prognosis prediction of breast cancer can spare a significant number of patients from receiving unnecessary ...adjuvant systemic treatment and its related expensive medical costs. Previous work relies mostly on selected gene expression data to create a predictive model. The emergence of deep learning methods and multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of breast cancer and therefore can improve diagnosis, treatment, and prevention. In this study, we propose a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer. The novelty of the method lies in the design of our method's architecture and the fusion of multi-dimensional data. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches. The source code implemented by TensorFlow 1.0 deep learning library can be downloaded from the Github: https://github.com/USTC-HIlab/MDNNMD.
Automatic detection of salient objects in images has gained its popularity in computer vision field for its usage in numerous vision tasks in recent years. Depth information plays an important role ...in the human vision system while it is underutilized in most existing two-dimensional (2-D) saliency detection methods. In this letter, a multistage salient object detection framework via minimum barrier distance transform and saliency fusion based on multilayer cellular automata (MCA) is proposed. First, we independently generate the 3-D spatial prior, depth bias, and RGB-produced and depth-induced saliency maps. Next, the two saliency maps are weighted by depth bias to obtain two initial maps. Then, we adopt a saliency optimization step to generate more precise depth-induced saliency map. Moreover, the initial RGB-produced and the optimized depth-induced maps are further fused with 3-D spatial prior. Finally, we utilize MCA to fuse all saliency maps generated previously and obtain the final saliency result with complete salient object. The proposed method is evaluated on the publicly available benchmark dataset, RGBD1000. Compared to several state-of-the-art 2-D and depth-aware approaches, the experimental results demonstrate the effectiveness and superiority of our method, which can accurately detect the salient objects from RGB-D images, and has the most satisfactory overall performance.
Abstract
Exploring a simple, fast, solvent-free synthetic method for large-scale preparation of cheap, highly active electrocatalysts for industrial hydrogen evolution reaction is one of the most ...promising work today. In this work, a simple, fast and solvent-free microwave pyrolysis method is used to synthesize ultra-small (3.5 nm) Ru-Mo
2
C@CNT catalyst with heterogeneous structure and strong metal-support interaction in one step. The Ru-Mo
2
C@CNT catalyst only exhibits an overpotential of 15 mV at a current density of 10 mA cm
−2
, and exhibits a large turnover frequency value up to 21.9 s
−1
under an overpotential of 100 mV in 1.0 M KOH. In addition, this catalyst can reach high current densities of 500 mA cm
−2
and 1000 mA cm
−2
at low overpotentials of 56 mV and 78 mV respectively, and it displays high stability of 1000 h. This work provides a feasible way for the reasonable design of other large-scale production catalysts.
Genomic studies of lung adenocarcinoma (LUAD) have advanced our understanding of the disease’s biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD remain poorly ...understood. We carried out a comprehensive proteomics analysis of 103 cases of LUAD in Chinese patients. Integrative analysis of proteome, phosphoproteome, transcriptome, and whole-exome sequencing data revealed cancer-associated characteristics, such as tumor-associated protein variants, distinct proteomics features, and clinical outcomes in patients at an early stage or with EGFR and TP53 mutations. Proteome-based stratification of LUAD revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Further, we nominated potential drug targets and validated the plasma protein level of HSP 90β as a potential prognostic biomarker for LUAD in an independent cohort. Our integrative proteomics analysis enables a more comprehensive understanding of the molecular landscape of LUAD and offers an opportunity for more precise diagnosis and treatment.
Display omitted
•Discovery of prognosis-associated proteins and pathways at early stage of LUAD•Proteomics revealed three subtypes related to clinical and molecular features•Identification of subtype-specific kinases and cancer-associated phosphoproteins•Identification of potential prognostic biomarkers and drug targets in LUAD
Integrative proteomic characterization of lung adenocarcinoma in 103 Chinese patients identifies three subtypes related to clinical and molecular features and nominates potential prognostic biomarkers and drug targets.
Carcinoma-associated fibroblasts (CAFs) are abundant and heterogeneous stromal cells in tumor microenvironment that are critically involved in cancer progression. Here, we demonstrate that two ...cell-surface molecules, CD10 and GPR77, specifically define a CAF subset correlated with chemoresistance and poor survival in multiple cohorts of breast and lung cancer patients. CD10+GPR77+ CAFs promote tumor formation and chemoresistance by providing a survival niche for cancer stem cells (CSCs). Mechanistically, CD10+GPR77+ CAFs are driven by persistent NF-κB activation via p65 phosphorylation and acetylation, which is maintained by complement signaling via GPR77, a C5a receptor. Furthermore, CD10+GPR77+ CAFs promote successful engraftment of patient-derived xenografts (PDXs), and targeting these CAFs with a neutralizing anti-GPR77 antibody abolishes tumor formation and restores tumor chemosensitivity. Our study reveals a functional CAF subset that can be defined and isolated by specific cell-surface markers and suggests that targeting the CD10+GPR77+ CAF subset could be an effective therapeutic strategy against CSC-driven solid tumors.
Display omitted
•CD10 and GPR77 define a new CAF subset•CD10+GPR77+ CAFs sustain cancer stemness and promote tumor chemoresistance•Complement signaling maintains NF-κB activation•Targeting CD10+GPR77+ CAFs restores chemosensitivity
CD10 and GPR77 identify a cancer stemness-sustaining cancer-associated fibroblast subset.
Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people's work and life in the ...future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as baseline method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time.
Accumulating evidences have indicated that lncRNAs play an important role in various human complex diseases. However, known disease-related lncRNAs are still comparatively small in number, and ...experimental identification is time-consuming and labor-intensive. Therefore, developing a useful computational method for inferring potential associations between lncRNAs and diseases has become a hot topic, which can significantly help people to explore complex human diseases at the molecular level and effectively advance the quality of disease diagnostics, therapy, prognosis and prevention. In this paper, we propose a novel prediction of lncRNA-disease associations via lncRNA-disease-gene tripartite graph (TPGLDA), which integrates gene-disease associations with lncRNA-disease associations. Compared to previous studies, TPGLDA can be used to better delineate the heterogeneity of coding-non-coding genes-disease association and can effectively identify potential lncRNA-disease associations. After implementing the leave-one-out cross validation, TPGLDA achieves an AUC value of 93.9% which demonstrates its good predictive performance. Moreover, the top 5 predicted rankings of lung cancer, hepatocellular carcinoma and ovarian cancer are manually confirmed by different relevant databases and literatures, affording convincing evidence of the good performance as well as potential value of TPGLDA in identifying potential lncRNA-disease associations. Matlab and R codes of TPGLDA can be found at following: https://github.com/USTC-HIlab/TPGLDA .
Alzheimer's disease (AD) is a chronic neurodegenerative disease, with the characteristics of neurofibrillary tangle (NFT) and senile plaque (SP) formation. Although great progresses have been made in ...clinical trials based on relevant hypotheses, these studies are also accompanied by the emergence of toxic and side effects, and it is an urgent task to explore the underlying mechanisms for the benefits to prevent and treat AD. Herein, based on animal experiments and a few clinical trials, neuroinflammation in AD is characterized by long-term activation of pro-inflammatory microglia and the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasomes. Damaged signals from the periphery and within the brain continuously activate microglia, thus resulting in a constant source of inflammatory responses. The long-term chronic inflammatory response also exacerbates endoplasmic reticulum oxidative stress in microglia, which triggers microglia-dependent immune responses, ultimately leading to the occurrence and deterioration of AD. In this review, we systematically summarized and sorted out that exercise ameliorates AD by directly and indirectly regulating immune response of the central nervous system and promoting hippocampal neurogenesis to provide a new direction for exploring the neuroinflammation activity in AD.
As one large class of non-coding RNAs （ncRNAs）, long ncRNAs （IneRNAs） have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. ...Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRN~protein bipartite network inference （LPBNI）. LPBNI aims to identify potential lncRNA-interacting proteins, by making full use of the known IncRNA-protein interactions. Leave-one-out cross validation （LOOCV） test shows that LPBNI significantly outperforms other network-based methods, including random walk （RWR） and protein-based collaborative filtering （ProCF）. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA-interacting proteins.
PDF
