Putative Zinc Finger Protein Binding Sites Are Over-Represented in the Boundaries of Methylation-Resistant CpG Islands in the Human Genome

Majority of CpG dinucleotides in mammalian genomes tend to undergo DNA methylation, but most CpG islands are resistant to such epigenetic modification. Understanding about mechanisms that may lead to the methylation resistance of CpG islands is still very poor.Using the genome-scale in vivo DNA methylation data from human brain, we investigated the flanking sequence features of methylation-resistant CpG islands, and discovered that there are several over-represented putative Transcription Factor Binding Sites (TFBSs) in methylation-resistant CpG islands, and a specific group of zinc finger protein binding sites are over-represented in boundary regions ( approximately 400 bp) flanking such CpG islands. About 77% of the over-represented putative TFBSs are conserved among human, mouse and rat. We also observed the enrichment of 4 histone methylations in methylation-resistant CpG islands or their boundaries.Our results suggest a possible mechanism that certain putative zinc finger protein binding sites over-represented in the boundary regions of the methylation-resistant CpG islands may block the spreading of methylation into these islands, and those TFBSs over-represented within the islands may both reinforce the methylation blocking and promote transcription. Some histone modifications may also enhance the immunity of the CpG islands against DNA methylation by augmenting these TFs' binding. We speculate that the dynamical equilibrium between methylation spreading and blocking is likely to be responsible for the establishment and maintenance of the relatively stable DNA methylation pattern in human somatic cells

SIRT1 is a regulator of autophagy: Implications in gastric cancer progression and treatment

AbstractSilent mating type information regulation 1 (SIRT1) is implicated in tumorigenesis through its effect on autophagy. In gastric cancer (GC), SIRT1 is a marker for prognosis and is involved in cell invasion, proliferation, epithelial-mesenchymal transition (EMT) and drug resistance. Autophagy can function as a cell-survival mechanism or lead to cell death during the genesis and treatment of GC. This functionality is determined by factors including the stage of the tumor, cellular context and stress levels. Interestingly, SIRT1 can regulate autophagy through the deacetylation of autophagy-related genes (ATGs) and mediators of autophagy. Taken together, these findings support the need for continued research efforts to understand the mechanisms mediating the development of gastric cancer and unveil new strategies to eradicate this disease

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

SPR:Supervised Personalized Ranking Based on Prior Knowledge for Recommendation

The goal of a recommendation system is to model the relevance between each user and each item through the user-item interaction history, so that maximize the positive samples score and minimize negative samples. Currently, two popular loss functions are widely used to optimize recommender systems: the pointwise and the pairwise. Although these loss functions are widely used, however, there are two problems. (1) These traditional loss functions do not fit the goals of recommendation systems adequately and utilize prior knowledge information sufficiently. (2) The slow convergence speed of these traditional loss functions makes the practical application of various recommendation models difficult. To address these issues, we propose a novel loss function named Supervised Personalized Ranking (SPR) Based on Prior Knowledge. The proposed method improves the BPR loss by exploiting the prior knowledge on the interaction history of each user or item in the raw data. Unlike BPR, instead of constructing triples, the proposed SPR constructs quadruples. Although SPR is very simple, it is very effective. Extensive experiments show that our proposed SPR not only achieves better recommendation performance, but also significantly accelerates the convergence speed, resulting in a significant reduction in the required training time

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA

Abstract Background Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features. Methods In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation. Results Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results. Conclusion The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation

A Person Reidentification Algorithm Based on Improved Siamese Network and Hard Sample

Person reidentification is aimed at solving the problem of matching and identifying people under the scene of cross cameras. However, due to the complicated changes of different surveillance scenes, the error rate of person reidentification exists greatly. In order to solve this problem and improve the accuracy of person reidentification, a new method is proposed, which is integrated by attention mechanism, hard sample acceleration, and similarity optimization. First, the bilinear channel fusion attention mechanism is introduced to improve the bottleneck of ResNet50 and fine-grained information in the way of multireceptive field feature channel fusion is fully learnt, which enhances the robustness of pedestrian features. Meanwhile, a hard sample selection mechanism is designed on the basis of the P2G optimization model, which can simplify and accelerate picking out hard samples. The hard samples are used as the objects of similarity optimization to realize the compression of the model and the enhancement of the generalization ability. Finally, a local and global feature similarity fusion module is designed, in which the weights of each part are learned through the training process, and the importance of key parts is automatically perceived. Experimental results on Market-1501 and CUHK03 datasets show that, compared with existing methods, the algorithm in this paper can effectively improve the accuracy of person reidentification

Human Action Recognition Algorithm Based on Improved ResNet and Skeletal Keypoints in Single Image

Human action recognition is an important part for computers to understand the behavior of people in pictures or videos. In a single image, there is no context information for recognition, so its accuracy still needs to be greatly improved. In this paper, a single-image human action recognition method based on improved ResNet and skeletal keypoints is proposed, and the accuracy is improved by several methods. We improved the backbone network ResNet-50 and CPN to a certain extent and constructed a multitask network to suit the human action recognition task, which not only improves the accuracy but also balances the total number of parameters and solves the problem of large network and slow operation. In this paper, the improvement methods of ResNet-50, CPN, and whole network are tested, respectively. The results show that the single-image human action recognition based on improved ResNet and skeletal keypoints can accurately identify human action in the case of different human movements, different background light, and occlusion. Compared with the original network and the main human action recognition algorithms, the accuracy of our method has its certain advantages