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Increasing evidence supports the essential roles of circular RNAs (circRNAs) and microRNAs (miRNAs/miRs) in different types of human cancer. For example, hsa_ circ_0137008 functions as a sponge for mi-338-5p and inhibits the malignant phenotype in colorectal cancer. Furthermore, hsa_circ_RNA_0011780 downregulates FBXW7 by targeting miR-554a and suppressing the progression of non-small cell lung cancer. Thus far, only a single report has identified that the miRNA miR-331-3p exerts a pivotal effect on human colorectal cancer (CRC) evolution. However, both the upand downstream regulatory mechanisms of miR-331-3p are unclear. In the present study, it was predicted via bioinformatics analysis that the circRNA, hsa_circ_0038646, and the glutamate receptor ionotropic kainate 3 (GRIK3) gene contain binding sites that can interact with miR-331-3p. Thus, hsa_ circ_0038646/miR-331-3p/GRIK3 may be a novel therapeutic pathway for CRC. Reverse transcription-quantitative PCR and western blotting analyses were performed, as well as cell proliferation, luciferase reporter and Transwell migration assays. Hsa_circ_0038646 was overexpressed in both CRC cells and tissues, and this aberrant expression was positively related with increasing tumor grade. Knockdown of hsa_circ_0038646 significantly weakened human CRC cell proliferation and migration. It was shown that hsa_circ_0038646 can sponge miR-331-3p to suppress its expression, and that suppression of miR-331-3p can reverse the effects of hsa_circ_0038646 inhibition in CRC cells. It was determined that GRIK3 is a downstream target of miR-331-3p, and that hsa_circ_0038646 could increase the levels of GRIK3 by suppressing miR-331-3p in CRC cells. Restoring GRIK3 expression rescued the weakened CRC cell proliferation and migration following hsa_circ_0038646 knockdown. The present study indicated that hsa_circ_0038646 functions as a tumor promoter in CRC by increasing GRIK3 expression via sponging of miR-331-3p. The hsa_circ_0038646/miR-331-3p/GRIK3 axis may be a novel therapeutic and diagnostic target of CRC.

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Attention deficit hyperactivity disorder (ADHD) is a kind of mental disease which often appears among young children. Various machine learning techniques including deep neural networks have been used to classify ADHD. As an alternative of deep neural networks, the deep forest or gcForest recently proposed by Zhou and Feng has demonstrated excellent performance on many imaging tasks. Therefore, in this paper, we are going to investigate using fMRI data and gcForest to discriminate ADHD subjects against normal controls. Two types of features are extracted from the fMRI data, they are 1-D functional connectivity (FC) feature and 3-D amplitude of low frequency fluctuations (ALFF) feature. We propose a revised gcForest method which uses a combined multi-grained scanning structure to fuse the two features together, thus a new concatenated feature vector can be formed for each sample. Moreover, considering the imbalanced property of ADHD data, we utilize synthetic minority over-sampling technique combined with edited-nearest neighbor to form synthetic minority concatenated feature vector samples for data balancing. Finally cascade forest is used to take the concatenated feature vector samples as input for classification. We test our method on the ADHD-200 public data sets and evaluate its performance on the hold-out testing data. We compare our method with several methods in the literature. The experiment illustrates that our method performs better than the reported methods. INDEX TERMS Attention deficit hyperactivity disorder, classification, deep forest, fMRI. The associate editor coordinating the review of this manuscript and approving it for publication was Omid Kavehei.

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15Near infrared (NIR) spectra of apples contain the most useful information of the soluble solids 16 content (SSC) and firmness of apples. A new feature extraction method, called sorting 17

Background Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model. Results In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively. Conclusion The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.