N6-methyladenosine (m6A) is methylation that occurs in the N6-position of adenosine, which is the most prevalent internal modification on eukaryotic mRNA. Accumulating evidence suggests that m6A modulates gene expression, thereby regulating cellular processes ranging from cell self-renewal, differentiation, invasion and apoptosis. M6A is installed by m6A methyltransferases, removed by m6A demethylases and recognized by reader proteins, which regulate of RNA metabolism including translation, splicing, export, degradation and microRNA processing. Alteration of m6A levels participates in cancer pathogenesis and development via regulating expression of tumor-related genes like BRD4, MYC, SOCS2 and EGFR. In this review, we elaborate on recent advances in research of m6A enzymes. We also highlight the underlying mechanism of m6A in cancer pathogenesis and progression. Finally, we review corresponding potential targets in cancer therapy.

The Wnt/β-catenin pathway comprises a family of proteins that play critical roles in embryonic development and adult tissue homeostasis. The deregulation of Wnt/β-catenin signalling often leads to various serious diseases, including cancer and non-cancer diseases. Although many articles have reviewed Wnt/β-catenin from various aspects, a systematic review encompassing the origin, composition, function, and clinical trials of the Wnt/β-catenin signalling pathway in tumour and diseases is lacking. In this article, we comprehensively review the Wnt/β-catenin pathway from the above five aspects in combination with the latest research. Finally, we propose challenges and opportunities for the development of small-molecular compounds targeting the Wnt signalling pathway in disease treatment.

Objectives: To compare achieved and predicted tooth movements of maxillary first molars and central incisors in first premolar extraction cases treated with Invisalign. Materials and Methods: The present study included 30 patients who received maxillary first premolar extraction treatment with Invisalign. The actual posttreatment model was registered with the pretreatment model on the palatal stable region and superimposed with the virtual posttreatment model. Achieved and predicted tooth movements of maxillary first molars and central incisors were compared using paired t-test. Linear mixed-effect model analyses were used to explore the influence of age (adolescents vs adults), attachment (G6-optimized vs 3-mm vertical, 3-mm horizontal, and 5-mm horizontal), and initial crowding on the differences between predicted and achieved tooth movement (DPATM). Results: First molars achieved greater mesial tipping, mesial translation, and intrusion than predicted. Central incisors achieved less retraction and greater lingual crown torque and extrusion than predicted. Adolescents showed greater DPATM in the mesiodistal translation of first molars and labiolingual translation of central incisors and smaller DPATM in the occlusogingival translation of the first molars and crown torque of the central incisors than adults. The 3-mm vertical attachment group showed greater DPATM in the mesiodistal translation of the first molars vs the G6-optimized attachment group. Initial crowding had an inverse correlation with DPATM in angulation and mesiodistal translation of the first molars. Conclusions: First molar anchorage control and central incisor retraction were not fully achieved as predicted in first premolar extraction treatment with Invisalign. Age, attachment, and initial crowding affected the differences between predicted and achieved tooth movement.

Several attempts have been lately proposed to tackle the problem of recovering the original image of an underwater scene using a sequence distorted by water waves. The main drawback of the state of the art [18] is that it heavily depends on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In this paper, we revisit the problem by proposing a data-driven two-stage approach, each stage is targeted toward a certain type of noise. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative robust registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences even only after the first stage.

ObjectiveColorectal cancer (CRC) is considered to be the most prevalent malignant tumors that contribute to high cancer-related mortality. However, the signaling pathways involved in CRC and CRC-driven genes are largely unknown. We seek to discover a novel biomarker in CRC.Materials and methodsAll clinical CRC samples (n=33) were from Xiangya Hospital. We first selected CCNA2 by integrated bioinformatics analysis of four GSE databases. Next, the expression of CCNA2 in tissues and cell lines was verified by quantitative real-time PCR. The effects of CCNA2 on cell growth, proliferation, cell cycle, and apoptosis were examined by in vitro assays.ResultsWe identified 498 shared DEGs (294 upregulated and 204 downregulated), and the top ten hub genes were selected by integrated analysis. These hub genes were significantly overexpressed in CRC samples and were positively correlated. Our data revealed that the expression of CCNA2 in CRC tissues is higher than that in normal tissues. The CCNA2 knockdown could significantly suppress CRC cell growth by impairing cell cycle progression and inducing cell apoptosis.ConclusionCCNA2, as a novel oncogenic gene, plays a role in regulating cancer cell growth and apoptosis. It could be used as a new biomarker for diagnosis and therapy in CRC.

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We propose an approach to improve the detection performance of a generic detector when it is applied to a particular video. The performance of offline-trained objects detectors are usually degraded in unconstrained video environments due to variant illuminations, backgrounds and camera viewpoints. Moreover, most object detectors are trained using Haar-like features or gradient features but ignore video specific features like consistent color patterns. In our approach, we apply a Superpixel-based Bag-of-Words (BoW) model to iteratively refine the output of a generic detector. Compared to other related work, our method builds a video-specific detector using superpixels, hence it can handle the problem of appearance variation. Most importantly, using Conditional Random Field (CRF) along with our super pixel-based BoW model, we develop an algorithm to segment the object from the background . Therefore our method generates an output of the exact object regions instead of the bounding boxes generated by most detectors. In general, our method takes detection bounding boxes of a generic detector as input and generates the detection output with higher average precision and precise object regions. The experiments on four recent datasets demonstrate the effectiveness of our approach and significantly improves the state-of-art detector by 5-16% in average precision.