Circular RNAs (circRNAs) have been regarded as critical regulators of human diseases and biological markers in some types of malignancies, including pancreatic ductal adenocarcinoma (PDAC). Recently, ...circ_0007534 has been identified as a novel cancer‐related circRNA. Nevertheless, its clinical relevance, functional roles, and mechanism have not been studied in PDAC. In the current study, real‐time quantitative polymerase chain reaction (RT‐qPCR) was used to detect the expression of circ_0007534 in 60‐paired PDAC tissue samples and different cell lines. Loss‐of‐function and gain‐of‐function assays were performed to detect cell proliferation, apoptosis, and metastatic properties affected by circ_0007534. An animal study was also carried out. The luciferase reporter assay was performed to uncover the underlying mechanism of circ_0007534. As a result, circ_0007534 was overexpressed not only in PDAC tissues but also in a panel of PDAC cell lines, and this overexpression is closely associated with advanced tumor stage and positive lymph node invasion. In addition, circ_0007534 may be regarded as an independent prognostic factor for patients with PDAC. For the part of functional assays, circ_0007534 significantly increased cell proliferation, migratory, and invasive potential of PDAC cells. Circ_0007534 could inhibit cell apoptosis partly via a Bcl‐2/caspase‐3 pathway. The xenograft study further confirmed the cell growth promoting the role of circ_0007534. Mechanistically, miR‐625 and miR‐892b were sponged by circ_0007534. The oncogenic functions of circ_0007534 is partly dependent on its regulation of miR‐625 and miR‐892b. In conclusion, our study illuminates a novel circRNA that confers an oncogenic function in PDAC.
Circ_0007534 is upregulated in pancreatic ductal adenocarcinoma (PDAC) both in tissues and cells.
Circ_0007534 is associated with patients’ adverse phenotypes and poor survival.
The circ_0007534/miR‐625/miR‐892 regulatory axis may contribute to the carcinogenesis and development of PDAC.
This paper presents a robust object tracking method via a spatial bias appearance model learned dynamically in video. Motivated by the attention shifting among local regions of a human vision system ...during object tracking, we propose to partition an object into regions with different confidences and track the object using a dynamic spatial bias appearance model (DSBAM) estimated from region confidences. The confidence of a region is estimated to reflect the discriminative power of the region in a feature space and the probability of occlusion. We propose a novel hierarchical Monte Carlo (HAMC) algorithm to learn region confidences dynamically in every frame. The algorithm consists of two levels of Monte Carlo processes implemented using two particle filtering procedures at each level and can efficiently extract high-confidence regions through video frames by exploiting the temporal consistency of region confidences. A dynamic spatial bias map is then generated from the high-confidence regions and is employed to adapt the appearance model of the object and to guide a tracking algorithm in searching for correspondences in adjacent frames of video images. We demonstrate feasibility of the proposed method in video surveillance applications. The proposed method can be combined with many other existing tracking systems to enhance the robustness of these systems.
Carbon‐based single‐atom catalysts (SACs) with well‐defined and homogeneously dispersed metal−N4 moieties provide a great opportunity for CO2 reduction. However, controlling the binding strength of ...various reactive intermediates on catalyst surface is necessary to enhance the selectivity to a desired product, and it is still a challenge. In this work, the authors prepared Sn SACs consisting of atomically dispersed SnN3O1 active sites supported on N‐rich carbon matrix (Sn‐NOC) for efficient electrochemical CO2 reduction. Contrary to the classic Sn‐N4 configuration which gives HCOOH and H2 as the predominant products, Sn‐NOC with asymmetric atomic interface of SnN3O1 gives CO as the exclusive product. Experimental results and density functional theory calculations show that the atomic arrangement of SnN3O1 reduces the activation energy for *COO and *COOH formation, while increasing energy barrier for HCOO* formation significantly, thereby facilitating CO2‐to‐CO conversion and suppressing HCOOH production. This work provides a new way for enhancing the selectivity to a specific product by controlling individually the binding strength of each reactive intermediate on catalyst surface.
A Sn‐based single‐atom catalyst with asymmetric atomic interface of SnN3O1 is designed and synthesized to switch the product of CO2 reduction from HCOOH to CO with an impressive FECO of 94%.
Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection ...algorithm based on the Single Shot MultiBox Detector (SSD) in Mimics is proposed in this paper. The algorithm firstly uses the aggregate channel feature model to preprocess the image to obtain the suspected calcium area, which greatly reduces the time of single-frame image detection. The basic network VGG-16 is replaced by Resnet-50, which introduces the identity mapping to solve the problem of reducing the detection accuracy when the number of network layers are increased. Finally, the powerful and flexible two-parameter loss function is used to optimize the training deep network and improve the network model generalization ability. Qualitative and quantitative experiments show that the performance of the proposed detection algorithm exceeds the existing calcium detection algorithms, and the detection efficiency is improved while ensuring the accuracy of calcium detection.
This paper presents a new method for detecting and recognizing text in complex images and video frames. Text detection is performed in a two-step approach that combines the speed of a text ...localization step, enabling text size normalization, with the strength of a machine learning text verification step applied on background independent features. Text recognition, applied on the detected text lines, is addressed by a text segmentation step followed by an traditional OCR algorithm within a multi-hypotheses framework relying on multiple segments, language modeling and OCR statistics. Experiments conducted on large databases of real broadcast documents demonstrate the validity of our approach.
Online display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important ...problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages and different time stamps have different patterns of user visits. In this paper, we propose a series of probabilistic latent class models to automatically learn the underlying user visit patterns among multiple Web pages and multiple time stamps. The last (and the most effective) proposed model identifies latent groups/classes of (i) Web pages and (ii) time stamps with similar user visit patterns, and learns a specialized forecast model for each latent Web page and time stamp class. Compared with a single regression model as well as several other baselines, the proposed latent class model approach has the capability of differentiating the importance of different types of information across different classes of Web pages and time stamps, and therefore has much better modeling flexibility. An extensive set of experiments along with detailed analysis carried out on real-world data from Yahoo! demonstrates the advantage of the proposed latent class models in forecasting online user visits in online display advertising.
This paper presents a system for protecting the privacy of specific individuals in video recordings. We address the following two problems: automatic people identification with limited labeled data, ...and human body obscuring with preserved structure and motion information. In order to address the first problem, we propose a new discriminative learning algorithm to improve people identification accuracy using limited training data labeled from the original video and imperfect pairwise constraints labeled from face obscured video data. We employ a robust face detection and tracking algorithm to obscure human faces in the video. Our experiments in a nursing home environment show that the system can obtain a high accuracy of people identification using limited labeled data and noisy pairwise constraints. The study result indicates that human subjects can perform reasonably well in labeling pairwise constraints with the face masked data. For the second problem, we propose a novel method of body obscuring, which removes the appearance information of the people while preserving rich structure and motion information. The proposed approach provides a way to minimize the risk of exposing the identities of the protected people while maximizing the use of the captured data for activity/behavior analysis.
We developed a tandem electrocatalyst for CO2‐to‐CO conversion comprising the single Cu site co‐coordinated with N and S anchored carbon matrix (Cu‐S1N3) and atomically dispersed Cu clusters (Cux), ...denoted as Cu‐S1N3/Cux. The as‐prepared Cu‐S1N3/Cux composite presents a 100 % Faradaic efficiency towards CO generation (FECO) at −0.65 V vs. RHE and high FECO over 90 % from −0.55 to −0.75 V, outperforming the analogues with Cu‐N4 (FECO only 54 % at −0.7 V) and Cu‐S1N3 (FECO 70 % at −0.7 V) configurations. The unsymmetrical Cu‐S1N3 atomic interface in the carbon basal plane possesses an optimized binding energy for the key intermediate *COOH compared with Cu‐N4 site. At the same time, the adjacent Cux effectively promotes the protonation of *CO2− by accelerating water dissociation and offering *H to the Cu‐S1N3 active sites. This work provides a tandem strategy for facilitating proton‐coupled electron transfer over the atomic‐level catalytic sites.
A tandem catalyst composed of single Cu sites co‐coordinated with N and S and atomically dispersed Cu clusters (Cu‐S1N3/Cux) was developed for CO2‐to‐CO conversion. The Cu‐S1N3/Cux exhibited a 100 % Faradaic efficiency for CO formation (FECO), outperforming the single‐atomic Cu‐N4 analogue (FECO=54 %).
Photocatalytic conversion of low‐concentration CO2 is considered as a promising way to simultaneously mitigate the environmental and energy issues. However, the weak CO2 adsorption and tough CO2 ...activation process seriously compromise the CO production, due to the chemical inertness of CO2 molecule and the formed fragile metal‐C/O bond. Herein, we designed and fabricated oxygen vacancy contained Co3O4 hollow nanoparticles on ordered macroporous N‐doped carbon framework (Vo−HCo3O4/OMNC) towards photoreduction of low‐concentration CO2. In situ spectra and ab initio molecular dynamics simulations reveal that the constructed oxygen vacancy is able to break the local structural symmetry of Co−O−Co sites. The formation of asymmetric active site switches the CO2 configuration from a single‐site linear model to a multiple‐sites bending one with a highly stable configuration, enhancing the binding and structural polarization of CO2 molecules. As a result, Vo−HCo3O4/OMNC shows unprecedent activity in the photocatalytic conversion of low‐concentration CO2 (10 % CO2/Ar) under laboratory light source or even natural sunlight, affording a syngas yield of 337.8 or 95.2 mmol g−1 h−1, respectively, with an apparent quantum yield up to 4.2 %.
In this paper, we present a system that automatically translates Arabic text embedded in images into English. The system consists of three components: text detection from images, character ...recognition, and machine translation. We formulate the text detection as a binary classification problem and apply gradient boosting tree (GBT), support vector machine (SVM), and location-based prior knowledge to improve the F1 score of text detection from 78.95% to 87.05%. The detected text images are processed by off-the-shelf optical character recognition (OCR) software. We employ an error correction model to post-process the noisy OCR output, and apply a bigram language model to reduce word segmentation errors. The translation module is tailored with compact data structure for hand-held devices. The experimental results show substantial improvements in both word recognition accuracy and translation quality. For instance, in the experiment of Arabic transparent font, the BLEU score increases from 18.70 to 33.47 with use of the error correction module.
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