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Identity-based remote data auditing schemes can verify data integrity and provide a simple identity authentication and management for multiple users. However, prior works on identity-based remote data auditing lack the support of dynamic operations. In these schemes, tag generation is linked to the index of data block, which is related to update operations such as modification, insertion and deletion. If users perform dynamic operations on a data block, the tags of all subsequent blocks need to be modified. It means that if users want to update data on a big data platform, they have to download the whole file, update the file and send the updated file to the big data platform. Such pattern will bring huge communication overhead. In this paper, we propose an identity-based dynamic data auditing scheme which supports dynamic data operations, including modification, insertion and deletion. As far as we know, there is still no other identity-based data auditing scheme that supports dynamic operations. In particular, to achieve efficient dynamic operations, we use the data structure of Merkle hash tree for block tag authentication, which helps update data with integrity assurance. Analyses of security and performance show that the proposed scheme is efficient and secure.

Background Identification of more promising microRNAs (miRNAs) are being extensively studied with respect to colorectal cancer (CRC), since CRC is the leading cause of cancer deaths and most common malignant tumors worldwide. A series of colon cancer (CCa) samples from The Cancer Genome Atlas (TCGA) were analyzed to provide a new perspective into this field. Methods The expression of miRNAs, mRNAs and the clinical data of 437 CRC patients were downloaded from the TCGA database. The survival-related differentially expressed miRNAs (sDMIRs) and mRNAs were detected by COX regression analysis. The high-risk group and low-risk group were separated by the median risk score of the risk score model. The potential clinical characteristics of these sDMIRs were analyzed by R software. The potential molecular mechanisms of these sDMIRs were explored by computational biology. The expression levels of three sDMIRs were explored by qPCR in CRC samples. Results Three DMIRs (hsa-miR-21-3p, hsa-miR-194-3p and hsa-miR-891a-5p) correlated with the most remarkable prognostic values of CRC patients were selected to establish the risk score model (RSM) by univariate and multivariate COX regression analysis and the survival probability of the low-risk group was longer than that in the high-risk group. We detected the target genes of three sDMIRs and the potential molecular mechanisms of these sDMIRs. We further verified the high expression levels of hsa-miR-21-3p and hsa-miR-194-3p were associated with the early T-stages, while hsa-miR-891a-5p illustrated the reversed result. Conclusion Our study demonstrated three sDMIRs with significantly clinical values illustrated the potential predicting values in the prognosis of CRC patients. Our results may provide a new perspective for the diagnostic methods and treatment strategies in CRC patients.

Objective To evaluate the efficacy and safety of apatinib in patients with relapse after surgery for fibrosarcoma. Methods We reviewed the clinical data of 56 patients who experienced recurrence after fibrosarcoma resection and who received chemotherapy from September 2015 to September 2017 (no significant difference in general data of patients) (p>0.05). Differences in drug use and adverse effects were observed between patients who received monotherapy and those who received regular chemotherapy (MAID/AI). Results Compared with the regular chemotherapy group, patients in the apatinib monotherapy group exhibited an improved overall response rate (ORR) and disease control rate (DCR). In patients treated with apatinib, the incidence of adverse reactions was improved, and the symptoms were mild (P Conclusion Apatinib is a single-drug regimen that can be used in cases of recurrence of fibrosarcoma with high expression of vascular endothelial growth factor receptor-2 (VEGFR-2); its short-term efficacy is excellent, and its side effects are minimal. This drug may be used as part of a regular chemotherapy program.

Objective: To compare the recurrence rate of retinopathy of prematurity (ROP) after treatment with 0.3 mg vs. 0.25 mg ranibizumab. Subjects: All patients with ROP who underwent intravitreal injection of ranibizumab in Hainan General Hospital between January 2014 and May 2020 were included in this study. Methods: 82 cases (146 eyes) who received intravitreal injection of 0.25 mg ranibizumab were included in the conventional-dose group, and 59 cases (108 eyes) who received intravitreal injection of 0.3 mg ranibizumab were included in the high-dose group. The two groups were further divided into the 25-28-week, 29-31-week, 32-34-week, and 35-36-week GA subgroups. The differences between the conventional-dose group and the high-dose group in gestational age (GA), birth weight(BW), age at initial injection(weeks), incidence of systemic diseases, the recurrence rate of ROP, and age at retinal vascularization completed(weeks) were analyzed. Results: GA, BW, age at initial injection, and the incidence of systemic diseases were not significantly different between the conventional-dose group and the high-dose group (p>0.05). The recurrence rates of ROP were significantly lower in the 25-28-week, 29-31-week, and 32-34-week subgroups of the high-dose group than in the same subgroups of the conventional-dose group (p0.05). Retinal vascularization was completed at a later age in the 32-34-week subgroup of the high-dose group than in the 32-34-week subgroup of the conventional-dose group (p0.05). No severe ocular or systemic complications occurred in any patient. Conclusion: Treatment with 0.3 mg ranibizumab can reduce the recurrence rate of ROP without prolonging retinal vascularization or causing serious systemic complications. Therefore, this dose may be an appropriate therapeutic dose for ROP.

When users store data in big data platforms, the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data. However, the existing remote data auditing schemes for big data platforms are only applicable to static data. In order to verify the integrity of dynamic data in a Hadoop big data platform, we presents a dynamic auditing scheme meeting the special requirement of Hadoop. Concretely, a new data structure, namely Data Block Index Table, is designed to support dynamic data operations on HDFS (Hadoop distributed file system), including appending, inserting, deleting, and modifying. Then combined with the MapReduce framework, a dynamic auditing algorithm is designed to audit the data on HDFS concurrently. Analysis shows that the proposed scheme is secure enough to resist forge attack, replace attack and replay attack on big data platform. It is also efficient in both computation and communication.

Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that visual contrast is the most important factor in bottom-up visual attention process. Many of the proposed models predict saliency maps based on the computation of visual contrast between salient regions and backgrounds. In this paper, we design an end-to-end multi-scale global contrast convolutional neural network (CNN) that explicitly learns hierarchical contrast information among global and local features of an image to infer its salient object regions. In contrast to many previous CNN based saliency methods that apply super-pixel segmentation to obtain homogeneous regions and then extract their CNN features before producing saliency maps region-wise, our network is pre-processing free without any additional stages, yet it predicts accurate pixel-wise saliency maps. Extensive experiments demonstrate that the proposed network generates high quality saliency maps that are comparable or even superior to those of state-of-the-art salient object detection architectures.

Sentiment analysis is attracting more and more attentions and has become a very hot research topic due to its potential applications in personalized recommendation, opinion mining, etc. Most of the existing methods are based on either textual or visual data and can not achieve satisfactory results, as it is very hard to extract sufficient information from only one single modality data. Inspired by the observation that there exists strong semantic correlation between visual and textual data in social medias, we propose an end-to-end deep fusion convolutional neural network to jointly learn textual and visual sentiment representations from training examples. The two modality information are fused together in a pooling layer and fed into fully-connected layers to predict the sentiment polarity. We evaluate the proposed approach on two widely used data sets. Results show that our method achieves promising result compared with the state-of-the-art methods which clearly demonstrate its competency., Accepted as oral presentation by ICIP2017

In the existing remote data integrity checking schemes, dynamic update operates on block level, which usually restricts the location of the data inserted in a file due to the fixed size of a data block. In this paper, we propose a remote data integrity checking scheme with fine-grained update for big data storage. The proposed scheme achieves basic operations of insertion, modification, deletion on line level at any location in a file by designing a mapping relationship between line level update and block level update. Scheme analysis shows that the proposed scheme supports public verification and privacy preservation. Meanwhile, it performs data integrity checking with low computation and communication cost.

Recently, the main therapy of medullary thyroid cancer (MTC) is surgical, but by which way there is a poor prognosis with a mean survival of only 5 years. In some cases, some researchers found that it is the medullary thyroid cancer stem cells (MTCSCs) that cause metastasis and recurrence. This study aimed to eradicate MTCSCs through administration of all-trans-retinoic acid (ATRA). Here we demonstrate that MTCSCs possess stem- like properties in serum-free medium. The ABCG2, OCT4 and sodium iodide symporter (NIS) were changed by ATRA. Additionally, we found that ATRA can increase the expression of NIS in vivo. All the data suggested that ATRA could increase the iodine uptake of MTCSCs through NIS.