Tracking-assisted Weakly Supervised Online Visual Object Segmentation in Unconstrained Videos

This paper tackles the task of online video object segmentation with weak supervision, i.e., labeling the target object and background with pixel-level accuracy in unconstrained videos, given only one bounding box information in the first frame. We present a novel tracking-assisted visual object segmentation framework to achieve this. On the one hand, initialized with a given bounding box in the first frame, the auxiliary object tracking module guides the segmentation module frame by frame by providing motion and region information, which is usually missing in semi-supervised methods. Moreover, compared with the unsupervised approach, our approach with such minimum supervision can focus on the target object without bringing unrelated objects into the final results. On the other hand, the video object segmentation module also improves the robustness of the visual object tracking module by pixel-level localization and objectness information. Thus, segmentation and tracking in our framework can mutually help each other in an online manner. To verify the generality and effectiveness of the proposed framework, we evaluate our weakly supervised method on two cross-domain datasets, i.e., the DAVIS and VOT2016 datasets, with the same configuration and parameter setting. Experimental results show the top performance of our method, which is even better than the leading semi-supervised methods. Furthermore, we conduct the extensive ablation study on our approach to investigate the influence of each component and main parameters

The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs

Clear evidence shows that tumors could secrete microRNAs (miRNAs) via exosomes to modulate the tumor microenvironment (TME). However, the mechanisms sorting specific miRNAs into exosomes are still unclear. In order to study the biological function and characterization of exosomal miRNAs, we performed whole-transcriptome sequencing in 59 patients’ whole-course cerebrospinal fluid (CSF) small extracellular vesicles (sEV) and matched glioma tissue samples. The results demonstrate that miRNAs could be divided into exosome-enriched miRNAs (ExomiRNAs) and intracellular-retained miRNAs (CLmiRNAs), and exosome-enriched miRNAs generally play a dual role. Among them, miR-1298-5p was enriched in CSF exosomes and suppressed glioma progression in vitro and vivo experiments. Interestingly, exosomal miR-1298-5p could promote the immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) to facilitate glioma. Therefore, we found miR-1298-5p had different effects on glioma cells and MDSCs. Mechanically, downstream signaling pathway analyses showed that miR-1298-5p plays distinct roles in glioma cells and MDSCs via targeting SETD7 and MSH2, respectively. Moreover, reverse verification was performed on the intracellular-retained miRNA miR-9-5p. Thus, we confirmed that tumor-suppressive miRNAs in glioma cells could be eliminated through exosomes and target tumor-associated immune cells to induce tumor-promoting phenotypes. Glioma could get double benefit from it. These findings uncover the mechanisms that glioma selectively sorts miRNAs into exosomes and modulates tumor immunity.publishedVersio

A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm

A Significant Statistical Advancement on the Predictive Values of ERCC1 Polymorphisms for Clinical Outcomes of Platinum-Based Chemotherapy in Non-Small Cell Lung Cancer: An Updated Meta-Analysis

Background. There is no definitive conclusion so far on the predictive values of ERCC1 polymorphisms for clinical outcomes of platinum-based chemotherapy in non-small cell lung cancer (NSCLC). We updated this meta-analysis with an expectation to obtain some statistical advancement on this issue. Methods. Relevant studies were identified by searching MEDLINE, EMBASE databases from inception to April 2015. Primary outcomes included objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). All analyses were performed using the Review Manager version 5.3 and the Stata version 12.0. Results. A total of 33 studies including 5373 patients were identified. ERCC1 C118T and C8092A could predict both ORR and OS for platinum-based chemotherapy in Asian NSCLC patients (CT + TT versus CC, ORR: OR = 0.80, 95% CI = 0.67–0.94; OS: HR = 1.24, 95% CI = 1.01–1.53) (CA + AA versus CC, ORR: OR = 0.76, 95% CI = 0.60–0.96; OS: HR = 1.37, 95% CI = 1.06–1.75). Conclusions. Current evidence strongly indicated the prospect of ERCC1 C118T and C8092A as predictive biomarkers for platinum-based chemotherapy in Asian NSCLC patients. However, the results should be interpreted with caution and large prospective studies are still required to further investigate these findings

Additional file 1 of ARPC1B promotes mesenchymal phenotype maintenance and radiotherapy resistance by blocking TRIM21-mediated degradation of IFI16 and HuR in glioma stem cells

Additional file 1: Figure S1. (A) The expression of ARPs among mesenchymal (MES), proneural (PN), and classical (CL) phenotypes in TCGA GBM. (B) Kaplan–Meier curves visualizing the overall survival of CCGA-GBM patients stratified according to expression of ARPs. (C) The expression of ARPs among MES, PN, and CL phenotypes in CGGA GBM. (D) GSEA exhibited a positive correlation between ARPC1B expression and MES phenotypes, and a negative correlation with PN phenotypes. (E) Single-cell RNA sequencing of GSE138794 visualizing the expression of ARPs other than ARPC1B. Fig. S2. (A) Correlation analysis of ARPC1B with CD44, YKL-40, OLIG2 and SOX2 in TCGA-GBM and CGGA-GBM, respectively. (B) Western blot analysis of ARPC1B and SOX2 protein levels in GSC 8-11 overexpressing ARCP1B. (C, D) Representative images and quantification of tumor sphere formation of GSC 8-11 (C) and GSC 11 (D) transduced with vector or ARPC1B. Scale bar, 100μm. (E) The protein expression of ARPC1B in GSC 20 and GSC 267 under different IR dose treatments. Fig. S3. (A) Flow cytometric analysis showing the effect of ARPC1B overexpression on the apoptosis in IR-treated (6Gy) GSC 8-11 cells. The right panels showing the quantification of apoptosis rate. (B) Representative images and quantification of comet assay showing the effect of ARPC1B overexpression on DNA damage of GSC 8-11 with IR treatment (6 Gy). Scale bar, 20μm. (C) Representative images and quantification of γ-H2AX IF staining showing the effect of ARPC1B knockdown on DNA damage of GSC 267 and GSC 20 with IR treatment (6 Gy). Scale bar, 40μm. (D) Representative images and quantification of γ-H2AX IF staining in GSC 8-11. Scale bar, 40μm. (E) Cell-cycle analysis of GSC 267, GSC 20 and GSC 8-11 in different treatment groups. The proportions of cells arrested in G2/M phase were quantified (right panel). Fig. S4. (A, B) Bioluminescence imaging of tumor size on day 7 in sh-control, sh-ARPC1B#1 and sh-ARPC1B#2 GSC 267 (A) or GSC 20 (B) xenograft nude mice in indicated groups. The right panel shows the quantification of photon counts of GSC 267 and GSC 20 xenografts. (C, D) Bioluminescence imaging of tumor size on day 7 (C) and day 30 (D) in Vector or ARPC1B-transfected GSC 8-11 xenograft nude mice receiving or exempt from IR treatment. The right panel showing the quantification of photon counts of GSC 8-11 xenografts. Fig. S5. (A) Representative images and quantification of IHC staining for CD44 in sections of non-IR GSC 267 xenografts (upper, scale bar, 200μm), and TUNEL staining in sections of IR treated GSC 267 xenografts (lower, scale bar, 200μm). (B) Representative images and quantification of IHC staining for CD44 in sections of non-IR GSC 8-11 xenografts (upper, scale bar, 200μm), and TUNEL staining in sections of IR treated GSC 8-11 xenografts (lower, scale bar, 200μm). (C) Representative images of H&E staining in sections from indicated xenografts. Scale bar, 400μm. (D) Western blotting analysis of protein levels of DBN1, ACTN4, FLNA, and CORO1C upon knockdown of ARPC1B in GSC 20 and GSC 267, or overexpression of ARPC1B in GSC 8-11. Fig. S6. (A) Co-IF staining exhibiting the distribution of ARPC1B with IFI16 or HuR in GSC 20. Scale bar, 5μm. (B) The protein levels of IFI16 and HuR after overexpression of ARPC1B in GSC 8-11. (C) The mRNA expression of IFI16 and HuR assessed by qRT-PCR assay. (D) The protein levels of IFI16 and HuR in sh-control or sh-ARPC1B-GSCs treated with 100μg/ml CHX for indicated times. (E) Representative images and quantification of IHC staining for ARPC1B, HuR and IFI16 in different groups of GSC 267 xenograft sections (scale bar, 200μm). Fig. S7. (A) Western blotting analysis showing the effect of TRIM21 knockdown on the protein levels of IFI16 and HuR in sh-ARPC1B GSCs. (B) Western blotting analysis showing the effect of ARPC1B knockdown in GSC 20 and GSC 267, or ARPC1B overexpression in GSC 8-11 on the protein levels of STAT3, p-STAT3, P65, and p-P65. (C) Western blotting analysis of protein levels of ARPC1B, IFI16, HuR, P65, p-P65, STAT3, p-STAT3 and, CD44 in GSC 20 treated with indicated interventions. (D) Western blotting analysis showing that knockdown of TRIM21 could reverse the effect of ARPC1B inhibition on IFI16 and HuR ubiquitination. GSCs were pretreated with MG132 (10 µM) for 6 hours before cell lysates were collected. Fig. S8. (A) Representative images of tumor sphere formation of GSC 267 and GSC 20 treated with indicated interventions. Scale bar, 100μm. (B) Representative images of flow cytometry assays showing apoptosis of GSC 267 and GSC 20 treated with indicated interventions. (C) Representative images of comet assays showing DNA damage of GSC 267 and GSC 20 treated with indicated interventions. Scale bar, 20μm. Fig. S9. (A) The correlation between the ARPC1B expression and drug sensitivity assessed by Spearman algorithm. (B) CCK-8 assay in GSC 267 and GSC 20 treated with different concentrations of AZD6738 for 48 h. (C) Representative images and quantification of apoptosis assays (upper panel) and comet assays (lower panel, scale bar, 20μm) for GSC 20 and GSC 267 upon treatment with the indicated interventions. (D) The representative images and quantification of apoptosis assays (upper panel) and comet assays (lower panel, scale bar, 20μm) for GSC 8-11 upon treatment with the indicated interventions. The right panels are the quantification of apoptosis rate and DNA damage, respectively. (E) Representative images and quantification of TUNEL staining in sections of GSC 267 xenografts for different groups. Scale bar, 200μm. (F) Bioluminescence imaging of tumor size on day 7 in GSC 267 xenograft nude mice treated with the indicated interventions. (G) The quantification of photon counts on day 7 of the GSC 267 xenografts. Supplementary Materials and Methods

Additional file 2 of ARPC1B promotes mesenchymal phenotype maintenance and radiotherapy resistance by blocking TRIM21-mediated degradation of IFI16 and HuR in glioma stem cells

Additional file 2: Table S1. Correlation analysis of ARPC1B with PN and MES marker genes (Correlation). Table S2. Mass spectrometry result of proteins related with ARPC1B. Table S3. The correlation between the ARPC1B expression and drug sensitivity assessed by Spearman algorithm (correlation)