Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely timeconsuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named Feature Pyramid and Hierarchical Boosting Network (FPHBN), for pavement crack detection. The proposed network integrates context information to low-level features for crack detection in a feature pyramid way. And, it balances the contributions of both easy and hard samples to loss by nested sample reweighting in a hierarchical way during training. In addition, we propose a novel measurement for crack detection named average intersection over union (AIU). To demonstrate the superiority and generalizability of the proposed method, we evaluate it on five crack datasets and compare it with state-of-the-art crack detection, edge detection, and semantic segmentation methods. Extensive experiments show that the proposed method outperforms these methods in terms of accuracy and generalizability. Code and data can be found in https://github.com/fyangneil/pavement-crack-detection
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.
Background & AimsCholangiocarcinomas (CCA) are resistant to chemotherapy, so new therapeutic agents are needed. We performed a screen to identify small-molecule compounds that are active against CCAs. Levels of microRNA 21 (MIR21 or miRNA21) are increased in CCAs. We investigated whether miRNA21 mediates resistance of CCA cells and organoids to HSP90 inhibitors.MethodsWe performed a high-throughput screen of 484 small-molecule compounds to identify those that reduced viability of 6 human CCA cell lines. We tested the effects of HSP90 inhibitors on cells with disruption of the MIR21 gene, cells incubated with MIR21 inhibitors, and stable cell lines with inducible expression of MIR21. We obtained CCA biopsies from patients, cultured them as organoids (patient-derived organoids). We assessed their architecture, mutation and gene expression patterns, response to compounds in culture, and when grown as subcutaneous xenograft tumors in mice.ResultsCells with IDH1 and PBRM1 mutations had the highest level of sensitivity to histone deacetylase inhibitors. HSP90 inhibitors were effective in all cell lines, irrespective of mutations. Sensitivity of cells to HSP90 inhibitors correlated inversely with baseline level of MIR21. Disruption of MIR21 increased cell sensitivity to HSP90 inhibitors. CCA cells that expressed transgenic MIR21 were more resistant to HSP90 inhibitors than cells transfected with control vectors; inactivation of MIR21 in these cells restored sensitivity to these agents. MIR21 was shown to target the DnaJ heat shock protein family (Hsp40) member B5 (DNAJB5). Transgenic expression of DNAJB5 in CCA cells that overexpressed MIR21 re-sensitized them to HSP90 inhibitors. Sensitivity of patient-derived organoids to HSP90 inhibitors, in culture and when grown as xenograft tumors in mice, depended on expression of miRNA21.ConclusionsmiRNA21 appears to mediate resistance of CCA cells to HSP90 inhibitors by reducing levels of DNAJB5. HSP90 inhibitors might be developed for the treatment of CCA and miRNA21 might be a marker of sensitivity to these agents.
Genetic changes are infrequent in acute myeloid leukemia (AML) compared to other malignancies and often involve epigenetic regulators, suggesting that an altered epigenome may underlie AML biology and outcomes. In 96 AML cases including 65 pilot samples selected for cured/not-cured, we found higher CpG island (CGI) promoter methylation in cured patients. Expanded genome-wide digital restriction enzyme analysis of methylation (DREAM) data revealed a CGI methylator phenotype independent of IDH1/2 mutations we term AML-CIMP (A-CIMP+). A-CIMP was associated with longer overall survival (OS) in this dataset (median OS, years: A-CIMP+ = Not reached, A-CIMP− =1.17; P=0.08). For validation we used 194 samples from The Cancer Genome Atlas interrogated with Illumina 450k methylation arrays where we confirmed longer OS in A-CIMP (median OS, years: A-CIMP+ =2.34, A-CIMP− =1.00; P=0.01). Hypermethylation in A-CIMP favored CGIs (OR: CGI/non-CGI=5.21), and while A-CIMP was enriched in CEBPA (P=0.002) and WT1 mutations (P=0.02), 70% of cases lacked either mutation. Hypermethylated genes in A-CIMP function in pluripotency maintenance, and a gene expression signature of A-CIMP was associated with outcomes in multiple datasets. We conclude that CIMP in AML cannot be explained solely by gene mutations (e.g. IDH1/2, TET2), and that curability in A-CIMP+ AML should be validated prospectively.
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