Background: Hypoxia is a key feature of breast cancer, which affects cancer development, metastasis and metabolism. Previous studies suggested that circular RNAs (circRNAs) could participate in cancer progression and hypoxia regulation. This study aimed to investigate the role of circRNA differentially expressed in normal cells and neoplasia domain containing 4C (circDENND4C) in breast cancer progression under hypoxia. Methods: Forty-three patients with breast cancer were involved in this study. Breast cancer cell lines MDA-MB-453 and SK-BR-3 were cultured under hypoxia (1% O 2) for experiments in vitro. The expression levels of circDENND4C, microRNA-200b (miR-200b) and miR-200c were measured by quantitative real-time polymerase chain reaction. Glycolysis was investigated by glucose consumption, lactate production and hexokinase II (HK2) protein level. Migration and invasion were evaluated via trans-well assay and protein levels of matrix metallopeptidase 9 (MMP9) and MMP2. The interaction between circDENND4C and miR-200b or miR-200c was explored by bioinformatics analysis, luciferase assay and RNA immunoprecipitation. Murine xenograft model was established to investigate the anti-cancer role of circDENND4C in vivo. Results: circDENND4C highly expressed in breast cancer was up-regulated in response to hypoxia. Knockdown of circDENND4C decreased glycolysis, migration and invasion in breast cancer cells under hypoxia. circDENND4C was validated as a sponge of miR-200b and miR-200c. Deficiency of miR-200b or miR-200c reversed the suppressive effect of circDENND4C knockdown on breast cancer progression. Moreover, silence of circDENND4C reduced xenograft tumor growth by increasing miR-200b and miR-200c. Conclusion: circDENND4C silence suppresses glycolysis, migration and invasion in breast cancer cells under hypoxia by increasing miR-200b and miR-200c.

The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task.

Recently, the multimodal information is taken into consideration for ground-based cloud classification in weather station networks, but intrinsic correlations between the multimodal information and the visual information cannot be mined sufficiently. We propose a novel approach called hierarchical multimodal fusion (HMF) for ground-based cloud classification in weather station networks, which fuses the deep multimodal features and the deep visual features in different levels, i.e., low-level fusion and high-level fusion. The low-level fusion directly fuses the heterogeneous features, which focuses on the modality-specific fusion. The high-level fusion integrates the output of low-level fusion with deep visual features and deep multimodal features, which could learn complex correlations among them owing to the deep fusion structure. We employ one loss function to train the overall framework of the HMF so as to improve the discrimination of cloud representations. The experimental results on the MGCD dataset indicate that our method outperforms other methods, which verifies the effectiveness of the HMF in ground-based cloud classification. INDEX TERMS Weather station networks, ground-based cloud classification, hierarchical multimodal fusion, convolutional neural network.

Background: Thyroid cancer (TC) is the most frequent endocrine malignancy. Long noncoding RNAs (lncRNAs) have been confirmed to act as significant roles in tumor development. The role of lncRNA TMPO-AS1 in TC is still unclear, so it remains to be explored. The aim of the research is to investigate the role and regulatory mechanism of TMPO-AS1 in TC. Methods: TMPO-AS1 and TMPO expression in TC tumors and cells was detected by TCGA database and QRT-PCR assay respectively. CCK-8, EDU, TUNEL and western blot assays were conducted to identify the biological functions of TMPO-AS1 in TC. Luciferase reporter and RNA pull down assays were conducted to measure the interaction among TMPO-AS1, TMPO and miR-498. Results: TMPO-AS1 was overexpressed in TC tissues and cell lines. Knockdown of TMPO-AS1 suppressed cell growth and accelerated cell apoptosis in TC. Furthermore, downregulation of TMPO-AS1 suppressed TMPO expression in TC. The data suggested that TMPO expression was upregulated in TC tissues and cell lines and was positively correlated with TMPO-AS1 expression in TC. Furthermore, the expression of miR-498 presented low expression in TC cells. And miR-498 expression was negatively regulated by TMPO-AS1, meanwhile, TMPO expression was negatively regulated by miR-498 in TC cells. Besides, it was confirmed that TMPO-AS1 could bind with miR-498 and TMPO in TC cells. In addition, it was validated that TMPO-AS1 elevated the levels of TMPO via sponging miR-498 in TC cells. Conclusions: TMPO-AS1 promotes cell proliferation in TC via sponging miR-498 to modulate TMPO.

Background Triple-negative breast cancer (TNBC) is associated with higher aggressiveness and mortality than hormone-positive breast cancer because of the lack of approved therapeutic targets. Patients with TNBC who attain a pathological complete response (pCR) after neoadjuvant chemotherapy have improved survival. Platinum-based agents show promising activity in TNBC; however, their use remains controversial. We conducted a meta-analysis to assess the role of platinum-based agents in neoadjuvant chemotherapy in patients with TNBC. Methods We performed an extensive literature search of the Pubmed, Embase, and Cochrane databases. We calculated pooled odds ratios (OR) with 95% confidence intervals (CI) for the identified studies. Results Eight randomized controlled trials with 1345 patients were included in the analysis. The addition of platinum-based agents improved pCR compared with neoadjuvant therapy based on anthracyclines, cyclophosphamide, taxanes, and fluorouracil (49.1% vs. 35.9%; OR: 1.87, 95% CI: 1.23–2.86). Hematological adverse events were similar in both groups, except for more thrombocytopenia in the platinum-based group (OR: 7.96, 95% CI: 3.18–19.93). Conclusion The addition of platinum-based agents to neoadjuvant chemotherapy improved pCR rates in patients with TNBC, with a slight increase in hematological toxicities. Platinum-based agents might thus be an accessible and economically viable option in patients with TNBC.