A general framework for processing high and veryhigh resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km² of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.Index Terms-Built-up density, CSL, global human settlement layer, linear regression, PANTEX, urban limits.

Background Long noncoding RNAs (lncRNAs) have emerged as critical players in cancer progression, but their functions in colorectal cancer (CRC) metastasis have not been systematically clarified. Methods lncRNA expression profiles in matched normal and CRC tissue were checked using microarray analysis. The biological roles of a novel lncRNA, namely RP11-138 J23.1 (RP11), in development of CRC were checked both in vitro and in vivo. Its association with clinical progression of CRC was further analyzed. Results RP11 was highly expressed in CRC tissues, and its expression increased with CRC stage in patients. RP11 positively regulated the migration, invasion and epithelial mesenchymal transition (EMT) of CRC cells in vitro and enhanced liver metastasis in vivo. Post-translational upregulation of Zeb1, an EMT-related transcription factor, was essential for RP11-induced cell dissemination. Mechanistically, the RP11/hnRNPA2B1/mRNA complex accelerated the mRNA degradation of two E3 ligases, Siah1 and Fbxo45, and subsequently prevented the proteasomal degradation of Zeb1. m 6 A methylation was involved in the upregulation of RP11 by increasing its nuclear accumulation. Clinical analysis showed that m 6 A can regulate the expression of RP11, further, RP11 regulated Siah1-Fbxo45/Zeb1 was involved in the development of CRC. Conclusions m 6 A-induced lncRNA RP11 can trigger the dissemination of CRC cells via post-translational upregulation of Zeb1. Considering the high and specific levels of RP11 in CRC tissues, our present study paves the way for further investigations of RP11 as a predictive biomarker or therapeutic target for CRC. Electronic supplementary material The online version of this article (10.1186/s12943-019-1014-2) contains supplementary material, which is available to authorized users.

Background Early treatment is key for optimizing the therapeutic success of drugs, and the current initiating treatment that blocks the progression of bone destruction during the pre-arthritic stages remains unsatisfactory. The microbial disorder in rheumatoid arthritis (RA) patients is significantly reversed with effective treatment. Modulating aberrant gut microbiomes into a healthy state is a potential therapeutic approach for preventing bone damage. Results By using metagenomic shotgun sequencing and a metagenome-wide association study, we assessed the effect of Lactobacillus casei ( L. casei ) on the induction of arthritis as well as on the associated gut microbiota and immune disorders in adjuvant-induced arthritis (AIA) rats. Treatment of AIA rats with L. casei inhibited joint swelling, lowered arthritis scores, and prevented bone destruction. Along with the relief of arthritis symptoms, dysbiosis in the microbiome of arthritic rats was significantly reduced after L. casei intervention. The relative abundance of AIA-decreased Lactobacillus strains, including Lactobacillus hominis , Lactobacillus reuteri , and Lactobacillus vaginalis , were restored to normal and Lactobacillus acidophilus was upregulated by the administration of L. casei to the AIA rats. Moreover, L. casei downregulated the expression of pro-inflammatory cytokines, which are closely linked to the effect of the L. casei treatment-associated microbes . Functionally, the maintenance of the redox balance of oxidative stress was involved in the improvement in the L. casei -treated AIA rats. Conclusion A single bacterium, L. casei (ATCC334), was able to significantly suppress the induction of AIA and protect bones from destruction in AIA rats by restoring the microbiome dysbiosis in the gut, indicating that using probiotics may be a promising strategy for treating RA, especially in the early stage of the disease. Electronic supplementary material The online version of this article (10.1186/s40168-019-0719-1) contains supplementary material, which is available to authorized users.

Background Chemotherapeutic resistance is the main cause of clinical treatment failure and poor prognosis in triple-negative breast cancer (TNBC). There is no research on chemotherapeutic resistance in TNBC from the perspective of circular RNAs (circRNAs). Methods TNBC-related circRNAs were identified based on the GSE101124 dataset. Quantitative reverse transcription PCR was used to detect the expression level of circWAC in TNBC cells and tissues. Then, in vitro and in vivo functional experiments were performed to evaluate the effects of circWAC in TNBC. Results CircWAC was highly expressed in TNBC and was associated with worse TNBC patient prognosis. Subsequently, it was verified that downregulation of circWAC can increase the sensitivity of TNBC cells to paclitaxel (PTX) in vitro and in vivo. The expression of miR-142 was negatively correlated with circWAC in TNBC. The interaction between circWAC and miR-142 in TNBC cells was confirmed by RNA immunoprecipitation assays, luciferase reporter assays, pulldown assays, and fluorescence in situ hybridization. Mechanistically, circWAC acted as a miR-142 sponge to relieve the repressive effect of miR-142 on its target WWP1. In addition, the overall survival of TNBC patients with high expression of miR-142 was significantly better than that of patients with low expression of miR-142, and these results were verified in public databases. MiR-142 regulated the expression of WWP1 and the activity of the PI3K/AKT pathway. It was confirmed that WWP1 is highly expressed in TNBC and that the prognosis of patients with high WWP1 expression is poor. Conclusions CircWAC/miR-142/WWP1 form a competing endogenous RNA (ceRNA) network to regulate PI3K/AKT signaling activity in TNBC cells and affect the chemosensitivity of cells.

Abstract:Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies.

The mechanistic action of bromodomain-containing protein 4 (BRD4) in cancer motility, including epithelial-mesenchymal transition (EMT), remains largely undefined. We found that targeted inhibition of BRD4 reduces migration, invasion, in vivo growth of patient-derived xenograft (PDX), and lung colonization of breast cancer (BC) cells. Inhibition of BRD4 rapidly decreases the expression of Snail, a powerful EMT transcription factor (EMT-TF), via diminishing its protein stability and transcription. Protein kinase D1 (PRKD1) is responsible for BRD4-regulated Snail protein stability by triggering phosphorylation at Ser11 of Snail and then inducing proteasome-mediated degradation. BRD4 inhibition also suppresses the expression of Gli1, a key transductor of Hedgehog (Hh) required to activate the transcription of SNAI1, in BC cells. The GACCACC sequence (−341 to −333) in the SNAI1 promoter is responsible for Gli1-induced transcription of SNAI1. Clinically, BRD4 and Snail levels are increased in lung-metastasized, estrogen receptor-negative (ER-), and progesterone receptor-negative (PR-) breast cancers and correlate with the expression of mesenchymal markers. Collectively, BRD4 can regulate malignancy of breast cancer cells via both transcriptional and post-translational regulation of Snail.

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series' suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001-2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.

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