Image Interpretation-Guided Supervised Classification Using Nested Segmentation

We present a new binary (two-class) supervised non-parametric classification approach that is based on iterative partitioning of multidimensional feature space into variably-sized and nested hyper-cubes (partitions). The proposed method contains elements of active learning and includes classifier to analyst queries. The spectral transition zone between two thematic classes (i.e., where training labels of different classes overlap in feature space) is targeted through iterative training derivation. Three partition categories are defined: pure, indivisible and unlabeled. Pure partitions contain training labels from only one class, indivisible partitions contain training data from different classes, and unlabeled partitions do not contain training data. A minimum spectral tolerance threshold defines the smallest partition volume to avoid over-fitting. In this way the transition zones between class distributions are minimized, thereby maximizing both the spectral volume of pure partitions in the feature space and the number of pure pixels in the classified image. The classification results are displayed to show each classified pixel\u27s partition category (pure, unlabeled and indivisible). Mapping pixels belonging to unlabeled partitions serves as a query from the classifier to the analyst, targeting spectral regions absent of training data. The classification process is repeated until significant improvement of the classification is no longer realized or when no classification errors and unlabeled pixels are left. Variably-sized partitions lead to intensive training data derivation in the spectral transition zones between the target classes. The methodology is demonstrated for surface water and permanent snow and ice classifications using 30 m conterminous United States Landsat 7 Enhanced Thematic Mapper Plus (ETM +) data time series from 2006 to 2010. The surface water result was compared with Shuttle Radar Topography Mission (SRTM) water body and National Land Cover Database (NLCD) open water classes with an overall agreement greater than 99% and Kappa coefficient greater than 0.9 in both of cases. In addition, the surface water result was compared with a classification generated using the same input data and a standard bagged Classification and Regression Tree (CART) classifier. The nested segmentation and CART-generated products had an overall agreement of 99.9 and Kappa coefficient of 0.99

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests\u27 absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. Google Earth™ time series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user\u27s accuracy of 78% and a producer\u27s accuracy of 68%. Excluding errors of adjacency, user\u27s and producer\u27s accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (Google Earth™) classification; however, user\u27s (42%) and producer\u27s (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user\u27s and producer\u27s accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user\u27s and producer\u27s accuracies) and urban gain (72% and 18% for respective user\u27s and producer\u27s accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs. gov/CONUS_5Y_LandCover/)

The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system

Global Trends of Forest Loss Due to Fire From 2001 to 2019

Forest fires contribute to global greenhouse gas emissions and can negatively affect public health, economic activity, and provision of ecosystem services. In boreal forests, fires are a part of the ecosystem dynamics, while in the humid tropics, fires are largely human-induced and lead to forest degradation. Studies have shown changing fire dynamics across the globe due to both climate and land use change. However, global trends in fire-related forest loss remain uncertain due to the lack of a globally consistent methodology applied to high spatial resolution data. Here, we create the first global 30-m resolution satellite-based map of annual forest loss due to fire. When producing this map, we match the mapped area of forest loss due to fire to the reference area obtained using a sample-based unbiased estimator, thus enabling map-based area reporting and trend analysis. We find an increasing global trend in forest loss due to fire from 2001 to 2019, driven by near-uniform increases across the tropics, subtropical, and temperate Australia, and boreal Eurasia. The results quantify the increasing threat of fires to remaining forests globally and may improve modeling of future forest fire loss rates under various climate change and development scenarios

How Linear Tension Converts to Curvature: Geometric Control of Bone Tissue Growth

This study investigated how substrate geometry influences in-vitro tissue formation at length scales much larger than a single cell. Two-millimetre thick hydroxyapatite plates containing circular pores and semi-circular channels of 0.5 mm radius, mimicking osteons and hemi-osteons respectively, were incubated with MC3T3-E1 cells for 4 weeks. The amount and shape of the tissue formed in the pores, as measured using phase contrast microscopy, depended on the substrate geometry. It was further demonstrated, using a simple geometric model, that the observed curvature-controlled growth can be derived from the assembly of tensile elements on a curved substrate. These tensile elements are cells anchored on distant points of the curved surface, thus creating an actin “chord” by generating tension between the adhesion sites. Such a chord model was used to link the shape of the substrate to cell organisation and tissue patterning. In a pore with a circular cross-section, tissue growth increases the average curvature of the surface, whereas a semi-circular channel tends to be flattened out. Thereby, a single mechanism could describe new tissue growth in both cortical and trabecular bone after resorption due to remodelling. These similarities between in-vitro and in-vivo patterns suggest geometry as an important signal for bone remodelling

A phase II study of single-agent gefitinib as first-line therapy in patients with stage IV non-small-cell lung cancer

The aim of this study was to evaluate the efficacy and tolerability of gefitinib (‘IRESSA') in Japanese patients with previously untreated stage IV non-small-cell lung cancer (NSCLC). This was a multi-institutional phase II study. Thirty-four patients with previously untreated stage IV NSCLC were enrolled between May 2003 and September 2004. Gefitinib was administered orally 250 mg once a day and was continued until there was either disease progression or severe toxicity. Objective tumour response rate was 26.5% (95% confidence interval, 11.7–41.3%). Adverse events were generally mild (National Cancer Institute-Common Toxicity Criteria grade 1 or 2) and consisted mainly of skin rash, fatigue and liver dysfunction. No pulmonary toxicity was observed. The global health status revealed that there was no change in quality of life during the study. This study found that single-agent gefitinib is active and well tolerated in chemonaive Japanese patients with advanced NSCLC

Antitumor effect of sFlt-1 gene therapy system mediated by Bifidobacterium Infantis on Lewis lung cancer in mice

Soluble fms-like tyrosine kinase receptor (sFlt-1) is a soluble form of extramembrane part of vascular endothelial growth factor receptor-1 (VEGFR-1) that has antitumor effects. Bifidobacterium Infantis is a kind of non-pathogenic and anaerobic bacteria that may have specific targeting property of hypoxic environment inside of solid tumors. The aim of this study was to construct Bifidobacterium Infantis-mediated sFlt-1 gene transferring system and investigate its antitumor effect on Lewis lung cancer (LLC) in mice. Our results demonstrated that the Bifidobacterium Infantis-mediated sFlt-1 gene transferring system was constructed successfully and the system could express sFlt-1 at the levels of gene and protein. This system could not only significantly inhibit growth of human umbilical vein endothelial cells induced by VEGF in vitro, but also inhibit the tumor growth and prolong survival time of LLC C57BL/6 mice safely. These data suggest that Bifidobacterium Infantis-mediated sFlt-1 gene transferring system presents a promising therapeutic approach for the treatment of cancer

Induction of broad immunity by thermostabilised vaccines incorporated in dissolvable microneedles using novel fabrication methods

Dissolvable microneedle (DMN) patches for immunization have multiple benefits, including vaccine stability and ease-of-use. However, conventional DMN fabrication methods have several drawbacks. Here we describe a novel, microfluidic, drop dispensing-based dissolvable microneedle production method that overcomes these issues. Uniquely, heterogeneous arrays, consisting of microneedles of diverse composition, can be easily produced on the same patch. Robustness of the process was demonstrated by incorporating and stabilizing adenovirus and MVA vaccines. Clinically-available trivalent inactivated influenza vaccine (TIV) in DMN patches is fully stable for greater than 6months at 40°C. Immunization using low dose TIV-loaded DMN patches induced significantly higher antibody responses compared to intramuscular-based immunization in mice. TIV-loaded patches also induced a broader, heterosubtypic neutralizing antibody response. By addressing issues that will be faced in large-scale fill-finish DMN fabrication processes and demonstrating superior thermostable characteristics and immunogenicity, this study progresses the translation of this microneedle platform to eventual clinical deployment