A portable NIR fluorimeter directly quantifies singlet oxygen generated by nanostructures for Photodynamic Therapy.

This paper reports on the setting up and calibration of a portable NIR fluorimeter specifically developed for quantitative direct detection of the highly reactive singlet oxygen (1O2) chemical specie, of great importance in Photodynamic therapies. This quantification relies on the measurement of fluorescence emission of 1O2, which is peaked in the near-infrared (NIR) at λ=1270nm. In recent years, several nanostructures capable of generating reactive oxygen species (ROS) when activated by penetrating radiation (X-rays, NIR light) have been developed to apply Photodynamic Therapy (PDT) to tumours in deep tissue, where visible light cannot penetrate. A bottleneck in the characterization of these nanostructures is the lack of a fast and reliable technique to quantitatively assess their performances in generating ROS, and in particular 1O2. For instance, the widely used PDT "Singlet Oxygen Sensor Green" kit suffers from self-activation under X-ray irradiation. To solve this difficulty, we propose here direct detection of 1O2 by spectroscopic means, using an apparatus developed by us around a recent thermoelectrically-cooled InGaAs single photon avalanche photodiode (SPAD). The SPAD is coupled to a custom-made integrating sphere designed for use under irradiation with high-energy X-ray beams from clinical Radiotherapy sources. We determine the detection threshold for our apparatus, which turns to be ∼9·1081O2 in realistic experimental condition and for measurements extending to 1 min of integration. After calibrations on standard photosensitizers, we demonstrate the potentiality of this instrument characterizing some photosensitizing nanostructures developed by us.

Tooth loss in complying and non-complying periodontitis patients with different periodontal risk levels during supportive periodontal care.

OBJECTIVES: To evaluate yearly tooth loss rate (TLR) in periodontitis patients with different periodontal risk levels who had complied or not complied with supportive periodontal care (SPC). MATERIALS AND METHODS: Data from 168 periodontitis patients enrolled in a SPC program based on a 3-month suggested recall interval for at least 3.5 years were analyzed. For patients with a mean recall interval within 2-4 months ("compliers") or > 4 months ("non-compliers") with different PerioRisk levels (Trombelli et al. 2009), TLR (irrespective of the cause for tooth loss) was calculated. TLR values were considered in relation to meaningful TLR benchmarks from the literature for periodontitis patients either under SPC (0.15 teeth/year; positive benchmark) or irregularly complying with SPC (0.36 teeth/year; negative benchmark). RESULTS: In both compliers and non-compliers, TLR was significantly below or similar to the positive benchmark in PerioRisk level 3 (0.08 and 0.03 teeth/year, respectively) and PerioRisk level 4 (0.12 and 0.18 teeth/year, respectively). Although marked and clinically relevant in non-compliers, the difference between TLR of compliers (0.32 teeth/year) and non-compliers (0.52 teeth/year) with PerioRisk level 5 and the negative benchmark was not significant. CONCLUSION: A SPC protocol based on a 3- to 6-month recall interval may effectively limit long-term tooth loss in periodontitis patients with PerioRisk levels 3 and 4. A fully complied 3-month SPC protocol seems ineffective when applied to PerioRisk level 5 patients. CLINICAL RELEVANCE: PerioRisk seems to represent a valid tool to inform the SPC recall interval as well as the intensity of active treatment prior to SPC enrollment.

Big Earth data: disruptive changes in Earth observation data management and analysis?

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term 'big Earth data' emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory.

ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006-2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1-Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2-Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 2: Validation.

ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2-Validation-accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

Semantic and syntactic interoperability in online processing of big Earth observation data.

The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases.

Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model.

A multiscale expectation-maximization semisupervised classifier suitable for badly posed image classification.

This paper deals with the problem of badly posed image classification. Although underestimated in practice, bad-posedness is likely to affect many real-world image classification tasks, where reference samples are difficult to collect (e.g., in remote sensing (RS) image mapping) and/or spatial autocorrelation is relevant. In an image classification context affected by a lack of reference samples, an original inductive learning multiscale image classifier, termed multiscale semisupervised expectation maximization (MSEM), is proposed. The rationale behind MSEM is to combine useful complementary properties of two alternative data mapping procedures recently published outside of image processing literature, namely, the multiscale modified Pappas adaptive clustering (MPAC) algorithm and the sample-based semisupervised expectation maximization (SEM) classifier. To demonstrate its potential utility, MSEM is compared against nonstandard classifiers, such as MPAC, SEM and the single-scale contextual SEM (CSEM) classifier, besides against well-known standard classifiers in two RS image classification problems featuring few reference samples and modestly useful texture information. These experiments yield weak (subjective) but numerous quantitative map quality indexes that are consistent with both theoretical considerations and qualitative evaluations by expert photointerpreters. According to these quantitative results, MSEM is competitive in terms of overall image mapping performance at the cost of a computational overhead three to six times superior to that of its most interesting rival, SEM. More in general, our experiments confirm that, even if they rely on heavy class-conditional normal distribution assumptions that may not be true in many real-world problems (e.g., in highly textured images), semisupervised classifiers based on the iterative expectation maximization Gaussian mixture model solution can be very powerful in practice when: 1) there is a lack of reference samples with respect to the problem/model complexity and 2) texture information is considered negligible (i.e., a piecewise constant image model holds).