Global approaches and local strategies for phase unwrapping

Phase unwrapping, i.e. the retrieval of absolute phases from wrapped, noisy measures, is a tough problem because of the presence of rotational inconsistencies (residues), randomly generated by noise and undersampling on the principal phase gradient field. These inconsistencies prevent the recovery of the absolute phase field by direct integration of the wrapped gradients. In this paper we examine the relative merit of known global approaches and then we present evidence that our approach based on “stochastic annealing” can recover the true phase field also in noisy areas with severe undersampling, where other methods fail. Then, some experiments with local approaches are presented. A fast neural filter has been trained to eliminate close residue couples by joining them in a way which takes into account the local phase information. Performances are about 60–70% of the residues. Finally, other experiments have been aimed at designing an automated method for the determination of weight matrices to use in conjunction with local phase unwrapping algorithms. The method, tested with the minimum cost flow algorithm, gives good performances over both simulated and real data

Synergistic interaction of fatty acids and oxysterols impairs mitochondrial function and limits liver adaptation during nafld progression

The complete mechanism accounting for the progression from simple steatosis to steatohepatitis in nonalcoholic fatty liver disease (NAFLD) has not been elucidated. Lipotoxicity refers to cellular injury caused by hepatic free fatty acids (FFAs) and cholesterol accumulation. Excess cholesterol autoxidizes to oxysterols during oxidative stress conditions. We hypothesize that interaction of FAs and cholesterol derivatives may primarily impair mitochondrial function and affect biogenesis adaptation during NAFLD progression. We demonstrated that the accumulation of specific non-enzymatic oxysterols in the liver of animals fed high-fat+high-cholesterol diet induces mitochondrial damage and depletion of proteins of the respiratory chain complexes. When tested in vitro, 5α-cholestane-3β,5,6β-triol (triol) combined to FFAs was able to reduce respiration in isolated liver mitochondria, induced apoptosis in primary hepatocytes, and down-regulated transcription factors involved in mitochondrial biogenesis. Finally, a lower protein content in the mitochondrial respiratory chain complexes was observed in human non-alcoholic steatohepatitis. In conclusion, hepatic accumulation of FFAs and non-enzymatic oxysterols synergistically facilitates development and progression of NAFLD by impairing mitochondrial function, energy balance and biogenesis adaptation to chronic injury

Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC)

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/semi-automatic translation framework of LC/LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India

Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: A Mediterranean assessment

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies—CORINE Land Cover, the Food and Agricultural Organisation (FAO) land cover classification system (LCCS) and the International Geosphere-Biosphere Programme to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring—a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result

A Survey of Fuzzy Clustering Algorithms for Pattern Recognition

Clustering algorithms aim at modelling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where clustering systems can be compared on the basis of their learning strategies. In the first part of this work, the following issues are reviewed: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, growing and pruning networks, modular network architectures, topologically perfect mapping, ecological nets and neuro-fuzziness. From this discussion an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed as a unifying framework in the comparison of clustering systems. Moreover, a set of functional attributes is selected for use as dictionary entries in our comparison. In the second part of this paper, five clustering algorithms taken from the literature are reviewed and compared on..