Evaluating uniform manifold approximation and projection for dimension reduction and visualization of polinsar features

In this paper, the nonlinear dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP) is investigated to visualize information contained in high dimensional feature representations of Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data. Based on polarimetric parameters, target decomposition methods and interferometric coherences a wide range of features is extracted that spans the high dimensional feature space. UMAP is applied to determine a representation of the data in 2D and 3D euclidean space, preserving local and global structures of the data and still suited for classification. The performance of UMAP in terms of generating expressive visualizations is evaluated on PolInSAR data acquired by the F-SAR sensor and compared to that of Principal Component Analysis (PCA), Laplacian Eigenmaps (LE) and t-distributed Stochastic Neighbor embedding (t-SNE). For this purpose, a visual analysis of 2D embeddings is performed. In addition, a quantitative analysis is provided for evaluating the preservation of information in low dimensional representations with respect to separability of different land cover classes. The results show that UMAP exceeds the capability of PCA and LE in these regards and is competitive with t-SNE

Investigation on the potential of hyperspectral and Sentinel-2 data for land-cover / land-use classification

The automated analysis of large areas with respect to land-cover and land-use is nowadays typically performed based on the use of hyperspectral or multispectral data acquired from airborne or spaceborne platforms. While hyperspectral data offer a more detailed description of the spectral properties of the Earth’s surface and thus a great potential for a variety of applications, multispectral data are less expensive and available in shorter time intervals which allows for time series analyses. Particularly with the recent availability of multispectral Sentinel-2 data, it seems desirable to have a comparative assessment of the potential of both types of data for land-cover and land-use classification. In this paper, we focus on such a comparison and therefore involve both types of data. On the one hand, we focus on the potential of hyperspectral data and the commonly applied techniques for data-driven dimensionality reduction or feature selection based on these hyperspectral data. On the other hand, we aim to reason about the potential of Sentinel-2 data and therefore transform the acquired hyperspectral data to Sentinel-2-like data. For performance evaluation, we provide classification results achieved with the different types of data for two standard benchmark datasets representing an urban area and an agricultural area, respectively

QTLs for salt tolerance in three different barley mapping populations 2006

Soil salinity is one of the crucial factors limiting crop production. Progression of salinisation of agriculturally arable land is mainly connected with mismanagement of water in irrigation systems, in particular under arid and semiarid climate conditions and global changes of water flow in the landscape. Selection of salt tolerant genotypes is necessary to ensure yield and to reclaim salt affected soils. The development of molecular marker(s) could facilitate the selection process. Phenotyping of mapping populations under salt stress conditions and calculation of QTLs are suitable instruments to detect markers that are responsible for tolerance/sensitivity. However, a quantitative inherited trait like salt tolerance requires a range of adaptations, with a whole host of genes interacting with each other to produce the visible phenotype

Cardiovascular reactivity in a simulated job interview: the role of gender role self-concept

This study investigated the relation of gender role self-concept (G-SC) to cardiovascular and emotional reactions to an ecologically relevant stressor in a sample of graduating male and female university students. Thirty-seven men and 37 women completed the Personal Attribute Questionnaire and worked on four tasks designed to reflect common features of a job interview. Blood pressure and heart rate were measured at baseline, during, and after each task; subjective stress was measured at baseline and after each task. Subjective and objective stress scores were averaged across tasks and analyzed by sex and G-SC (i.e., instrumentality, expressiveness). Results indicated that women as a group demonstrated greater emotional reactivity, but did not differ in their physiological reactions when compared to men. Regardless of sex, participants’ instrumentality scores contributed significantly to the variation in subjective stress response: those scoring high on instrumentality reported less stress, but evidenced greater blood pressure reactivity than those scoring low on instrumentality. These results suggest that gender roles, particularly an instrumental self-concept, may play an important role in both subjective and objective reactions to an ecologically relevant stressor

Formation mechanism of a nano ring of bismuth cations and mono-lacunary Keggin-type phosphomolybdate

A new hetero-bimetallic polyoxometalate (POM) nano ring was synthesized in a one-pot procedure. The structure consists of tetrameric units containing four bismuth-substituted monolacunary Keggin anions including distorted [BiO8] cubes. The nano ring is formed via self -assembly from metal precursors in aqueous acidic medium. The compound (NH4)16[(BiPMo11O39)4]×22H2O; (P4Bi4Mo44) was characterized by single-crystal X-ray diffraction, extended X-ray absorption fine structure spectroscopy (EXAFS), Raman spectroscopy, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF), and thermogravimetry/differential scanning calorimetry (TG-DSC-MS). The formation of the nano ring in solution was studied by time-resolved in situ small- and wide-angle X-ray scattering (SAXS/WAXS) and in situ EXAFS measurements at the Mo-K and the Bi-L3 edge indicating a two-step process consisting of condensation of Mo-anions and formation of Bi-Mo-units followed by a rapid self-assembly to yield the final tetrameric ring structure

The UV-A and visible solar irradiance spectrum: inter-comparison of absolutely calibrated, spectrally medium resolution solar irradiance spectra from balloon- and satellite-borne measurements

International audienceWithin the framework of the ENVISAT/-SCIAMACHY satellite validation, solar irradiance spectra are absolutely measured at moderate resolution in the UV/visible spectral range (in the UV from 316.7–418 nm and the visible from 400–652 nm at a full width half maximum resolution of 0.55 nm and 1.48 nm, respectively) from aboard the azimuth-controlled LPMA/DOAS balloon gondola at around 32 km balloon float altitude. After accounting for the atmospheric extinction due to Rayleigh scattering and gaseous absorption (O3, and NO2), the measured solar spectra are compared with previous observations. Our solar irradiance is +1.6% larger than the re-calibrated Kurucz et al. (1984) solar spectrum (Fontenla et al., 1999, called MODTRAN 3.5) in the visible spectral range (435–650 nm), +1.5% larger in the (370–415 nm) wavelength interval, but -4% smaller in the UV spectral range (316.7–370 nm), when the Kurucz spectrum is convolved to the spectral resolution of our instrument. The same comparison with the SOLSPEC solar spectrum (Thuillier et al., 1997, 1998a, b) confirms the somewhat larger solar irradiance (+1.7%) measured by the balloon instrument from 435–500 nm, but not from 500–650 nm, where the SOLSPEC is -1.3% lower than MODTRAN 3.5. Comparison of the SCIAMACHY solar spectrum from channels 1 to 4 (– re-calibrated by the University of Bremen –) with MODTRAN 3.5 indicates an agreement of +0.2% in the visible spectral range (435–585 nm). With this calibration, the SCIAMACHY solar spectrum is congruent with the balloon observations (-1%) in the 316.7–370 nm wavelength range, but both are up to -5%/-3% smaller than MODTRAN 3.5 and SOLSPEC, respectively. In agreement with findings of Skupin et al. (2002) our study emphasizes that the present ESA SCIAMACHY level 1 calibration is systematically +15% larger in the considered wavelength intervals when compared to all available other solar irradiance measurements

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts