Data compression experiments with LANDSAT thematic mapper and Nimbus-7 coastal zone color scanner data

A case study is presented where an image segmentation based compression technique is applied to LANDSAT Thematic Mapper (TM) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. The compression technique, called Spatially Constrained Clustering (SCC), can be regarded as an adaptive vector quantization approach. The SCC can be applied to either single or multiple spectral bands of image data. The segmented image resulting from SCC is encoded in small rectangular blocks, with the codebook varying from block to block. Lossless compression potential (LDP) of sample TM and CZCS images are evaluated. For the TM test image, the LCP is 2.79. For the CZCS test image the LCP is 1.89, even though when only a cloud-free section of the image is considered the LCP increases to 3.48. Examples of compressed images are shown at several compression ratios ranging from 4 to 15. In the case of TM data, the compressed data are classified using the Bayes' classifier. The results show an improvement in the similarity between the classification results and ground truth when compressed data are used, thus showing that compression is, in fact, a useful first step in the analysis

Insilico interaction analysis of herbal bioactive molecules with Penicillin binding protein in staphylococcus aureus

Staphylococcus aureus is an opportunistic bacterial pathogen associated with asymptomatic colonization of the skin and mucosal surfaces of normal humans. Staphylococcus aureus isolates are often multidrug resistant. Antibiotics resistant Staphylococcus aureus is an emerging concern in the medical field. Due to their increasing resistance to numerous antibiotics, screening for alternate compound is required. Penicillin binding protein (PBP) was considered as an essential drug target for inhibiting bacterial growth. Molecular docking studies were performed to identify the lead molecule against Penicillin binding protein. Interaction of PBP with the plant derived compounds indicates the effective interaction of rosmarinic acid with highest fitness score and maximum number of hydrogen bonds (h-bonds). The study concludes that rosmarinic acid may be effective as an inhibitor for PBP and hence, can be regarded as a potential drug candidate for treating β-lactam resistant Staphylococcus aureu

Metallic Xenon, Molecular Condensates, and Superconductivity

A possibility of explaining the light absorption observed to occur under pressure-induced xenon metallization as due to the transition to the superconducting state is analyzed. The mechanism of the van der Waals bonding is discussed.Comment: LaTeX 2.09 (RevTeX), 4 pages, 4 PostScript figures included in tex

A comparative study of nonlinear filtering techniques

Abstract-In a recent work it is shown that importance sampling can be avoided in the particle filter through an innovation structure inspired by traditional nonlinear filtering combined with optimal control formalisms. The resulting algorithm is referred to as feedback particle filter. The purpose of this paper is to provide a comparative study of the feedback particle filter (FPF). Two types of comparisons are discussed: i) with the extended Kalman filter, and ii) with the conventional resampling-based particle filters. The comparison with Kalman filter is used to highlight the feedback structure of the FPF. Also computational cost estimates are discussed, in terms of number of operations relative to EKF. Comparison with the conventional particle filtering approaches is based on a numerical example taken from the survey article on the topic of nonlinear filtering A secondary purpose of this paper is to provide a summary of the FPF algorithm, that can aid practitioners to rapidly implement the algorithm. A detailed algorithm (pseudo-code) is included, and compared against an EKF algorithm. Such comparisons also help highlight the feedback structure of the FPF algorithm

Polycyclic aromatic hydrocarbons as skin carcinogens:Comparison of benzo [a]pyrene, dibenzo[def,p]chrysene and three environmental mixtures in the FVB/N mouse

The polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), was compared to dibenzo[def,p]chrysene (DBC) and combinations of three environmental PAH mixtures (coal tar, diesel particulate and cigarette smoke condensate) using a two stage, FVB/N mouse skin tumor model. DBC (4 nmol) was most potent, reaching 100% tumor incidence with a shorter latency to tumor formation, less than 20 weeks of 12-O-tetradecanoylphorbol-13-acetate (TPA) promotion compared to all other treatments. Multiplicity was 4 times greater than BaP (400 nmol). Both PAHs produced primarily papillomas followed by squamous cell carcinoma and carcinoma in situ. Diesel particulate extract (1 mg SRM 1650b; mix 1) did not differ from toluene controls and failed to elicit a carcinogenic response. Addition of coal tar extract (1 mg SRM 1597a; mix 2) produced a response similar to BaP. Further addition of 2 mg of cigarette smoke condensate (mix 3) did not alter the response with mix 2. PAH-DNA adducts measured in epidermis 12 h post initiation and analyzed by (32)P post- labeling, did not correlate with tumor incidence. PAH- dependent alteration in transcriptome of skin 12 h post initiation was assessed by microarray. Principal component analysis (sum of all treatments) of the 922 significantly altered genes (p<0.05), showed DBC and BaP to cluster distinct from PAH mixtures and each other. BaP and mixtures up-regulated phase 1 and 2 metabolizing enzymes while DBC did not. The carcinogenicity with DBC and two of the mixtures was much greater than would be predicted based on published Relative Potency Factors (RPFs)

Effect of acute copper sulfate exposure on olfactory responses to amino acids and pheromones in goldfish (Carassius auratus)

Exposure of olfactory epithelium to environmentally relevant concentrations of copper disrupts olfaction in fish. To examine the dynamics of recovery at both functional and morphological levels after acute copper exposure, unilateral exposure of goldfish olfactory epithelia to 100 μM CuSO4 (10 min) was followed by electro-olfactogram (EOG) recording and scanning electron microscopy. Sensitivity to amino acids (L-arginine and L-serine), generally considered food-related odorants, recovered most rapidly (three days), followed by that to catecholamines(3-O-methoxytyramine),bileacids(taurolithocholic acid) and the steroid pheromone, 17,20 -dihydroxy-4-pregnen- 3-one 20-sulfate, which took 28 days to reach full recovery. Sensitivity to the postovulatory pheromone prostaglandin F2R had not fully recovered even at 28 days. These changes in sensitivity were correlated with changes in the recovery of ciliated and microvillous receptor cell types. Microvillous cells appeared largely unaffected by CuSO4 treatment. Cilia in ciliated receptor neurones, however, appeared damaged one day post-treatment and were virtually absent after three days but had begun to recover after 14 days. Together, these results support the hypothesis that microvillous receptor neurones detect amino acids whereas ciliated receptor neurones were not functional and are responsible for detection of social stimuli (bile acidsandpheromones).Furthermore, differences in sensitivity to copper may be due to different transduction pathways in the different cell types

QSO Absorption Systems Detected in Ne VIII: High-Metallicity Clouds with a Large Effective Cross Section

Using high resolution, high signal-to-noise ultraviolet spectra of the z = 0.9754 quasar PG1148+549 obtained with the Cosmic Origins Spectrograph (COS) on the Hubble Space Telescope, we study the physical conditions and abundances of NeVIII+OVI absorption line systems at z(abs) =0.68381, 0.70152, 0.72478. In addition to NeVIII and OVI, absorption lines from multiple ionization stages of oxygen (OII, OIII, OIV) are detected and are well-aligned with the more highly ionized species. We show that these absorbers are multiphase systems including hot gas (T ~ 10^{5.7} K) that produces NeVIII and OVI, and the gas metallicity of the cool phase ranges from Z = 0.3 Z_{solar} to supersolar. The cool (~10^{4} K) phases have densities n_{H} ~ 10^{-4} cm^{-3} and small sizes (< 4kpc); these cool clouds are likely to expand and dissipate, and the NeVIII may be within a transition layer between the cool gas and a surrounding, much hotter medium. The NeVIII redshift density, dN/dz = 7^{+7}_{-3}, requires a large number of these clouds for every L > 0.1L* galaxy and a large effective absorption cross section (>~ 100 kpc), and indeed, we find a star forming ~L* galaxy at the redshift of the z(abs)=0.72478 system, at an impact parameter of 217 kpc. Multiphase absorbers like these NeVIII systems are likely to be an important reservoir of baryons and metals in the circumgalactic media of galaxies.Comment: Final published version (Astrophysical Journal

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail

An Automated Crop Intensity Algorithm (ACIA) for global cropland intensity mapping at 30-m using multi-source time-series data and Google Earth Engine

Agricultural practices and investments vary by season due to the different challenges faced, such as drought, salinity, or flooding, and the different requirements such as varietal choice, water source (rainfall or irrigation or a mix of the two), inputs, and crop establishment methods. Both cropping intensity and the number of crops planted annually in an area can be used as a measures of food security given that these factors can greatly affect overall gross production for that location. Traditionally, MODIS Normalized Difference Vegetation Index (NDVI) has routinely been used to investigate crop intensity, since MODIS provides global coverage and sufficient temporal observance. However, current techniques for quantifying cropping intensity may not be accurate in regions of the world like Africa and South Asia, which are dominated by smallholder farms where the size of one field is typically much smaller than a MODIS pixel (250 m). As a result, we investigated the capability of combining Landsat 8 (16 day – 30 m) data with Sentinal-2 (5 day – 10 to 20 m) to map crop intensity over very large areas such as Africa and South Asia. We developed the Automated Crop Intensity Algorithm (ACIA) to produce crop intensity at 30-m using our Landsat-8 and Sentinel-2 combination data through Google Earth Engine (GEE) cloud computing platform. We smoothed the temporal 30-m NDVI data from our Landsat-8 and Sentinel-2 combination to overcome cloud effects. The 30-m NDVI time-series were then identified into 4 crop intensity classes with ACIA. The 4 crop intensity classes were: 1) Single crop, season 1, 2) Single crop season 2, 3) Double crop, and 4) continuous crop. When performing cropping intensity mapping with ACIA, we introduced the global crop seasonality map from SAGE Crop Calendar Dataset (Sacks, 2010) as a reference. The final 30-m crop intensity map was evaluated by comparing with: 1) random samples interpreted by validators; 2) MODIS results; and 3) survey-based statistics. Although temporal frequency of our Landsat-8 and Sentinel-2 30-m combination is far less than daily coverage of MODIS data, the results of crop intensity were comparable between the ACIA algorithm and MODIS r for the study areas (R2 > 0.75 and RMSE < 0.15). Given the scaling ability provided by Google Earth Engine, the ACIA algorithm can also be applied to agricultural croplands anywhere in the world