Modifying Photos Based on Photo Capture from Multiple Device Cameras

This disclosure describes techniques to modify photos based on additional photos captured from multiple cameras of a device. A user captures a main photo of a scene with one camera of a device, and, in the background, additional photo(s) are automatically captured by other camera(s) of the device. Portions from the additional photos can be used to modify the main photo. For example, the view and/or aspect ratio of the captured photo can be modified to show portions of the scene that are not present in the main photo. This saves the user time and effort by automatically capturing additional photos of a scene and providing a range of editing options that take advantage of the additional photos

Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study

In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predicted simultaneously with R2 > 0.5 (RPD > 1.50). Improved predictions were observed for most soil properties based on sensor data fusion than those based on individual sensors. After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). Improved predictions for SOM, Ca, Mg, and Al were observed using SVM over PLSR. The predictive capability was followed by RF and MARS, with CART. Predictions of pH and LBC were only feasible using MARS and PLSR, respectively. In this field, it was not possible to predict extractable P and K using all tested sensor combinations or algorithms. With large variability in SOM, the field presents a special situation and thus, the result could be specific to the study site. Further research includes an extended number of experimental sites covering different geographic areas around Eastern Canada

The effect and mechanism of endothelin-1-induced intracellular free calcium in human lung adenocarcinoma cells SPC-A1

Background and objective Endothelin-1 (ET-1) is a potent mitogen involved in cell growth in human lung adenocarcinoma cells SPC-A1. The increase in intracellular free calcium ([Ca2+]i) plays a great role in this process. The aim of this study is to investigate the ET-1-induced [Ca2+]i responses in SPC-A1 cells and to explore its cellular mechanism. Methods [Ca2+]i was measured by Fura-2/AM fluorescent assay. Endothelin receptors antagonists, calcium channel blockers and intracellular signal transduction blockers were used to study the underlying mechanism of ET-1-induced [Ca2+]i responses in SPC-A1 cells. Results At the concentration of 1×10-15 mol/L-1×10-8 mol/L, ET-1 caused a dose-dependent increase of [Ca2+]i in SPC-A1 cells (P0.05), a highly selective endothelin receptor B (ETBR) antagonist. Depletion of extracellular Ca2+ with free Ca2+ solution and 0.1mmol/L ethyleneglycol bis (2-aminoethyl ether) tetraacetic acid (EGTA) or blockade of voltage dependent calcium channel with nifedipine at 1×10-6 mol/L significantly reduced the ET-1-induced increase of [Ca2+]i. The ET-1-induced (1×10-10 mol/L) increase of [Ca2+]i was also significantly attenuated by U73122 at 1×10-5 mol/L (P<0.05), a phospholipase C inhibitor, and by Ryanodine at 50×10-6 mol/L. However, Staurosporine (2×10-9 mol/L), a protein kinas C inhibitor, exerted no significant effect on the ET-1-induced (1×10-10 mol/L) increase of [Ca2+]i. Conclusion ET-1 elevates [Ca2+]i via activation of ETA receptor. Both phospholipase C/Ca2+ pathway and Ca2+ influx through voltage dependent Ca2+ channel activate by ETAR contribute to this process

Anti-Jamming Tracking Algorithm for Ship Target Based on Correlation Filtering

For solving the problem of tracking drift or loss caused by morphological changes and scale scaling in ship target tracking, this paper designs a target tracking algorithm based on multi feature fusion and threshold selection to resist morphological changes and scale transformation interference. In the anti morphological change module, a multi feature weighted fusion method is designed. Through the adaptive weighted fusion of histogram of oriented gradients (HOG), local bninary patterns (LBP) and color names (CN) by the contribution rate of color moment feature recognition, the feature extraction ability of important parts of ship targets is strengthened, and the robustness of the proposed algorithm in the tracking process is improved. In the anti scale transformation interference module, a method with multi resolution target box joint search method and determination of target position by the maximum response peak is designed to solve the problem of low robustness of the tracking box caused by the scaling of ship targets. The experimental results show that the proposed algorithm has better tracking performance, with an accuracy of 93.6% and a success rate of 70.1% on the OTB dataset. This method is superior to other related algorithms

Rhodamine B-co-condensed spherical SBA-15 nanoparticles: facile co-condensation synthesis and excellent fluorescence features

Novel rhodamine B-co-condensed spherical SBA-15 nanoparticles (RhB-Cc-SBA-15NPs) and rhodamine B-post-grafted short columnar SBA-15 nanoparticles (RhB-Pg-SBA-15NPs) have been synthesized by a facile co-condensation approach and a relatively complicated post-grafting route, respectively. SEM, TEM, SAXRD, FTIR, absorption spectroscopy, fluorescence spectroscopy and nitrogen adsorption-desorption techniques were employed to characterize the morphology, the mesostructure and the spectral features of fluorescence nanocomposites with different doping amounts of RhB. The results show that (1) the facile co-condensation method involves an electron-donor- acceptor (EDA) composite micelle, which plays two significant roles of uniformly dispersing RhB groups within the ordered mesopore channels and effectively adjusting the morphology and particle size of SBA-15 in the nano-scale; (2) novel spherical RhB-Cc-SBA-15NPs with a uniform particle size of ca. 400 nm have been obtained for the first time; (3) large numbers of RhB groups are covalently bound and monodisperse within the ordered mesoporous channels of the RhB-Cc-SBA-15NPs owing to the facile co-condensation method, however, most probably aggregated on the outside surface of the RhB-Pg-SBA-15NPs; (4) compared with other dye-doped silica materials reported previously, RhB-Cc-SBA-15NPs exhibit uniform size, high dispersivity and doping amounts of the bound RhB, high fluorescence quantum yields and fluorescence detectivity, and excellent photostability. RhB-Cc-SBA-15NPs, combining the advantages of a well-defined morphology and mesostructure, exhibit excellent fluorescence features, and present great potential for applications in drug delivery and fluorescence probing for medical diagnosis and synchronous therapy

A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** &#8804; r &#8804; 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 &#8804; r &#8804; 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops

Fabrication of mesoporous zeolite microspheres by a one-pot dual-functional templating approach

Mesoporous microspheres of zeolite have been fabricated through direct self-assembly between an aluminosilicate precursor, tetrapropylammonium hydroxide (TPAOH) and poly(methyl methacrylate) (PMMA) nanospheres, where the PMMA nanospheres act as dual-functional templates for the generation of both mesoporosity and spherical morphology