Mapping big brains at subcellular resolution in the era of big data in zoology

Connectome-scale structural mapping is fundamental for understanding the underlying mechanisms of brain cognition and brain disease pathogenesis.By combining rapidly developing three-dimensional(3D)imaging techniques and big data analysis methods,researchers are working on mesoscale mapping of mammalian brains at an accelerated pace.Here,we briefly describe existing brain-wide imaging strategies,especially our recently established primateoptimized pipeline capable of pan-brain neuronal connectivity mapping at subcellular resolution,and further discuss their vast application prospects in the big data era of zoology.

Quantification and spatial distribution of salicylic acid in film tablets using FT-Raman mapping with multivariate curve resolution

In this study, we proposed a rapid and sensitive method for quantification and spatial distribution of salicylic acid in film tablets using FT-Raman spectroscopy with multivariate curve resolution(MCR). For this purpose, the constituents of film tablets were identified by using FT-Raman spectroscopy, and then eight different concentrations of salicylic acid tablets were visualized by Raman mapping. MCR was applied to mapping data to expose the active pharmaceutical ingredients in the presence of other excipients by monitoring distribution maps and combination of FT-Raman mapping with MCR enabled the determination of lower salicylic acid concentrations. In addition, the distribution of major excipient, lactose, was examined in the tablet form. A calibration curve was obtained by plotting the intensity of the Raman signal at 1635 cm^(-1) versus the concentration of salicylic acid and the correlation was found to be linear within the range of 0.5%–3.9% with a correlation coefficient of 0.99. The limit of detection for the technique was determined 0.35%. The ability of the technique to quantify salicylic acid in tablet test samples was also investigated.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Application of Ultra-High Resolution STXM-NEXAFS and Nano-SIMS on the Mapping of Carbon Related Samples

Nano-Scale mapping of minerals and organic compounds give unprecedented high resolution information on the origin and nature of substances,and provide new insight on their correlative distribution and interaction,thus present a powerful tool to study the progressive changes of geological samples,and may even be applied to study extraterrestrial samples in search of life.One example we present here explore the use of elemental microprobe,X-Ray Photon Spectroscopy(XPS),and synchrotron-based Scanning Transmission X-ray Microscopy(STXM) coupled with Near Edge X-ray Absorption Fine Structure(NEXAFS) Spectroscopy to investigate the surface properties and stability of micron-size organic carbonaceous particles from Central Amazon,Brazil,specifically focusing on black carbon in Kaolinitic Oxisol originated from anthropogenic processes,and their interaction with cations.

Edge-Preserving Depth Map Super-Resolution with Intensity Guidance

Depth discontinuity edge affects the visual quality of synthesized images in 3D image warping.However,it suffers from accuracy degradation when up-sampled from low-resolution depth maps,especially at large scaling factors.To preserve the accuracy of depth discontinuity,a novel joint bilateral depth super-resolution with intensity guidance method is proposed.Particularly,the fast local intensity classification is exploited to estimate depth coefficients in joint bilateral up-sampling for depth maps,so as to eliminate depth discontinuity edge misalignment.Additionally,the proposed method is accelerated on graphic processing units(GPUs)to meet the requirement of realtime application.Experiments demonstrate that our method can preserve the accuracy of depth discontinuity edges after super resolution,leveraging the visual quality of synthesized image in 3D image warping.

Influence of the interaction volume on the kinetic energy resolution of a velocity map imaging spectrometer

We investigate the influence of the interaction volume on the energy resolution of a velocity map imaging spectrometer. The simulation results show that the axial interaction size has a significant influence on the resolution. This influence is increased for a higher kinetic energy. We further show that the radial interaction size has a minor influence on the energy resolution for the electron or ion with medium energy, but it is crucial for the resolution of the electron or ion With low kinetic energy. By tracing the flight trajectories we show how the electron or ion energy resolution is influenced by the interaction size.

Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method

We tried more precise mapping of vegetation using UAV?(unmanned aerial vehicle), as a new method of creating vegetation maps, and we?objected to be clearly the efficient mapping of vegetation using the UAV method by comparing vegetation maps created by analysing aerial photographs taken by a UAV and an aircraft (manned flight). The aerial photography using UAV was conducted in the Niida River estuary (the secondary river flowing into Minamisoma City in Fukushima Prefecture, Japan). The photography period was in August 2013. We analysed the aerial photographs using ArcGis 9 (Esri Japan Corporation, Tokyo, Japan). The aerial photographs of the main plant communities (Phragmites australis,?Typha domingensis, and?Miscanthus sacchariflorus) taken by the UAV could clearly discriminate each plant community at the 1/50 scale. Moreover, it could clearly discriminate the shape of a plant at the 1/10 scale. We compared the vegetation maps by analysing the aerial photos taken by a UAV (2013 shooting) and an aircraft (2011 shooting). As a result, the vegetation map created by the UAV method could clearly discriminate community distributions. We conclude that vegetation surveys using UAV are possible and are capable of a highly precise community division in places where field reconnaissance is difficult. The UAV method is effective and will contribute to the improvement of research methods in the future;this method may reduce research costs associated with a reduction in field survey days and man-power.

Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

A MODIS Time Series Data Based Algorithm for Mapping Forest Fire Burned Area

Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.

The global rainforest mapping project JERS-1: a paradigm of international collaboration for monitoring land cover change

The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.

OBJECT-BASED SUPER RESOLUTION FOR INTELLIGENT VISUAL SURVEILLANCE VIDEO

Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image reg- istration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method.

Equality Testing for Soil Grid Unit Resolutions to Polygon Unit Scales with DNDC Modeling of Regional SOC Pools

Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.

Smart Photogrammetric and Remote Sensing Image Processing for Very High Resolution Optical Images——Examples from the CRC-AGIP Lab at UNB

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.

High Resolution Crossed Molecular Beams Study of the H+HD→H2+D Reaction

The H+H2 reaction is the simplest chemical reaction system and has long been the prototype model in the study of reaction dynamics. Here we report a high resolution experimental investigation of the state-to-state reaction dynamics in the H+HD→H2+D reaction by using the crossed molecular beams method and velocity map ion imaging technique at the collision energy of 1.17 eV. D atom products in this reaction were probed by the near threshold 1+1'(vacuum ultraviolet+ultraviolet) laser ionization scheme. The ion image with both high angular and energy resolution were acquired. State-to-state differential cross sections was accurately derived. Fast forward scattering oscillations, relating with interference effects in the scattering process, were clearly observed for H2 products at H2(v'=0,j'=1) and H2(v'=0,j'=3) rovibrational levels. This study further demonstrates the importance of measuring high-resolution differential cross sections in the study of state-to-state reaction dynamics in the gas phase.

Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm

A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.

Super resolution reconstruction of moving objects from low resolution surveillance video

Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based super resolution reconstruction scheme was proposed,in which a six-parameter affine model-based object tracking and registration method was first used to segment and match objects among a se-quence of low resolution frames.The motion model was then further extended to the traditional maximuma posterior(MAP)super resolution algorithm.The proposed object tracking and registration method wasevaluated by both simulated and real acquired sequences.The results have demonstrated the high accura-cy of the proposed object based method and the enhanced reconstruction performance of the extended ap-proach.

CRYSTAL STRUCTURE OF THE COMPLEX OF MUNG BEAN TRYPSIN INHIBITOR LYSINE ACTIVE FRAGMENT WITH BOVINE TRYPSIN AT 1.8 A RESOLUTION

The structure of the complex of mung bean trypsin inhibitor lysine active fragment with bovine trypsin has been determined at a resolution of 1.8 A by A-ray crystallographic analysis and the complex model refined by restrained least-squares minimization with the data between 10 and 1.8 resolution.The current conventional R factor is 17.3%,and the model con- tains 1648 protein atoms,219 inhibitor atoms and 126 water molecules.The most prominent feature of the inhibitor fragment is that it does not contain any alpha-helices.Most of the chain fold in an irregular fashion.The seven residues of the binding segment of the inhibitor lysine active frag- ment are in specific contact with bovine trypsin.The binding interaction and geometry around the reactive site are similar to that observed in other studies of trypsin-inhibitor complexes.

Super-resolution image reconstruction based on three-step-training neural networks

A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

Mapping super-resolution image quality

The local quality of super-resolution microscopy images can be assessed and mapped by rolling Fourier ring correlation,even when image quality varies within a single image.

基于MAP的自适应图像配准及超分辨率重建

图像超分辨率重建是一种将多幅低分辨率图像合成为高分辨率图像的技术。当前的超分辨重建技术主要分为图像配准和超分辨率重建2个步骤,提出一种基于最大后验概率的图像超分辨率重建算法,将这2个步骤合二为一;与此同时,为了解决配准参数以及点扩展函数估计值的不精确性问题,在每一幅低分辨率图像代价函数的残差项引入了自适应加权系数并随之给出了迭代算法的总体框架。实验表明,该算法在收敛性和精确性上都达到了较好的效果。