A Structure Similarity Analysis of Secreted Proteins in Magnaporthe oryzae and Its Host Oryza sativa

The structure similarity of secreted proteins in rice blast fungus Magnaporthe oryzae and its host Oryza sativa was analyzed. One thousand two hundred and forty one proteins were predicted as secreted proteins using four algorithms based on 11 074 proteins in genome of M. oryzae. One hundred and forty six secreted proteins( 11. 8% of M. oryzae secretome) were aligned with 116 rice proteins( 0. 21% of 56 278 rice proteins) using BLAST search on rice genome. One hundred sixteen rice similar proteins participated in rice cell wall modification( cell wall associated enzymes) and signal transduction( proteases). These results imply that both cell wall involved proteins and signal transduction are probably hijacks pathway between host pants and pathogenic fungi. Because these proteins are highly conserved among fungi and plants,the express patterns of these protein coding genes during the interaction process are valuable to study in detail.

QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially.However,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems operate.Here,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)numbers.QAUST uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence motifs.These three pieces of information are combined by consensus averaging to make the final prediction.Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark set.The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading.We further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint

Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.

Paired regions for shadow removal approach based on multi-features

The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challenge of shadow removal is how to restore the content of shadow areas correctly while removing the shadow in the image.Paired regions for shadow removal approach based on multi-features is proposed, in which shadow removal is only performed on related sunlit areas.Feature distance between regions is calculated to find the optimal paired regions with considering of multi-features(texture, gradient feature, etc.) comprehensively.Images in different scenes with peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) evaluation indexes are chosen for experiments.The results are shown with six existing comparison methods by visual and quantitative assessments, which verified that the proposed method shows excellent shadow removal effect, the brightness, color of the removed shadow area, and the surrounding non-shadow area can be naturally fused.

Underwater Image Enhancement Using MIRNet

In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are expensive.Hence,the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques.Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation.In the proposed model,the authors used a deep learning model for underwater image enhancement.First,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique.Next,the pre-processed image is given to the MIRNet for enhancement.MIRNet is a deep learning framework that can be used to enhance the low-light level images.The enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.

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.

Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear

In the last years, digital image processing and analysis are used for computer assisted evaluation of semen quality with therapeutic goals or to estimate its fertility by means of spermatozoid motility and morphology. Sperm morphology is assessed routinely as part of standard laboratory analysis in the diagnosis of human male infertility. Nowadays assessments of sperm morphology are mostly done based on subjective criteria. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm’s parts. In this paper, we have proposed a new method for segmentation of sperm’s Acrosome, Nucleus, Mid-piece and identification of sperm’s tail through some points which are placed on the sperm’s tail, accurately. These estimated points could be used to verify the morphological characteristics of sperm’s tail such as length, shape and etc. At first, sperm’s Acrosome, Nucleus and Mid-piece are segmented through a method based on a Bayesian classifier which utilizes the entropy based expectation–maximization (EM) algorithm and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the apriori probability of each class. Then, a pixel at the end of sperm’s Mid-piece, is selected as an initial point. To find other pixels which are placed on the sperm’s tail, structural similarity index (SSIM) is used in an iterative scheme. In order to stop the algorithm automatically at the end of sperm’s tail, local entropy is estimated and used as a feature to determine if a point is located on the sperm’s tail or not. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the Accuracy, Sensitivity and Specificity were calculated.

A Hybrid Security Framework for Medical Image Communication

Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

Advanced technology used for arithmetic computing application,comprises greater number of approximatemultipliers and approximate adders.Truncation and Rounding-based Scalable ApproximateMultiplier(TRSAM)distinguish a variety of modes based on height(h)and truncation(t)as TRSAM(h,t)in the architecture.This TRSAM operation produces higher absolute error in Least Significant Bit(LSB)data shift unit.A new scalable approximate multiplier approach that uses truncation and rounding TRSAM(3,7)is proposed to increase themultiplier accuracy.With the help of foremost one bit architecture,the proposed scalable approximate multiplier approach reduces the partial products.The proposed approximate TRSAM multiplier architecture gives better results in terms of area,delay,and power.The accuracy of 95.2%and the energy utilization of 24.6 nJ is observed in the proposed multiplier design.The proposed approach shows 0.11%,0.23%,and 0.24%less Mean Absolute Relative Error(MARE)when compared with the existing approach for the input of 8-bit,16-bit,and 32-bit respectively.It also shows 0.13%,0.19%,and 0.2%less Variance of Absolute Relative Error(VARE)when compared with the existing approach for the input of 8-bit,16-bit,and 32-bit respectively.The proposed approach is implemented with Field-Programmable Gate Array(FPGA)and shows the delay of 3.640,6.481,12.505,22.572,and 36.893 ns for the input of 8-bit,16-bit,32-bit,64-bit,and 128-bit respectively.The proposed approach is applied in digital filters designwhich shows the Peak-Signal-to-NoiseRatio(PSNR)of 25.05 dB and Structural Similarity Index Measure(SSIM)of 0.98 with 393 pJ energy consumptions when used in image application.The proposed approach is simulated with Xilinx and MATLAB and implemented with FPGA.

USSL Net:Focusing on Structural Similarity with Light U-Structure for Stroke Lesion Segmentation

Automatic segmentation of ischemic stroke lesions from computed tomography(CT)images is of great significance for identifying and curing this life-threatening condition.However,in addition to the problem of low image contrast,it is also challenged by the complex changes in the appearance of the stroke area and the difficulty in obtaining image data.Considering that it is difficult to obtain stroke data and labels,a data enhancement algorithm for one-shot medical image segmentation based on data augmentation using learned transformation was proposed to increase the number of data sets for more accurate segmentation.A deep convolutional neural network based algorithm for stroke lesion segmentation,called structural similarity with light U-structure(USSL)Net,was proposed.We embedded a convolution module that combines switchable normalization,multi-scale convolution and dilated convolution in the network for better segmentation performance.Besides,considering the strong structural similarity between multi-modal stroke CT images,the USSL Net uses the correlation maximized structural similarity loss(SSL)function as the loss function to learn the varying shapes of the lesions.The experimental results show that our framework has achieved results in the following aspects.First,the data obtained by adding our data enhancement algorithm is better than the data directly segmented from the multi-modal image.Second,the performance of our network model is better than that of other models for stroke segmentation tasks.Third,the way SSL functioned as a loss function is more helpful to the improvement of segmentation accuracy than the cross-entropy loss function.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection

We present the analysis of three independent and most widely used image smoothing techniques on a new fractional based convolution edge detector originally constructed by same authors for image edge analysis. The implementation was done using only Gaussian function as its smoothing function based on predefined assumptions and therefore did not scale well for some types of edges and noise. The experiments conducted on this mask using known images with realistic geometry suggested the need for image smoothing adaptation to obtain a more optimal performance. In this paper, we use the structural similarity index measure and show that the adaptation technique for choosing smoothing function has significant advantages over a single function implementation. The new adaptive fractional based convolution mask can smoothly find edges of various types in detail quite significantly. The method can now trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges.

RF MEMS Based Reconfigurable Rectangular Slotted Self Similar Antenna

Wireless communication systems which require flexibility and reconfigurability in antenna systems faces main problems like antenna performance, size, weight and cost. A wide band Frequency Reconfigurable Rectangular Slotted Self Similar Antenna has been proposed in this paper. The rectangular slotted patch is repeated for two iterations at different scales and is separated by means of Radio Frequency Micro Electro Mechanical Systems (RF MEMS) switches in order to provide reconfigurability. The antenna can operate in three frequency bandsi.e. K-band, Ku-band and Ka-band by altering the states of RF MEMS switches. To avoid fringing effects and to improve antenna performance, quarter wavelength (λ/4) spacing is required between the antenna and the ground plane. However, a Reconfigurable Antenna requires different λ/4 spacing which is difficult to achieve using a common ground plane. So the Frequency Reconfigurable antenna is integrated with high impedance surface (HIS) like Electronic Band Gap (EBG) structures to suppress standing waves and surface waves with a unified profile thickness of 1.75 mm. The overall dimension of the proposed antenna along with RF MEMS Switch, feed element and HIS is about 8 mm × 8 mm × 1.75 mm. The simulated results of the proposed antenna reveal enhancement in antennas performance like Voltage Standing Wave Ratio (VSWR), Front to Back Ratio (FBR) and bandwidth when it is placed over HIS EBG. Also the radiation patterns of the proposed antenna when placed over EBG shows the suppression of side lobe and backward radiation.

Ectopic osteogenesis and angiogenesis regulated by porous architecture of hydroxyapatite scaffolds with similar interconnecting structure in vivo

The macro-pore sizes of porous scaffold play a key role for regulating ectopic osteogenesis and angiogenesis but many researches ignored the influence of interconnection between macro-pores with different sizes.In order to accurately reveal the relationship between ectopic osteogenesis and macro-pore sizes in dorsal muscle and abdominal cavities of dogs,hydroxyapatite(HA)scaffolds with three different macro-pore sizes of 500–650,750–900 and 1100–1250 mm were prepared via sugar spheres-leaching process,which also had similar interconnecting structure determined by keeping the d/s ratio of interconnecting window diameter to macro-pore size constant.The permeability test showed that the seepage flow of fluid through the porous scaffolds increased with the increase of macro-pore sizes.The cell growth in three scaffolds was not affected by the macro-pore sizes.The in vivo ectopic implantation results indicated that the macro-pore sizes of HA scaffolds with the similar interconnecting structure have impact not only the speed of osteogenesis and angiogenesis but also the space distribution of newly formed bone.The scaffold with macro-pore sizes of 750–900 mm exhibited much faster angiogenesis and osteogenesis,and much more uniformly distribution of new bone than those with othermacro-pore sizes.This work illustrates the importance of a suitable macro-pore sizes in HA scaffolds with the similar interconnecting structure which provides the environment for ectopic osteogenesis and angiogenesis.

Adversarial network embedding using structural similarity

Network embedding which aims to embed a given network into a low-dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification,link prediction and network visualization.The emerging network embedding methods have shifted of emphasis in utilizing mature deep learning models.The neural-network based network embedding has become a mainstream solution because of its high eficiency and capability of preserv-ing the nonlinear characteristics of the network.In this paper,we propose Adversarial Network Embedding using Structural Similarity(ANESS),a novel,versatile,low-complexity GAN-based network embedding model which utilizes the inherent vertex-to-vertex structural similarity attribute of the network.ANESS learns robustness and ffective vertex embeddings via a adversarial training procedure.Specifically,our method aims to exploit the strengths of generative adversarial networks in generating high-quality samples and utilize the structural similarity identity of vertexes to learn the latent representations of a network.Meanwhile,ANESS can dynamically update the strategy of generating samples during each training iteration.The extensive experiments have been conducted on the several benchmark network datasets,and empirical results demon-strate that ANESS significantly outperforms other state-of-theart network embedding methods.

Classification of protein domains based on their three-dimensional shapes (CPD3DS)

Protein design has become a powerful method to expand the number of natural proteins and design customized proteins according to demands.Domain-based protein design spares the need to create novel elements from scratch,which makes it a more efficient strategy than scratch-based protein design in designing multi-domain proteins,protein complexes and biomaterials.As the surface shape plays a central role in domain-domain and protein-protein interactions,a global map of the surface shapes of all domains should be very beneficial for domain-based protein design.Therefore,in this study,we characterized the surface shapes of protein domains,collected from CATH and SCOP databases,with their 3D-Zernike descriptors(3DZDs).Then similarities of domain shape features were identified,and all domains were classified accordingly.The preferences of the combinations of domains between different clusters were analyzed in natural proteins from the Protein Data Bank.A user-friendly website,termed CPD3DS,was also developed for storage,retrieval,analyses and visualization of our results.This work not only provides an overall view of protein domain shapes by showing their variety and similarities,but also opens up a new avenue to understand the properties of protein structural domains,and design principles of protein architectures.

A combination of learning and non-learning based method for enhancement, compression and reconstruction of underwater images

Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.

Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

Automatic detection of a designated building area(DBA)is a research hotspot in the field of target detection using remote sensing images.Target detection is urgently needed for tasks such as illegal building monitoring,dynamic land use monitoring,antiterrorism efforts,and military reconnaissance.The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes.To address the problems of the current detection methods,this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images.Our method was conducted in two main stages.(1)During keypoint generation,we proposed a screening method based on structural pattern descriptors.The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram(MLPH)feature;then,we used one-class support vector machine(OC-SVM)merely to screen those building attribute keypoints.(2)To match the screened keypoints,we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints.We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection.We tested the proposed method on building area sets of different types and at different time phases.The experimental results show that the proposed method is both highly accurate and computationally efficient.