Image Registration Algorithm Using Mexican Hat Function-Based Operator and Grouped Feature Matching Strategy

Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS). The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume.     

Multisite Image Data Collection and Management Using the RSNA Image Sharing Network

The execution of a multisite trial frequently includes image collection. The Clinical Trials Processor (CTP) makes removal of protected health information highly reliable. It also provides reliable transfer of images to a central review site. Trials using central review of imaging should consider using CTP for handling image data when a multisite trial is being designed.     

Stabilization of DNA Triple Helices Using Conjugates of Oligonucleotides and Synthetic Ligands

The possibility is discussed of stabilizing a DNA triple helix by covalent conjugation to the third strand (through its terminal phosphate) of ligands that have affinity to double and triple helices. Two types of stabilizers are considered: minor groove binders based on oligopyrroles, and triplex-specific intercalators. As a target, a synthetic 29-mer duplex containing a natural polypurine sequence of the human immunodeficiency provirus was employed. The stabilization with minor groove binders requires several conditions to be respected: a sufficiently long linker capable of reaching the minor groove from the major groove, a specific double-stranded structure of the oligopyrrole fragment, and its in-phase fitness to the target sequence. The best stabilizers of a triplex were novel conjugates in which two parallel molecules containing six pyrrole units each are linked to the same 5"-phosphate of a 16-mer triplex-forming oligonucleotide. The stabilizing properties of these derivatives were comparable to those of benzoindoloquinoline (BIQ) intercalators attached to the terminal phosphate of triple-helix forming oligonucleotides.     

Stabilization of Cr,Pb, and Zn in Soil Using Lignite

A uniform multivariable model system was used to investigate whether lignite from Visonta (Hungary) has a stabilizing effect on the heavy metals Cr, Pb, and Zn in the case of complex soil contamination with these metals. The investigated soil was acidic sand. Soluble element concentrations extracted by distilled water, ammonium acetate buffer, and ammonium-acetate + EDTA extractant were determined. The effect of lignite on the humus quality of the soil and its influence on the cation exchange capacity was also measured. The results were evaluated using analysis of variance, regression analysis, and principal component analysis. The results indicated that lignite was capable of stabilizing Pb, Zn, and particularly Cr. According to the optical method, lignite did not increase the amount of higher condensation and polymerization humic fractions, but it did increase the cation exchange capacity of the soil. If the lignite-based remediation technology is to become applicable in practice, further tests will be required on the efficiency of lignite, accompanied by a change in the scale of the experiments.     

使用图像特征构建快速有效的蛋白质折叠识别方法

蛋白质结构自动分类是探索蛋白质结构- 功能关系的一种重要研究手段。首先将蛋白质折叠子三维空间结构映射成为二维距离矩阵,并将距离矩阵视作灰度图像。然后基于灰度直方图和灰度共生矩阵提出了一种计算简单的折叠子结构特征提取方法,得到了低维且能够反映折叠结构特点的特征,并进一步阐明了直方图中零灰度孤峰形成原因,深入分析了共生矩阵特征中灰度分布、不同角度和像素距离对应的结构意义。最后应用于27类折叠子分类,对独立集测试的精度达到了71.95 %,对所有数据进行10 交叉验证的精度为78.94 %。与多个基于序列和结构的折叠识别方法的对比结果表明,此方法不仅具有低维和简洁的特征,而且无需复杂的分类系统,能够有效和高效地实现多类折叠子识别。     

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.     

Head Movements Evoked in Alert Rhesus Monkey by Vestibular Prosthesis Stimulation: Implications for Postural and Gaze Stabilization

The vestibular system detects motion of the head in space and in turn generates reflexes that are vital for our daily activities. The eye movements produced by the vestibulo-ocular reflex (VOR) play an essential role in stabilizing the visual axis (gaze), while vestibulo-spinal reflexes ensure the maintenance of head and body posture. The neuronal pathways from the vestibular periphery to the cervical spinal cord potentially serve a dual role, since they function to stabilize the head relative to inertial space and could thus contribute to gaze (eye-in-head + head-in-space) and posture stabilization. To date, however, the functional significance of vestibular-neck pathways in alert primates remains a matter of debate. Here we used a vestibular prosthesis to 1) quantify vestibularly-driven head movements in primates, and 2) assess whether these evoked head movements make a significant contribution to gaze as well as postural stabilization. We stimulated electrodes implanted in the horizontal semicircular canal of alert rhesus monkeys, and measured the head and eye movements evoked during a 100ms time period for which the contribution of longer latency voluntary inputs to the neck would be minimal. Our results show that prosthetic stimulation evoked significant head movements with latencies consistent with known vestibulo-spinal pathways. Furthermore, while the evoked head movements were substantially smaller than the coincidently evoked eye movements, they made a significant contribution to gaze stabilization, complementing the VOR to ensure that the appropriate gaze response is achieved. We speculate that analogous compensatory head movements will be evoked when implanted prosthetic devices are transitioned to human patients.     

How Visuo-Spatial Mental Imagery Develops: Image Generation and Maintenance

Two experiments examined the nature of visuo-spatial mental imagery generation and maintenance in 4-, 6-, 8-, 10-year old children and adults (N = 211). The key questions were how image generation and maintenance develop (Experiment 1) and how accurately children and adults coordinate mental and visually perceived images (Experiment 2). Experiment 1 indicated that basic image generation and maintenance abilities are present at 4 years of age but the precision with which images are generated and maintained improves particularly between 4 and 8 years. In addition to increased precision, Experiment 2 demonstrated that generated and maintained mental images become increasingly similar to visually perceived objects. Altogether, findings suggest that for simple tasks demanding image generation and maintenance, children attain adult-like precision younger than previously reported. This research also sheds new light on the ability to coordinate mental images with visual images in children and adults.     

Prediction of Protein Secondary Structure Using Feature Selection and Analysis Approach

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.     

Controlling the Metabolic Flux through the Carotenoid Pathway Using Directed mRNA Processing and Stabilization

A synthetic operon containing the crtI and crtY genes, encoding the phytoene desaturase and the lycopene cyclase, respectively, was placed under the control of the araBAD promoter. DNA cassettes encoding mRNA secondary structures were placed at the 5' and 3' ends of the genes and a putative RNase E site was placed between the genes. This construct was transformed into Escherichia coli cells harboring the genes for phytoene production. By varying the mRNA secondary structures, we were able to modulate the flux through the carotenoid pathway, resulting in a 300-fold variation in the production of beta-carotene relative to lycopene. In addition, intermediates in the pathway from phytoene to beta-carotene production that are not observed in cells expressing the recombinant operon were observed when the engineered operons were used, indicating that changes in levels of the enzymes affected the formation of intermediates. These results indicate that it is possible to coordinately regulate the genes encoding the enzymes of a metabolic pathway and balance the production of the intermediates.     

Thermal Stabilization of Dihydrofolate Reductase Using Monte Carlo Unfolding Simulations and Its Functional Consequences

Design of proteins with desired thermal properties is important for scientific and biotechnological applications. Here we developed a theoretical approach to predict the effect of mutations on protein stability from non-equilibrium unfolding simulations. We establish a relative measure based on apparent simulated melting temperatures that is independent of simulation length and, under certain assumptions, proportional to equilibrium stability, and we justify this theoretical development with extensive simulations and experimental data. Using our new method based on all-atom Monte-Carlo unfolding simulations, we carried out a saturating mutagenesis of Dihydrofolate Reductase (DHFR), a key target of antibiotics and chemotherapeutic drugs. The method predicted more than 500 stabilizing mutations, several of which were selected for detailed computational and experimental analysis. We find a highly significant correlation of r = 0.65–0.68 between predicted and experimentally determined melting temperatures and unfolding denaturant concentrations for WT DHFR and 42 mutants. The correlation between energy of the native state and experimental denaturation temperature was much weaker, indicating the important role of entropy in protein stability. The most stabilizing point mutation was D27F, which is located in the active site of the protein, rendering it inactive. However for the rest of mutations outside of the active site we observed a weak yet statistically significant positive correlation between thermal stability and catalytic activity indicating the lack of a stability-activity tradeoff for DHFR. By combining stabilizing mutations predicted by our method, we created a highly stable catalytically active E. coli DHFR mutant with measured denaturation temperature 7.2°C higher than WT. Prediction results for DHFR and several other proteins indicate that computational approaches based on unfolding simulations are useful as a general technique to discover stabilizing mutations.     

基于间隔二肽组分和递归特征消除法的DNA结合蛋白的鉴定

DNA结合蛋白(DNA-binding proteins,DBPs)的鉴定在原核和真核生物的基因和蛋白质功能注释研究中具有十分重要的意义.本研究首次运用间隔二肽组分(gapped-dipeptide composition,Gap DPC)结合递归特征消除法(recursive feature elimination,RFE)鉴定DBPs.首先获得待测蛋白质氨基酸序列的位置特异性得分矩阵(position specific scoring matrix,PSSM),在此基础上提取蛋白质的Gap DPC特征,通过RFE法选择最优特征,然后利用支持向量机(support vector machine,SVM)作为分类器,在蛋白质序列数据集PDB396和LB1068中进行夹克刀交叉验证(jackknife cross validation test).研究结果显示,基于PDB396和LB1068数据集,DBPs预测的准确率、Matthews相关系数、敏感性和特异性分别达到93.43%、0.86、89.04%和96.00%,以及86.33%、0.73、86.49%和86.18%,明显优于文献报道中的相关方法,为DBPs的鉴定提供了新的模型.     

巴戟天花药发育过程中多糖和脂滴分布特征

巴戟天花药发育中多糖和脂滴类物质的分布呈现一定的规律：减数分裂之前,花药壁的绒毡层细胞中有少量脂滴,其他细胞中脂滴和淀粉粒都很少。四分体时期,四分体小孢子中开始出现脂滴,绒毡层细胞中的脂滴较以前增加,其他细胞中的脂滴和淀粉粒仍然很少。小孢子早期,游离小孢子在其表面形成了花粉外壁,靠外壁下方有一层周缘分布的多糖物质。绒毡层细胞中的脂滴明显减少。发育晚期的小孢子中形成一个大液泡,细胞质中出现淀粉粒;同时在药壁和药隔组织中也出现了淀粉粒。此时绒毡层退化。在二胞花粉早期,花粉中积累了大量淀粉粒和一些脂滴。但在成熟的花粉中（二胞花粉晚期）,淀粉粒消失,只有一定数量的脂滴保留。巴戟天成熟花粉中积累的营养物质主要为脂滴。     

National Survey of Radiation Dose and Image Quality in Adult CT Head Scans in Taiwan

Introduction

The purpose of the present study was to evaluate the influence of different variables on radiation dose and image quality based on a national database.

Materials and Methods

Taiwan’s Ministry of Health and Welfare requested all radiology departments to complete a questionnaire for each of their CT scanners. Information gathered included all scanning parameters for CT head scans. For the present analysis, CT machines were divided into three subgroups: single slice CT (Group A); multi-detector CT (MDCT) with 2-64 slices (Group B); and MDCT with more than 64 slices (Group C). Correlations between computed tomography dose index (CTDI) and signal-to-noise ratio (SNR) with cumulated tube rotation number (CTW(n)) and cumulated tube rotation time (CTW(s)), and sub group analyses of CTDI and SNR across the three groups were performed.

Results

CTDI values demonstrated a weak correlation (r = 0.33) with CTW(n) in Group A. SNR values demonstrated a weak negative correlation (r = -0.46) with CTW(n) in Group C. MDCT with higher slice numbers used more tube potential resulting in higher effective doses. There were both significantly lower CTDI and SNR values in helical mode than in axial mode in Group B, but not Group C.

Conclusion

CTW(n) and CTW(s) did not influence radiation output. Helical mode is more often used in MDCT and results in both lower CTDI and SNR compared to axial mode in MDCT with less than 64 slices.     

Discriminating between Lysine Sumoylation and Lysine Acetylation Using mRMR Feature Selection and Analysis

Post-translational modifications (PTMs) are crucial steps in protein synthesis and are important factors contributing to protein diversity. PTMs play important roles in the regulation of gene expression, protein stability and metabolism. Lysine residues in protein sequences have been found to be targeted for both types of PTMs: sumoylations and acetylations; however, each PTM has a different cellular role. As experimental approaches are often laborious and time consuming, it is challenging to distinguish the two types of PTMs on lysine residues using computational methods. In this study, we developed a method to discriminate between sumoylated lysine residues and acetylated residues. The method incorporated several features: PSSM conservation scores, amino acid factors, secondary structures, solvent accessibilities and disorder scores. By using the mRMR (Maximum Relevance Minimum Redundancy) method and the IFS (Incremental Feature Selection) method, an optimal feature set was selected from all of the incorporated features, with which the classifier achieved 92.14% accuracy with an MCC value of 0.7322. Analysis of the optimal feature set revealed some differences between acetylation and sumoylation. The results from our study also supported the previous finding that there exist different consensus motifs for the two types of PTMs. The results could suggest possible dominant factors governing the acetylation and sumoylation of lysine residues, shedding some light on the modification dynamics and molecular mechanisms of the two types of PTMs, and provide guidelines for experimental validations.     

Giga-Pixel Lensfree Holographic Microscopy and Tomography Using Color Image Sensors

We report Giga-pixel lensfree holographic microscopy and tomography using color sensor-arrays such as CMOS imagers that exhibit Bayer color filter patterns. Without physically removing these color filters coated on the sensor chip, we synthesize pixel super-resolved lensfree holograms, which are then reconstructed to achieve ∼350 nm lateral resolution, corresponding to a numerical aperture of ∼0.8, across a field-of-view of ∼20.5 mm². This constitutes a digital image with ∼0.7 Billion effective pixels in both amplitude and phase channels (i.e., ∼1.4 Giga-pixels total). Furthermore, by changing the illumination angle (e.g., ±50°) and scanning a partially-coherent light source across two orthogonal axes, super-resolved images of the same specimen from different viewing angles are created, which are then digitally combined to synthesize tomographic images of the object. Using this dual-axis lensfree tomographic imager running on a color sensor-chip, we achieve a 3D spatial resolution of ∼0.35 µm×0.35 µm×∼2 µm, in x, y and z, respectively, creating an effective voxel size of ∼0.03 µm³ across a sample volume of ∼5 mm³, which is equivalent to >150 Billion voxels. We demonstrate the proof-of-concept of this lensfree optical tomographic microscopy platform on a color CMOS image sensor by creating tomograms of micro-particles as well as a wild-type C. elegans nematode.     

MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods

An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ₁-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ₁-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ₁-regularized parallel imaging methods.     

A Novel Image Recuperation Approach for Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image

Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state. Also the diagnosis of diseases did not employ machine learning methods to categorize candidate fundus images into true positive and true negative ratio. Several candidate fundus images did not include more detailed feature selection technique for diabetic retinopathy. To apply machine learning methods and classify the candidate fundus images on the basis of sliding window a method called, Diabetic Fundus Image Recuperation (DFIR) is designed in this paper. The initial phase of DFIR method select the feature of optic cup in digital retinal fundus images based on Sliding Window Approach. With this, the disease state for diabetic retinopathy is assessed. The feature selection in DFIR method uses collection of sliding windows to obtain the features based on the histogram value. The histogram based feature selection with the aid of Group Sparsity Non-overlapping function provides more detailed information of features. Using Support Vector Model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy diseases. The ranking of disease level for each candidate set provides a much promising result for developing practically automated diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, specificity rate, ranking efficiency and feature selection time.