Matching Regions Research Articles

The Development of a Modified Ear Recognition System for Personnel Identification

Background: This study proposed a Match Region Localization (MRL) Ear Recognition System (ERS). Captured ear images were pre-processed through cropping and enhancement. The preprocessed ear images were segmented using the proposed MRL segmentation algorithm and divided into 160 sub-images. The principal features of the segmented ear images were extracted and used in template generation. k-nearest neighbor classifiers with Euclidean distance metrics were applied in the classification. Objective: The proposed ERS exhibited a recognition accuracy of 97.7%. Other publicly available ear datasets can be tested using the proposed system for cross-database comparison and can be improved by reducing their errors. Methods: This research follows four major stages, namely, the development of a PCA-based ear recognition algorithm, implementation of the developed algorithm, determination of the optimum ear segmentation method, and evaluation of the performance of the technique. Results: The False Acceptance Rate (FAR) of the developed Ear Recognition System (ERS) is 0.06. This result implies that six out of every 100 intruders will be falsely accepted. Conclusion: The developed ERS outperforms the existing ERS by approximately 24.61% in terms of system recognition accuracy; the developed ERS can be tested on other publicly available ear databases to check its performance on larger platforms.

A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

S100B gene polymorphisms are associated with the S100B level and Alzheimer's disease risk by altering the miRNA binding capacity.

To examine the role of S100B in genetic susceptibility to Alzheimer’s disease (AD), we conducted a case-control study to analyze four polymorphism loci (rs2839364, rs1051169, rs2300403, and rs9722) of the S100B gene and AD risk. We found an independent increased risk of AD in ApoE ε4(-) subjects carrying the rs9722 AA-genotype (OR = 2.622, 95% CI = 1.399–4.915, P = 0.003). Further investigation revealed the serum S100B levels to be lower in rs9722 GG carriers than in rs9722 AA carriers (P = 0.003). We identified three miRNAs (miR-340-3p, miR-593-3p, miR-6827-3p) in which the seed match region covered locus rs9722. Luciferase assays indicated that the rs9722 G allele has a higher binding affinity to miR-6827-3p than the rs9722 A allele, leading to a significantly decreased fluorescence intensity. Subsequent western blot analysis showed that the S100B protein level of SH-SY5Y cells, which carry the rs9722 G allele, decreased significantly following miR-6827-3p stimulation (P = 0.009). The present study suggests that the rs9722 polymorphism may upregulate the expression of S100B by altering the miRNA binding capacity and may thus increase the AD risk. This finding would be of great help for the early diagnosis of AD.

Verification of field match lines in whole breast radiation therapy using Cherenkov imaging

PurposeIn mono-isocentric radiation therapy treatment plans designed to treat the whole breast and supraclavicular lymph nodes, the fields meet at isocenter, forming the match line. Insufficient coverage at the match line can lead to recurrence, and overlap over weeks of treatment can lead to increased risk of healthy tissue toxicity. Cherenkov imaging was used to assess the accuracy of delivery at the match line and identify potential incidents during patient treatments. Methods and materialsA controlled calibration was constructed from the deconvolved Cherenkov images from the delivery of a modified patient treatment plan to an anthropomorphic phantom with introduced separation and overlap. The trend from this calibration was then used to evaluate the field match line for accuracy and inter-fraction consistency for two patients. ResultsThe intersection point between matching field profiles was directly correlated to the distance (gap/overlap) between the fields (anthropomorphic phantom R2 = 0.994 “breath hold” and R2 = 0.990 “free breathing”). The profile intersection points from two patients’ imaging sessions yielded an average of +1.40 mm offset (overlap) and −1.32 mm offset (gap), thereby introducing roughly a 25.0% over-dose and a −23.6% under-dose (R2 = 0.994). ConclusionsThis study shows that field match regions can be detected and quantified by taking deconvolved Cherenkov images and using their product image to create steep intensity gradients, causing match lines to stand out. These regions can then be quantitatively translated into a dose consequence. This approach offers a high sensitivity detection method which can quantify match line variability and errors in vivo.

Does diabetes mellitus predispose to both rotator cuff surgery and subsequent failure?

BackgroundDiabetes mellitus (DM) may be associated with the etiology of rotator cuff disease; however, its effect on healing after surgical rotator cuff repair (RCR) is not well characterized. The purposes of this study are to analyze the association between DM and surgical RCR, the association between DM and revision RCR after RCR, and the association between DM and the cost of RCR.MethodsA retrospective analysis of claims data of privately and publicly insured subjects from the Truven Health MarketScan database from 2008 to 2017 was conducted, collecting RCR cases and controls matched for age, sex, year of RCR, and first and last year in the database. Multivariable logistic regression models were used to compare DM incidence within the RCR and control groups after adjusting for all matching variables plus region, insurance plan type, tobacco use, and Charlson comorbidity index (CCI). Cox proportional hazard models were used to compare rates of revision RCR between DM and non-DM groups after adjusting for patient age, sex, year of RCR, plan type, and CCI. Generalized estimating equations were used to analyze RCR cost, and exponentiated regression coefficients were reported to represent cost ratios.ResultsThe full analysis cohort consisted of 292,666 RCR cases and matched controls. The adjusted odds of having RCR surgery in diabetic patients was 48% higher (odds ratio = 1.48 [95% confidence interval {CI} 1.46 to 1.51], P < .001) than nondiabetics. DM was not significantly associated with revision RCR after RCR when adjusting for age, sex, region, plan type, tobacco use, year of RCR, and CCI (hazard ratio = 1.03, 95% CI 0.99 to 1.07, P = .17). Diabetes was associated with a higher cost of RCR by 3% (ratio = 1.03, 95% CI 1.02 to 1.03, P < .001).ConclusionsDiabetic patients are at a higher risk of undergoing RCR surgery; however, there is no association between DM and subsequent rotator cuff revision surgery.

Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as the block matching and CNN (BM-CNN) method. Training the CNN is accomplished using simulated data from a fast turbulence simulation tool capable of producing a large amount of degraded imagery from declared truth images rapidly. Testing is done using independent data simulated with a different well-validated numerical wave-propagation simulator. Our proposed BM-CNN TM method is evaluated in a number of experiments using quantitative metrics. The quantitative analysis is made possible by virtue of having truth imagery from the simulations. A number of restored images are provided for subjective evaluation. We demonstrate that the BM-CNN TM method outperforms the benchmark methods in the scenarios tested.

Analysis of amyloid and tau deposition in Alzheimer's disease using 11C-Pittsburgh compound B and 18F-THK 5351 positron emission tomography imaging.

This study aims to determine the deposition of 11C-Pittsburgh compound B (11C-PiB) and 18F-THK 5351 using a normal database of the optimal cut-off-points for standardized uptake value ratios (SUVRs) in Alzheimer's disease (AD) patients. Sixteen AD patients and 24 cognitively normal individuals were enrolled in this study. The optimal cutoff points for the SUVR from the normal database were used for quantitative analysis. P-mod software with the Automated Anatomical Labeling merged atlas was employed to generate automatic volumes of interest to identify different brain regions, and the SUVRs of AD patients were compared with those of the age-matched normal controls. The correlation between PiB and THK5351 deposition at matching brain regions was identified. The mean regional 11C-PiB SUVRs of the AD patients were significantly higher than the healthy controls (P < 0.05). The 11C-PiB SUVR cut-offs were 1.46–1.81, with sensitivity ranging from 81.25% to 93.75% and specificity of 100%. The mean SUVRs of 18F-THK 5351 in various regions were also significantly higher in the AD patients than in the healthy controls (P < 0.05). The inferior temporal gyrus yielded an optimum SUVR cut-off-points of 1.5 with 80% sensitivity and 83.33% specificity. The correlation of PiB and THK5351 SUVR was reported at precuneus, parietal, and occipital brain areas, with spearman's rho of 0.67, 0.66, and 0.72, respectively. Our findings allow determination of the SUVRs of 11C-PiB and 18F-THK-5351 amyloid and tau positron emission tomography tracers for clinical use, according to the normal database of the optimal cut-off-points for SUVRs in AD patients.

Copy Move Forgery Detection based on double matching

Copy Move is a technique widespreadly used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD method is proposed, including double matching process and region localizing process. In double matching process, the first matching is conducted on Delaunay triangles consisting of Local Intensity Order Pattern (LIOP) keypoints, to find the approximate location of suspicious regions. In order to find sufficient keypoint pairs, the existing set of matching triangles is expanded by adding their neighbors iteratively, covering the whole tampered regions, and the second matching with a looser threshold is conducted on the vertices. In the region localizing process, considering the case of multiple copies, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to classify the keypoint pairs described in a new model. Experimental results indicate that the proposed method, with good robustness, outperforms some state-of-the-art methods.

Prevalence, antimicrobial resistance, and genotyping of Shiga toxin-producing Escherichia coli in foods of cattle origin, diarrheic cattle, and diarrheic humans in Egypt

Shiga toxin-producing Escherichia coli (STEC) is a pathotype of E. coli that causes enteric and systemic diseases ranging from diarrhoea to severe hemorrhagic colitis (HC) and hemolytic uremic syndrome (HUS). The emergence of multidrug-resistant (MDR) STEC from cattle sources has increased public health risk and limited treatment options. The prevalence of STEC was investigated in 200 raw food samples (milk and beef samples) and 200 diarrheic samples (cattle and human samples) in a matched region. The presence of stx genes (stx1 and stx2), carbapenemase-encoding genes (blaVIM, blaNDM-1, and blaIMP), and extended-spectrum β-lactamase (ESBL)-encoding genes (blaTEM group, blaCTX-M1 group, and blaOXA-1 group) was screened by polymerase chain reaction (PCR). Antibiogram and Enterobacterial repetitive intergenic consensus (ERIC)-PCR were also conducted. STEC isolates were identified in 6.5% (13/200) of food samples [6% (6/100) of milk and 7% (7/100) of beef samples] and in 11% (22/200) of diarrheic cases [12% (12/100) of cattle and 10% (10/100) of human samples]. We found that O26 (4.5%, 18/400) and O111 (1.5%, 6/400) were the most prevalent STEC serovars and were found more commonly in diarrheic samples. STEC strains with both stx genes, stx2 only, and stx1 only genotypes were present in 62.9% (22/35), 20% (7/35), and 17.1% (6/35) of isolates, respectively. Carbapenemase-producing STEC (CP STEC) isolates were found in 1.8% (7/400) of samples [0.5% (1/200) of foods and 3% (6/200) of diarrheic cases]. The blaVIM gene was detected in all CP STEC isolates, and one human isolate carried the blaNDM-1 gene. ESBL-producing STEC strains were detected in 4.3% (17/400) of samples [1.5% (3/200) of food samples and 7% (14/200) of diarrheic cases]. The blaTEM, blaCTX-M1, and blaOXA-1 genes were detected in 42.9% (15/35), 28.6% (10/35), and 2.9% (1/35) of STEC isolates, respectively. Approximately half (51.4%, 18/35) of STEC isolates were MDR STEC; all CP STEC and ESBL-producing STEC were also MDR STEC. The highest antimicrobial resistance rates were found against nalidixic acid (51.4%) and ampicillin (48.6%), whereas the lowest rates were reported against gentamicin (5.7%) and ciprofloxacin (11.4%). MDR STEC strains were 5.3 times more likely to be found in diarrheic cases than in foods (P = 0.009, 95% CI 1.5–18.7). ERIC-PCR was used for genotyping STEC isolates into 27 different ERIC-types (ETs) with a discrimination index of 0.979. Five ETs showed clusters of 2–4 identical isolates that shared the same virulence and antibiotic resistance genetic profile. Human isolates matched food isolates in two of these ET clusters (the O26 CP STEC cluster and the O111 STEC cluster), highlighting the potential cross-species zoonotic transmission of these pathogens and/or their genes in the study region. This is the first detection of CP STEC in milk and diarrheic cattle in Egypt.

Planar Primitive Group-Based Point Cloud Registration for Autonomous Vehicle Localization in Underground Parking Lots

We present a registration strategy based on a planar primitive group for indoor environments between a large-scale point cloud and a small-scale point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm first divides the point cloud into planes by region growing and then refines the plane boundaries by local <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering. The planes are grouped to obtain planar primitive groups that are used to infer potential matching regions for an effective coarse registration and then fine-tuning. The algorithm is applied for autonomous vehicle localization in underground garages. Evaluation on four datasets shows that the algorithm can provide decimeter-level localization accuracy in seconds.

Experimental and theoretical studies on the surface morphology variation of a Ni substrate by graphene growth

As graphene grows on a metal substrate, step bunching and terrace formation occur due to thermodynamic instability. Variation in the terrace–step bunching morphology is caused by graphene–metal interaction. The mechanism for this morphological variation was elucidated in terms of the crystal lattice matching by the observation of step bunching formation using in situ scanning electron microscopy and analysis of the crystal orientation of the same microscopic field using electron backscatter diffraction. The cross-sectional structure was also observed using transmission electron microscopy (TEM). These combined methods enable a reliable analysis of the step bunching region. The lattice matching was also evaluated according to the 0-lattice theory. From the structural analysis using TEM and the application of the 0-lattice theory, the graphene-covered surface was confirmed to consist of good and poor lattice-matching regions. The steps ejected from the terrace region piled up in the poor lattice-matching region to form a low Miller index plane. It was concluded that step flow occurs on the good lattice-matching region and steps are piled up and arranged very closely on the poor matching region based on the lattice-matching evaluation by the 0-lattice theory.

YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images

Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST Rotated BRIEF (ORB)—require heavy computation to extract and describe features in high-resolution UAV images. To overcome this issue, You Only Look Once version 3 (YOLOv3) combined with the traditional feature point matching algorithms is utilized to extract descriptive features from the drone dataset of residential areas for roof detection. Unlike the traditional feature extraction algorithms, YOLOv3 performs the feature extraction solely on the proposed candidate regions instead of the entire image, thus the complexity of the image matching is reduced significantly. Then, all the extracted features are fed into Structural Similarity Index Measure (SSIM) to identify the corresponding roof region pair between consecutive image sequences. In addition, the candidate corresponding roof pair by our architecture serves as the coarse matching region pair and limits the search range of features matching to only the detected roof region. This further improves the feature matching consistency and reduces the chances of wrong feature matching. Analytical results show that the proposed method is 13× faster than the traditional image matching methods with comparable performance.

A New SLAM Algorithm with Key-Frame Estimation and Local Map Upgrade Scheme

Simultaneous Localization and Mapping(SLAM) is the most important tool in creating map and auto-navigation, which is an indispensable link for pilotless automobile. The current SLAM algorithms suffer from unreliable feature matching and large registration error. To reduce those deficiencies, we proposed a key-frame estimation and local map upgrade scheme, which include the following 3 parts:1) Local map matching strategy; 2) Local map updating scheme; 3) Key-frame selecting scheme. Experimental results proved that our scheme improved the performance of current localization methods.

Omni-Zernike Algorithm for Template Matching in Catadioptric System

Image descriptor have been widely applied in many computer visions and image understanding applications including pattern recognition, robotic, video surveillance, camera calibration and image retrieval, etc. Invariants features are robust when apply several transformations of photometry (illumination, blur, noise, JPEG compression) and transformations of geometry (scaling, rotation, translation and viewpoint change). In this study, we present representation and matching region descriptors. Consequently, a set of region used provided by catadioptric system for evaluation of the performance. These regions are normalized by unit circle form with form and size change. In this contribution, the image descriptors of regions used is Moment's Zernike. They are most suitable invariants in omnidirectional context thanks to the polar coordinates used both omnidirectional geometry and Zernike Moments formulation. The aim is realize a robust matching between object's block by using a measure of distance between Zernike's moment descriptors for optimal similarity. Results shown clearly demonstrate the performance of our method and powerful than most important region descriptors (GLOH, SIFT, PCA-SIFT, complex moments and steerable filters) in term of the ROC curve or precision-recall criterion.

Robust Localization in Map Changing Environments Based on Hierarchical Approach of Sliding Window Optimization and Filtering

Precise localization in map-changing environments is a major challenge faced in the case of autonomous vehicles. In such environments, map-matching-based localization can result in an incorrect vehicle pose because the High-Definition map (HD map) and the sensor measurements from the real environments are different. In order to solve this problem, this paper proposes robust localization in map-changing environments based on a hierarchical approach of sliding window optimization and filtering. The changing environments are explicitly modeled by a sub-map from the sliding-window-based graph optimization, and the generated sub-map is used for the map matching of the real-time filter. Since the optimized sub-map includes sensor measurement from both the changed and unchanged regions, it reduces the proportion of the changed region in the total matching region, thereby increasing the robustness of the map matching. The proposed hierarchical localization algorithm is verified and evaluated via simulation and experiments. The results show that the proposed algorithm provides a robust vehicle pose in map-changing environments.

Identification of a Type IV-A CRISPR-Cas System Located Exclusively on IncHI1B/IncFIB Plasmids in Enterobacteriaceae.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) are diverse immune systems found in many prokaryotic genomes that target invading foreign DNA such as bacteriophages and plasmids. There are multiple types of CRISPR with arguably the most enigmatic being Type IV. During an investigation of CRISPR carriage in clinical, multi-drug resistant, Klebsiella pneumoniae, a Type IV-A3 CRISPR-Cas system was detected on plasmids from two K. pneumoniae isolates from Egypt (isolated in 2002–2003) and a single K. pneumoniae isolate from the United Kingdom (isolated in 2017). Sequence analysis of all other genomes available in GenBank revealed that this CRISPR-Cas system was present on 28 other plasmids from various Enterobacteriaceae hosts and was never found on a bacterial chromosome. This system is exclusively located on IncHI1B/IncFIB plasmids and is associated with multiple putative transposable elements. Expression of the cas loci was confirmed in the available clinical isolates by RT-PCR. In all cases, the CRISPR-Cas system has a single CRISPR array (CRISPR1) upstream of the cas loci which has several, conserved, spacers which, amongst things, match regions within conjugal transfer genes of IncFIIK/IncFIB(K) plasmids. Our results reveal a Type IV-A3 CRISPR-Cas system exclusively located on IncHI1B/IncFIB plasmids in Enterobacteriaceae that is likely to be able to target IncFIIK/IncFIB(K) plasmids presumably facilitating intracellular, inter-plasmid competition.

TEMPORAL REPETITION DETECTION FOR GROUND VISIBILITY ASSESSMENT

Abstract. Assessing ground visibility is a crucial step in automatic satellite image analysis. Nevertheless, several recent Earth observation satellite constellations lack specially designed spectral bands and use a frame camera, precluding spectrum-based and parallax-based cloud detection methods. An alternative approach is to detect the parts of each image where the ground is visible. This can be done by comparing locally pairs of registered images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series while the appearance of clouds changes at each date. To detect reliably the “visible” ground, we propose here an a contrario local image matching method coupled with an efficient greedy algorithm.

IC Microscopic Image Stitching Based on Improved Line Matching

Integrated Circuit(IC) microscopic image stitching is an important process in IC reverse engineering. However, IC is highly integrated and miniaturized, with many similar structures and dense distribution, and the overlapping region of some IC microimages may be the low-texture region. As a result, the traditional image stitching method is prone to mismatch, and the stitching effect is poor. This paper proposes an IC microscopic image stitching method based on improved line matching. The method first extract line segments from two IC microscopic images to be stitched by using Edline algorithm. Secondly, the intersection of line segments is generated by using the position relation of line segments in a certain strategy. Then, combined with the 2D capture method of the IC microscopic image, we limit the matching region. Finally, image registration is completed by combining points and lines, and a transformation matrix is obtained to stitch IC microimages. Experimental results demonstrate that the proposed method reduces the possibility of mismatching in the stitching process while using the stable line features and the position constraints between line segments in IC microscopic images. Moreover, the image stitching guided by line features can also achieve good stitching results in low-texture IC micrograph.

Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph

Accurate digital orthophoto map generation from high-resolution aerial images is important in various applications. Compared with the existing commercial software and the current state-of-the-art mosaicing systems, a novel fast georeferenced orthophoto mosaicing framework is proposed in this study. The framework can adapt to the challenging requirements of high-accuracy orthoimage generations with relatively fast speed, even if the overlap rate is low. We provide appearance and spatial correlation-constrained fast low-overlap neighbor candidate query and matching. On the basis of GPS information, we introduce an absolute position and rotation-averaging strategy for global pose initialization, which is essential for the high convergence and efficiency of nonconvex pose optimization of every image. We also propose a planar-restricted global pose graph optimization method. The optimization is extremely efficient and robust considering that point clouds are parameterized to planes. Finally, we apply a matching graph-based exposure compensation and region reduction algorithm for large-scale and high-resolution image fusion with high efficiency and novel precision. Experimental results demonstrate that our method can achieve the state-of-the-art performance while maintaining high precision and robustness.

Whole Genome Sequencing of Hepatitis A Virus Using a PCR-Free Single-Molecule Nanopore Sequencing Approach.

Hepatitis A virus (HAV) is one of the most common causes of acute viral hepatitis in humans. Although HAV has a relatively small genome, there are several factors limiting whole genome sequencing such as PCR amplification artefacts and ambiguities in de novo assembly. The recently developed Oxford Nanopore technologies (ONT) allows single-molecule sequencing of long-size fragments of DNA or RNA using PCR-free strategies. We have sequenced the whole genome of HAV using a PCR-free approach by direct reverse-transcribed sequencing. We were able to sequence HAV cDNA and obtain reads over 7 kilobases in length containing almost the whole genome of the virus. The comparison of these raw long nanopore reads with the HAV reference wild type revealed a nucleotide sequence identity between 81.1 and 96.6%. By de novo assembly of all HAV reads we obtained a consensus sequence of 7362 bases, with a nucleotide sequence identity of 99.0% with the genome of the HAV strain pHM175/18f. When the assembly was performed using as reference the HAV strain pHM175/18f a consensus with a sequence similarity of 99.8 % was obtained. We have also used an ONT amplicon-based assay to sequence two fragments of the VP3 and VP1 regions which showed a sequence similarity of 100% with matching regions of the consensus sequence obtained using the direct cDNA sequencing approach. This study showed the applicability of ONT sequencing technologies to obtain the whole genome of HAV by direct cDNA nanopore sequencing, highlighting the utility of this PCR-free approach for HAV characterization and potentially other viruses of the Picornaviridae family.