Dokyung Lee

Clinical Evaluation of a Loop-Mediated Isothermal Amplification (LAMP) Assay for Rapid Detection of Neisseria meningitidis in Cerebrospinal Fluid

Background Neisseria meningitidis (Nm) is a leading causative agent of bacterial meningitis in humans. Traditionally, meningococcal meningitis has been diagnosed by bacterial culture. However, isolation of bacteria from patients’ cerebrospinal fluid (CSF) is time consuming and sometimes yields negative results. Recently, polymerase chain reaction (PCR)-based diagnostic methods of detecting Nm have been considered the gold standard because of their superior sensitivity and specificity compared with culture. In this study, we developed a loop-mediated isothermal amplification (LAMP) method and evaluated its ability to detect Nm in cerebrospinal fluid (CSF). Methodology/Principal Findings We developed a meningococcal LAMP assay (Nm LAMP) that targets the ctrA gene. The primer specificity was validated using 16 strains of N. meningitidis (serogroup A, B, C, D, 29-E, W-135, X, Y, and Z) and 19 non-N. meningitidis species. Within 60 min, the Nm LAMP detected down to ten copies per reaction with sensitivity 1000-fold more than that of conventional PCR. The LAMP assays were evaluated using a set of 1574 randomly selected CSF specimens from children with suspected meningitis collected between 1998 and 2002 in Vietnam, China, and Korea. The LAMP method was shown to be more sensitive than PCR methods for CSF samples (31 CSF samples were positive by LAMP vs. 25 by PCR). The detection rate of the LAMP method was substantially higher than that of the PCR method. In a comparative analysis of the PCR and LAMP assays, the clinical sensitivity, specificity, positive predictive value, and negative predictive value of the LAMP assay were 100%, 99.6%, 80.6%, and 100%, respectively. Conclusions/Significance Compared to PCR, LAMP detected Nm with higher analytical and clinical sensitivity. This sensitive and specific LAMP method offers significant advantages for screening patients on a population basis and for diagnosis in clinical settings.

Molecular Insights Into the Evolutionary Pathway of Vibrio cholerae O1 Atypical El Tor Variants

Pandemic V. cholerae strains in the O1 serogroup have 2 biotypes: classical and El Tor. The classical biotype strains of the sixth pandemic, which encode the classical type cholera toxin (CT), have been replaced by El Tor biotype strains of the seventh pandemic. The prototype El Tor strains that produce biotype-specific cholera toxin are being replaced by atypical El Tor variants that harbor classical cholera toxin. Atypical El Tor strains are categorized into 2 groups, Wave 2 and Wave 3 strains, based on genomic variations and the CTX phage that they harbor. Whole-genome analysis of V. cholerae strains in the seventh cholera pandemic has demonstrated gradual changes in the genome of prototype and atypical El Tor strains, indicating that atypical strains arose from the prototype strains by replacing the CTX phages. We examined the molecular mechanisms that effected the emergence of El Tor strains with classical cholera toxin-carrying phage. We isolated an intermediary V. cholerae strain that carried two different CTX phages that encode El Tor and classical cholera toxin, respectively. We show here that the intermediary strain can be converted into various Wave 2 strains and can act as the source of the novel mosaic CTX phages. These results imply that the Wave 2 and Wave 3 strains may have been generated from such intermediary strains in nature. Prototype El Tor strains can become Wave 3 strains by excision of CTX-1 and re-equipping with the new CTX phages. Our data suggest that inter-chromosomal recombination between 2 types of CTX phages is possible when a host bacterial cell is infected by multiple CTX phages. Our study also provides molecular insights into population changes in V. cholerae in the absence of significant changes to the genome but by replacement of the CTX prophage that they harbor.

A Novel Loop-Mediated Isothermal Amplification Assay for Serogroup Identification of Neisseria meningitidis in Cerebrospinal Fluid

We have developed a novel Neisseria meningitidis serogroup-specific loop-mediated isothermal amplification (LAMP) assay for six of the most common meningococcal serogroups (A, B, C, W, X, and Y). The assay was evaluated using a set of 31 meningococcal LAMP assay positive cerebrospinal fluid (CSF) specimens from 1574 children with suspected meningitis identified in prospective surveillance between 1998 and 2002 in Vietnam, China, and Korea. Primer specificity was validated using 15 N. meningitidis strains (including serogroups A, B, C, E, W, X, Y, and Z) and 19 non-N. meningitidis species. The N. meningitidis serogroup LAMP detected down to ten copies and 100 colony-forming units per reaction. Twenty-nine CSF had N. meningitidis serogroup identified by LAMP compared with two CSF in which N. meningitidis serogroup was identified by culture and multi-locus sequence typing. This is the first report of a serogroup-specific identification assay for N. meningitidis using the LAMP method. Our results suggest that this assay will be a rapid, sensitive, and uniquely serogroup-specific assay with potential for application in clinical laboratories and public health surveillance systems.

Fast intra coding unit decision for high efficiency video coding based on statistical information

The latest video coding compression standard is known as highefficiency video coding (HEVC). It supports high-resolution video sequences and has better coding performance than the previous standard H.264/AVC. A quad-tree based coding unit (CU) partitioning process is one of the most efficient technologies used in an HEVC encoder. A coding tree unit (typically 6464) can be split into smaller CUs based on rate-distortion optimization, allowing various types of video content to be adaptively compressed. In addition, intra prediction of the HEVC standard supports 35 prediction modes (planar, DC, and 33 angular modes) to improve coding efficiency. However, the computational complexity of HEVC encoder becomes a critical problem when implement with an encoder. Thus, a fast CU size decision algorithm for intra prediction of an HEVC encoder is proposed in this study. We utilize image complexity and an adaptive depth prediction for early split CU decision making. In addition, the Bayesian decision rule and quadratic discriminant analysis are used for early termination of the CU partitioning process. Experimental results show that our proposed algorithm considerably reduces encoding time by approximately 55.47% with only a small BD-BR loss (1.01%) compared to the HEVC reference software HM 16.0. Predicted depth and variance difference are exploited for early-split CU detection.CUs partitioning process can be terminated by the Bayesian decision rule and quadratic discriminant analysis.Using online learning system, thresholds of proposed algorithm are periodically updated.Proposed algorithm reduces encoding time up to 55.47% with 1.01% BD-BR loss.

Incidence of Deep Vein Thrombosis and Venous Thromboembolism following TKA in Rheumatoid Arthritis versus Osteoarthritis: A Meta-Analysis.

This meta-analysis was designed to compare the incidence of deep vein thrombosis (DVT) and venous thromboembolism (VTE) following total knee arthroplasty (TKA) in patients with rheumatoid arthritis (RA) and osteoarthritis (OA). All studies directly comparing the post-TKA incidence of DVT and/or VTE in patients with RA and OA were included. For all comparisons, odds ratios and 95% confidence intervals (CI) were calculated for binary outcomes. Six studies were included in the meta-analysis. The pooled data showed that the combined rates of asymptomatic and symptomatic DVT did not differ significantly in the RA and OA groups (1065/222,714 [0.5%] vs. 35,983/6,959,157 [0.5%]; OR 0.77, 95% CI: 0.57 to 1.02; P = 0.07). The combined rates of asymptomatic and symptomatic DVT and pulmonary embolism (PE) after TKA were significantly lower in the RA than in the OA group (1831/225,406 [0.8%] vs. 63,953/7,018,721 [0.9%]; OR 0.76, 95% CI: 0.62 to 0.93; P = 0.008). Conclusiviely, the DVT rates after primary TKA were similar in RA and OA patients. In contrast, the incidence of VTE (DVT plus PE) after primary TKA was lower in RA than in OA patients, despite patients with RA being at theoretically higher risk of thrombi due to chronic inflammation.

Fast CU size decision algorithm using machine learning for HEVC intra coding

Abstract High Efficiency Video Coding (HEVC) is a state-of-the-art video compression standard which improves coding efficiency significantly compared with the previous coding standard, H.264/AVC. In the HEVC standard, novel technologies consuming massive computational power are adopted, such as quad-tree-based coding unit (CU) partitioning. Although an HEVC encoder can efficiently compress various video sequences, the computational complexity of an exhaustive search has become a critical problem in HEVC encoder implementation. In this paper, we propose a fast algorithm for the CU partitioning process of the HEVC encoder using machine learning methods. A complexity measure based on the Sobel operator and rate-distortion costs are defined as features for our algorithm. A CU size can be determined early by employing Fisher’s linear discriminant analysis and the k-nearest neighbors classifier. The statistical data used for the proposed algorithm is updated by adaptive online learning phase. The experimental results show that the proposed algorithm can reduce encoding time by approximately 54.0% with a 0.68% Bjontegaard-Delta bit-rate increase.

Fast transform mode decision for HEVC screen content coding

A screen content coding (SCC) standard based on high efficiency video coding (HEVC) was finalized by joint collaborative team on video coding (JCT-VC). The coding efficiency of the standard has been improved by adopting new technologies, such as intra block copy, palette mode, and adaptive motion vector resolution. However, the encoding time is significantly increased. Also, a transform skip mode is selected more frequently because traditional transform techniques for video compression cannot efficiently compress screen content videos. Therefore, we propose an early determination method for transform process in the HEVC-SCC. Residual variance is employed as a measure of complexity. Using statistics of residual variance and an online learning system, we can adaptively determine a threshold to detect the transform skip mode. Experimental results demonstrate that the proposed algorithm successfully reduces encoding time with a small coding efficiency loss.

Adaptive two-step adjustable partial distortion search algorithm for motion estimation

Motion estimation is widely used in video coding schemes, because it enables the transmission and storage of video signals within lower bit rate. A full search (FS) algorithm is optimal method of motion estimation, but it is suffers from high computational complexity. To reduce the complexity various methods are proposed. Recently, two-step edge based partial distortion search algorithm (TS-EPDS) is introduced, and it shows about 100 times faster than FS without PSNR degradation. In this paper, we proposed adaptive two-step adjustable partial distortion search algorithm, and it is 200 times faster than FS with negligible PSNR decrease. The proposed algorithm is suitable for real time implementation of high quality digital video application.

Efficient starting point decision for enhanced hexagonal search

In order adapt the center-biased characteristic of motion information in the real world video sequences, an improved method for starting point is proposed in this paper. For precise prediction of motion information in current block, we referred to motion vector of blocks in the reference frame and current frame. We also modified the search pattern of first step in enhanced hexagonal search. Experimental results show that the proposed algorithm reduces computational complexity in terms of the both time and search point, and improve peak-to-signal ratio of video sequence.