IoT-based Edge Computing (IoTEC) for Improved Environmental Monitoring.

This research considered several applications of a coupled Internet of Things sensor network with Edge Computing (IoTEC) for improved environmental monitoring. Two pilot applications, covering environmental monitoring of vapor intrusion and system performance of wastewater-based algae cultivation, were designed to compare data latency, energy consumption, and economic cost between the IoTEC approach and the conventional sensor monitoring method. The results show that the IoTEC monitoring approach, compared with conventional IoT sensor networks, could significantly reduce data latency by 13%, and the amount of data transmission decreased by an average of 50%. In addition, the IoTEC method can increase the duration of power supply by 130%. Collectively, these improvements could lead to a compelling cost reduction of 55% - 82% per year for monitoring vapor intrusion at five houses, with more houses leading to more significant savings. Additionally, our results demonstrate the feasibility of deploying machine learning tools at edge servers for more advanced data processing and analysis.

HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection.

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios, such as schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, including flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets, since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://pegasus.ac.cn .

Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting.

COVID-19 has become a matter of serious concern over the last few years. It has adversely affected numerous people around the globe and has led to the loss of billions of dollars of business capital. In this paper, we propose a novel Spatial-Temporal Synchronous Graph Transformer network (STSGT) to capture the complex spatial and temporal dependency of the COVID-19 time series data and forecast the future status of an evolving pandemic. The layers of STSGT combine the graph convolution network (GCN) with the self-attention mechanism of transformers on a synchronous spatial-temporal graph to capture the dynamically changing pattern of the COVID time series. The spatial-temporal synchronous graph simultaneously captures the spatial and temporal dependencies between the vertices of the graph at a given and subsequent time-steps, which helps capture the heterogeneity in the time series and improve the forecasting accuracy. Our extensive experiments on two publicly available real-world COVID-19 time series datasets demonstrate that STSGT significantly outperforms state-of-the-art algorithms that were designed for spatial-temporal forecasting tasks. Specifically, on average over a 12-day horizon, we observe a potential improvement of 12.19% and 3.42% in Mean Absolute Error (MAE) over the next best algorithm while forecasting the daily infected and death cases respectively for the 50 states of US and Washington, D.C. Additionally, STSGT also outperformed others when forecasting the daily infected cases at the state level, e.g., for all the counties in the State of Michigan. The code and models are publicly available at https://github.com/soumbane/STSGT.

Quantification metrics for telangiectasia using optical coherence tomography.

Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disorder that causes vascular malformations throughout the body. The most prevalent and accessible of these lesions are found throughout the skin and mucosa, and often rupture causing bleeding and anemia. A recent increase in potential HHT treatments have created a demand for quantitative metrics that can objectively measure the efficacy of new and developing treatments. We employ optical coherence tomography (OCT)-a high resolution, non-invasive imaging modality in a novel pipeline to image and quantitatively characterize dermal HHT lesion behavior over time or throughout the course of treatment. This study is aimed at detecting detailed morphological changes of dermal HHT lesions to understand the underlying dynamic processes of the disease. We present refined metrics tailored for HHT, developed from a pilot study using 3 HHT patients and 6 lesions over the course of multiple imaging dates, totalling to 26 lesion images. Preliminary results from these lesions are presented in this paper alongside representative OCT images. This study provides a new objective method to analyse and understand HHT lesions using a minimally invasive, accessible, cost-effective, and efficient imaging modality with quantitative metrics describing morphology and blood flow.

Impact of COVID-19 on city-scale transportation and safety: An early experience from Detroit.

The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City---Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus and the rising number of COVID-19 confirmed cases and deaths. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goals of this work are: i) figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the City of Detroit, ii) determining whether each type of data (e.g. traffic volume data) could be a useful factor in the confirmed-cases prediction, and iii) providing an early future prediction method for COVID-19 rates, which can be a vital contributor to life-saving advanced preventative and preparatory responses. In addressing these problems, the prediction results of six feature groups are presented and analyzed to quantify the prediction effectiveness of each type of data. Then, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next week. The model demonstrated a promising prediction result with a coefficient of determination ( R 2 ) of up to approximately 0.91. Furthermore, six essential observations with supporting evidence are presented, which will be helpful for decision-makers to take specific measures that aid in preventing the spread of COVID-19 and protecting public health and safety. The proposed approaches could be applied, customized, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.

Real-time en-face Gabor optical coherence tomographic angiography on human skin using CUDA GPU.

We recently proposed an optical coherence tomographic angiography (OCTA) algorithm, Gabor optical coherence tomographic angiography (GOCTA), which can extract microvascular signals from a spectral domain directly with lower computational complexity compared to other algorithms. In this manuscript, we combine a programmable swept source, an OCT complex signal detecting unit, and graphics process units (GPU) to achieve a real-time en-face GOCTA system for human skin microvascular imaging. The programmable swept source can balance the A-scan rate and the spectral tuning range; the polarization-modulation based complex signal detecting unit can double the imaging depth range, and the GPU can accelerate data processing. C++ and CUDA are used as the programming platform where five parallel threads are created for galvo-driving signal generation, data acquisition, data transfer, data processing, and image display, respectively. Two queues (for the raw data and en-face images, respectively) are used to improve the data exchange efficiency among different devices. In this study, the data acquisition time and data processing time for each 3D complex volume (256×304×608 pixels,) are 405.3 and 173.7 milliseconds respectively. To the best of our knowledge, this is the first time to show en-face microvascular images covering 3×3 mm2 at a refresh rate of 2.5 Hz.

Gabor optical coherence tomographic angiography (GOCTA) (Part II): theoretical basis of sensitivity improvement and optimization for processing speed.

We previously proposed a Gabor optical coherence tomography angiography (GOCTA) algorithm for spectral domain optical coherence tomography (SDOCT) to extract microvascular signals from spectral fringes directly, with speed improvement of 4 to 20 times over existing methods. In this manuscript, we explored the theoretical basis of GOCTA with comparison of experimental data using solid and liquid displacement sample targets, demonstrating that the majority of the GOCTA sensitivity advantage over speckle variance based techniques was in the small displacement range (< 10 ∼ 20 µm) of the moving target (such as red blood cells). We further normalized GOCTA signal by root-mean-square (RMS) of original fringes, achieving a more uniform image quality, especially at edges of blood vessels where slow flow could occur. Furthermore, by transecting the spectral fringes and using skipped convolution, the data processing speed could be further improved. We quantified the trade-off in signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) under various sub-spectral bands and found an optimized condition using 1/4 spectral band for minimal angiography image quality degradation, yet achieving a further 26.7 and 34 times speed improvement on GPU and CPU, respectively. Our optimized GOCTA algorithm has a speed advantage of over 140 times compared to existing speckle variance OCT (SVOCT) method.

2D MEMS-based high-speed beam-shifting technique for speckle noise reduction and flow rate measurement in optical coherence tomography.

In this manuscript, a two-dimensional (2D) micro-electro-mechanical system (MEMS)-based, high-speed beam-shifting spectral domain optical coherence tomography (MHB-SDOCT) is proposed for speckle noise reduction and absolute flow rate measurement. By combining a zigzag scanning protocol, the frame rates of 45.2 Hz for speckle reduction and 25.6 Hz for flow rate measurement are achieved for in-vivo tissue imaging. Phantom experimental results have shown that by setting the incident beam angle to ϕ = 4.76° (between optical axis of objective lens and beam axis) and rotating the beam about the optical axis in 17 discrete angular positions, 91% of speckle noise in the structural images can be reduced. Furthermore, a precision of 0.0032 µl/s is achieved for flow rate measurement with the same beam angle, using three discrete angular positions around the optical axis. In-vivo experiments on human skin and chicken embryo were also implemented to further verify the performance of speckle noise reduction and flow rate measurement of MHB-SDOCT.

Differential phase standard-deviation-based optical coherence tomographic angiography for human retinal imaging in vivo.

We present a differential phase standard-deviation (DPSD)-based optical coherence tomographic (OCT) angiography (OCTA) technique to calculate the angiography images of the human retina. The standard deviation was calculated along the depth direction on the differential phase image of two B-scans (from the same position, at different times) to contrast dynamic vascular signals. The performance of a DPSD was verified by both phantom and in vivo experiments. When compared to other OCTA algorithms such as phase variance OCT, speckle variance OCT, and optical microangiography, we showed that a DPSD achieved improved image contrast and higher sensitivity. Furthermore, we also found the improved signal-to-noise ratio and contrast-to-noise ratio of 1.6 dB and 0.5, respectively, in large scanning range images.

Beam-shifting technique for speckle reduction and flow rate measurement in optical coherence tomography.

In this Letter, we propose a beam-shifting optical coherence tomography scheme for speckle reduction and blood flow rate calculation, where variations of the speckle pattern and Doppler angle were generated by parallel shifting of the sample beam incident on the objective lens. The resultant optical coherent tomography images could then be averaged for speckle noise reduction and simultaneously analyzed for flow rate measurement. The performance of the proposed technique was verified by both phantom and in vivo experiments.

Buffer-averaging super-continuum source based spectral domain optical coherence tomography for high speed imaging.

In super-continuum (SC) source based spectral domain optical coherence tomography (SC-SDOCT), the stability of the power spectral density (PSD) has a significant impact on OCT system sensitivity and image signal to noise ratio (SNR). High speed imaging decreases the camera's exposure time, thus each A-scan contained fewer laser pulse excited SC wideband emissions, resulting in a decrease of SNR. In this manuscript, we present a buffer-averaging SC-SDOCT (BASC-SDOCT) to improve the system's performance without losing imaging speed, taking advantage of the excess output power from typical SC sources. In our proposed technique, the output light from SC was passed through a fiber based light buffering and averaging system to improve the PSD stability by averaging 8 SC emissions. The results showed that 6.96 µs of SC emission after buffering and averaging can achieve the same PSD stability equivalent to a longer exposure time of 55.68 µs, despite increasing the imaging speed from 16.8 kHz to 91.9 kHz. The system sensitivity was improved by 8.6 dB, reaching 100.6 dB, which in turn improved SNR of structural imaging, Doppler OCT velocity measurement, and speckle variance OCT (SVOCT) angiographic imaging as demonstrated by phantom and in vivo experiments.

Differential standard deviation of log-scale intensity based optical coherence tomography angiography.

In this paper, a differential standard deviation of log-scale intensity (DSDLI) based optical coherence tomography angiography (OCTA) is presented for calculating microvascular images of human skin. The DSDLI algorithm calculates the variance in difference images of two consecutive log-scale intensity based structural images from the same position along depth direction to contrast blood flow. The en face microvascular images were then generated by calculating the standard deviation of the differential log-scale intensities within the specific depth range, resulting in an improvement in spatial resolution and SNR in microvascular images compared to speckle variance OCT and power intensity differential method. The performance of DSDLI was testified by both phantom and in vivo experiments. In in vivo experiments, a self-adaptive sub-pixel image registration algorithm was performed to remove the bulk motion noise, where 2D Fourier transform was utilized to generate new images with spatial interval equal to half of the distance between two pixels in both fast-scanning and depth directions. The SNRs of signals of flowing particles are improved by 7.3 dB and 6.8 dB on average in phantom and in vivo experiments, respectively, while the average spatial resolution of images of in vivo blood vessels is increased by 21%.

Imaginary part-based correlation mapping optical coherence tomography for imaging of blood vessels in vivo.

We present an imaginary part-based correlation mapping optical coherence tomography (IMcmOCT) technique for in vivo blood vessels imaging. In the conventional correlation mapping optical coherence tomography (cmOCT) method, two adjacent frames of intensity-based structural images are correlated to extract blood flow information and the size of correlation window has to be increased to improve the signal-to-noise ratio of microcirculation maps, which may cause image blur and miss the small blood vessels. In the IMcmOCT method, the imaginary part of a depth-resolved complex analytic signal in two adjacent B-scans is correlated to reconstruct microcirculation maps. Both phantom and in vivo experiments were implemented to demonstrate that the proposed method can provide improved sensitivity for extracting blood flow information in small vessels.

The relationship of chronic and momentary work stress to cardiac reactivity in female managers: feasibility of a smart phone-assisted assessment system.

OBJECTIVES: To evaluate a wireless smart phone-assisted (SPA) system that assesses ongoing heart rate (HR) and HR-triggered participant reports of momentary stress when HR is elevated during daily life. This SPA system was used to determine the independent and interactive roles of chronic and momentary work stress on HR reactivity among female managers. METHODS: A sample of 40 female managers reported their chronic work stress and wore the SPA system during a regular workday. They provided multiple reports of their momentary stress, both when triggered by increased HR and at random times. Relationships among chronic stress, momentary stress, and HR were analyzed with hierarchical linear modeling. RESULTS: Both chronic work stress (b = 0.08, standard error [SE] = 0.03, p = .003) and momentary work stress (b = 1.25, SE = 0.62, p = .052) independently predicted greater HR reactivity, adjusting for baseline HR, age, smoking, caffeine, alcohol use, and momentary physical activity levels. More importantly, chronic and momentary stress significantly interacted (b = 1.00, SE = 0.04, p = .036); high momentary stress predicted elevated HR only in the context of high chronic stress. CONCLUSIONS: Female managers who experience chronic work stress displayed elevated cardiac reactivity during momentary stress at work. The joint assessment of chronic stress and momentary stress and their relationship to physiological functioning during work clarifies the potential health risks associated with work stress. Moreover, this wireless SPA system captures the immediate subjective context of individuals when physiological arousal occurs, which may lead to tailored stress management programs in the workplace.

Quantum attack-resistent certificateless multi-receiver signcryption scheme.

The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC), which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC) based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ) problem and its unforgeability under the Isomorphism of Polynomials (IP) assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards.

HONEY: a multimodality fall detection and telecare system.

BACKGROUND: The increasing cost in terms of money and healthcare resources is driving healthcare providers to provide home-based telecare instead of institutionalized healthcare. Falling is one of the most common and dangerous accidents for elderly individuals and a significant factor affecting the living quality of the elderly. Many efforts have been put toward providing a robust method to detect falls accurately and in a timely manner. This study facilitated a reliable, safe, and real-time home-based healthcare environment, which we have termed the Home Healthcare Sentinel System (HONEY), to detect falls for elderly people in the home telecare environment. The basic idea of HONEY is a three-step detection scheme that consists of multimodality signal sources, including an accelerometer sensor, audio, images, and video clips via speech recognition and on-demand video techniques. MATERIALS AND METHODS: The magnitude of acceleration, corresponding to a user's movements, triggers fall detection combining speech recognition and on-demand video. If a fall occurs, an alarm e-mail is delivered to medical staff or caregivers at once, containing the fall information, so that caregivers could make a primary diagnosis based on it. This article also describes the implementation of the prototype of HONEY. RESULTS: A comprehensive evaluation with 10 volunteers shows that HONEY has high accuracy of 94% for fall detection, 18% higher than the Advanced Magnitude Algorithm (AMA), which is a wearable sensor-based method, and the false-positive and false-negative rates are 3% and 10%, respectively, 19% and 16% lower than AMA, respectively. The average response time for a detected fall is 46.2 s, which is also short enough for first aid. CONCLUSIONS: In summary, HONEY provides a highly reliable and convenient fall detection solution for the home-based environment.

Sequence analysis of the Meq gene in the predominant Marek's disease virus strains isolated in China during 2006-2008.

The main aim of the present study were to investigate sequence diversity in the Meq gene of Marek's disease viruses (MDV) isolated in China and to determine the most prevalent MDV strains. The 19 MDV strains were isolated from dead or diseased chickens from different chicken farms in China during 2006-2008, and the Meq gene was sequenced from each of these strains. Sequence analysis showed that all of the isolates contained an open reading frame of 1020 nucleotides, which encoded a 339 amino acid peptide. Compared with reference MDV strains, 12 of the 19 MDV isolates possessed two amino acid substitutions, (T → A) at position 139 and (P → R) at position 176, one isolate shared sequence similarity with the attenuated strain CVI988, and five of the other six isolates exhibited one amino acid change (P → T) at position 177 or 176. The 19 MDV isolates shared between 99.0 and 100% nucleotide sequence homology, and between 97.7 and 100% amino acid sequence homology. The nucleotide and amino acid sequence identity between the 19 MDV isolates and the 25 reference MDV strains varied from 97.6 to 100% and 94.4 to 100%, respectively. Based on the phylogenetic relationships between Meq gene sequences, Chinese MDV isolates constituted a separate clade to MDV reference strains, demonstrating that a different genotype of MDV was prevalent in China between 2006 and 2008.

CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping.

BACKGROUND: Research in genetics has developed rapidly recently due to the aid of next generation sequencing (NGS). However, massively-parallel NGS produces enormous amounts of data, which leads to storage, compatibility, scalability, and performance issues. The Cloud Computing and MapReduce framework, which utilizes hundreds or thousands of shared computers to map sequencing reads quickly and efficiently to reference genome sequences, appears to be a very promising solution for these issues. Consequently, it has been adopted by many organizations recently, and the initial results are very promising. However, since these are only initial steps toward this trend, the developed software does not provide adequate primary functions like bisulfite, pair-end mapping, etc., in on-site software such as RMAP or BS Seeker. In addition, existing MapReduce-based applications were not designed to process the long reads produced by the most recent second-generation and third-generation NGS instruments and, therefore, are inefficient. Last, it is difficult for a majority of biologists untrained in programming skills to use these tools because most were developed on Linux with a command line interface. RESULTS: To urge the trend of using Cloud technologies in genomics and prepare for advances in second- and third-generation DNA sequencing, we have built a Hadoop MapReduce-based application, CloudAligner, which achieves higher performance, covers most primary features, is more accurate, and has a user-friendly interface. It was also designed to be able to deal with long sequences. The performance gain of CloudAligner over Cloud-based counterparts (35 to 80%) mainly comes from the omission of the reduce phase. In comparison to local-based approaches, the performance gain of CloudAligner is from the partition and parallel processing of the huge reference genome as well as the reads. The source code of CloudAligner is available at http://cloudaligner.sourceforge.net/ and its web version is at http://mine.cs.wayne.edu:8080/CloudAligner/. CONCLUSIONS: Our results show that CloudAligner is faster than CloudBurst, provides more accurate results than RMAP, and supports various input as well as output formats. In addition, with the web-based interface, it is easier to use than its counterparts.

[Mutational analysis of Meq, RLORF4, RLORF12 and 132bpr genes of epidemic Marek's disease virus strains highly passaged on chicken embryo fibroblast].

Recently, much work has been devoted to study MD-induced oncogenesis and the genes involved in this process. Among many genes in the MDV genome, several genes such as Meq, RLORF4, RLORF12, and 132bpr have been considered recently associated with virulence of MDV. In this paper, primers of Meq, RLORF4, RLORF12 and 132bpr genes were designed and synthesized, based on the published whole genome sequence of MDV strain GA. The genes of Meq, RLORF4 and RLORF12 from four Chinese epidemic MDV strains highly passaged on chicken embryo fibroblast (CEF), i. e. L-SYp85C, L-MSp75C, L-CZp75C, and L-ZYp75C, as well as their corresponding parent strains, i. e. L-SY, L-MS, L-CZ, and L-ZY, the reference virulent strain J-1 and the vaccine strain 814 were amplified by PCR respectively. Then the PCR products of interest were cloned and sequenced respectively. The results of sequence comparison and analysis of Meq genes in the study indicated that Meq genes from the two strains L-ZYp75C and L-CZp75C contained single nucleotide insertion and deletion. The Meq gene from strain L-ZYp75C contained an extra cytidine (C) insertion at nucleotide position 529 and a single thymidine (T) deletion at nucleotide position 602, resulting in a frameshift mutation. And this frameshift mutation could lead to changes in deduced amino acid sequence from position 177 to 200 of Meq gene. The extra C insertion at nucleotide position 625 in Meq gene of strain L-CZp75C was also predicted to cause frameshift mutation in three overlapping genes (Meq, RLORF6 and 23KD genes). The comparison of nucleotide sequences of RLORF4 genes in the study revealed that the RLORF4 gene of strain L-SYp85C contained a fragment deletion in Open Reading Frame (ORF) from nucleotide position 215 to 265, resulting in 17 amino acids deletions, which were not found in other sequenced strains. Comparison of nucleotide sequences of RLORF12 genes in the study revealed several mutations. The RLORF12 gene of strain L-MSp75C contained a single T deletion at nucleotide position 67 and of 814 vaccine strain a large fragment deletion from nucleotide position 18 to 86, both of the deletions located in Origin of replication site (Ori) of MDV genome. But strain L-ZYp75C possessed an unique "TGTTGGG" deletion in its RLORF12 gene. When the four Chinese epidemic MDV strains were serially passaged on CEF, the number of copies of the 132bp repeats increased from 2 to more than 10 copies. All of above results indicated that deletion and/or insertion mutation occurred in Meq, RLORF4, RLORF12 and 132bpr after serial passage of these four Chinese epidemic MDV strains on CEF.

[Cloning and sequence analysis of the Meq gene of 4 Marek's disease virus isolates from China].

Earlier studies have determined that the repeat regions of oncogenic serotype 1 MDV (Marek's disease virus) encode a basic leucine zipper protein, Meq, which structurally resembles the Jun/Fos family of transcriptional activators. Meq has been suggested as the MDV-associated oncogene. In this paper, based on the published sequence of Meq gene of GA strain of MDV, a pair of primers were designed and synthesized. Meq gene ORF (Open reading frame) of the four Chinese local MDV isolates, the reference strain J-1 and the vaccine strain 814 were amplified by using polymerase chain reaction(PCR). Then the PCR products were cloned and sequenced respectively. The results of sequence comparison indicated that the sequences of Meq gene in different strains are relatively conserved and homology of the amino acid sequences is 96.5%-99.7%. The proline-rich repeats of Meq gene of four MDV isolates have site mutations, and it is related to MDV's virulence. Two unique site mutations appear in Meq gene of Chinese local MDV isolates, but they aren't present in Meq gene of the published MDV strains from abroad and the early domestic strains. It seems that some regularities exist between such mutations in four Chinese local MDV isolates and the virulence of MDV, but the regularities need further research.