A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG

In this paper, a fully-mapped field programmable gate array (FPGA) accelerator is proposed for artificial intelligence (AI)-based analysis of electrocardiogram (ECG). It consists of a fully-mapped 1-D convolutional neural network (CNN) and a fully-mapped heart rate estimator, which constitute a complementary dual-function analysis. The fully-mapped design projects each layer of the 1-D CNN to a hardware module on an Intel Cyclone V FPGA, and a virtual flatten layer is proposed to effectively bridge the feature extraction layers and fully-connected layer. Also, the fully-mapped design maximizes computational parallelism to accelerate CNN inference. For the fully-mapped heart rate estimator, it performs pipelined transformations, self-adaptive threshold calculation, and heartbeat count on the FPGA, without multiplexed usage of hardware resources. Furthermore, heart rate calculation is elaborately analyzed and optimized to remove division and acceleration, resulting in an efficient method suitable for hardware implementation. According to our experiments on 1-D CNN, the accelerator can achieve 43.08× and 8.38× speedup compared with the software implementations on ARM-Cortex A53 quad-core processor and Intel Core i7-8700 CPU, respectively. For the heart rate estimator, the hardware implementations are 25.48× and 1.55× faster than the software implementations on the two aforementioned platforms. Surprisingly, the accelerator achieves an energy efficiency of 63.48 GOPS/W, which obviously surpasses existing studies. Considering its power consumption is only 67.74 mW, it may be more suitable for resource-limited applications, such as wearable and portable devices for ECG monitoring

A semi-quantitative upconversion nanoparticle-based immunochromatographic assay for SARS-CoV-2 antigen detection

The unprecedented public health and economic impact of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been met with an equally unprecedented scientific response. Sensitive point-of-care methods to detect SARS-CoV-2 antigens in clinical specimens are urgently required for the rapid screening of individuals with viral infection. Here, we developed an upconversion nanoparticle-based lateral flow immunochromatographic assay (UCNP-LFIA) for the high-sensitivity detection of SARS-CoV-2 nucleocapsid (N) protein. A pair of rabbit SARS-CoV-2 N-specific monoclonal antibodies was conjugated to UCNPs, and the prepared UCNPs were then deposited into the LFIA test strips for detecting and capturing the N protein. Under the test conditions, the limit of detection (LOD) of UCNP-LFIA for the N protein was 3.59 pg/mL, with a linear range of 0.01–100 ng/mL. Compared with that of the current colloidal gold-based LFIA strips, the LOD of the UCNP-LFIA-based method was increased by 100-fold. The antigen recovery rate of the developed method in the simulated pharyngeal swab samples ranged from 91.1 to 117.3%. Furthermore, compared with the reverse transcription-polymerase chain reaction, the developed UCNP-LFIA method showed a sensitivity of 94.73% for 19 patients with COVID-19. Thus, the newly established platform could serve as a promising and convenient fluorescent immunological sensing approach for the efficient screening and diagnosis of COVID-19

Genome-Wide Analysis of DNA Methylation and Cigarette Smoking in a Chinese Population

Background: Smoking is a risk factor for many human diseases. DNA methylation has been related to smoking, but genome-wide methylation data for smoking in Chinese populations is limited. Objectives: We aimed to investigate epigenome-wide methylation in relation to smoking in a Chinese population. Methods: We measured the methylation levels at > 485,000 CpG sites (CpGs) in DNA from leukocytes using a methylation array and conducted a genome-wide meta-analysis of DNA methylation and smoking in a total of 596 Chinese participants. We further evaluated the associations of smoking-related CpGs with internal polycyclic aromatic hydrocarbon (PAH) biomarkers and their correlations with the expression of corresponding genes. Results: We identified 318 CpGs whose methylation levels were associated with smoking at a genome-wide significance level (false discovery rate < 0.05), among which 161 CpGs annotated to 123 genes were not associated with smoking in recent studies of Europeans and African Americans. Of these smoking-related CpGs, methylation levels at 80 CpGs showed significant correlations with the expression of corresponding genes (including RUNX3, IL6R, PTAFR, ANKRD11, CEP135 and CDH23), and methylation at 15 CpGs was significantly associated with urinary 2-hydroxynaphthalene, the most representative internal monohydroxy-PAH biomarker for smoking. Conclusion: We identified DNA methylation markers associated with smoking in a Chinese population, including some markers that were also correlated with gene expression. Exposure to naphthalene, a byproduct of tobacco smoke, may contribute to smoking-related methylation. Citation: Zhu X, Li J, Deng S, Yu K, Liu X, Deng Q, Sun H, Zhang X, He M, Guo H, Chen W, Yuan J, Zhang B, Kuang D, He X, Bai Y, Han X, Liu B, Li X, Yang L, Jiang H, Zhang Y, Hu J, Cheng L, Luo X, Mei W, Zhou Z, Sun S, Zhang L, Liu C, Guo Y, Zhang Z, Hu FB, Liang L, Wu T. 2016. Genome-wide analysis of DNA methylation and cigarette smoking in Chinese. Environ Health Perspect 124:966–973; http://dx.doi.org/10.1289/ehp.150983

A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD

The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy

STATISTICAL MODELLING OF RNA-SEQ DATA AND DENSITY ESTIMATION IN FINITE MIXTURE MODELS

Ph.DDOCTOR OF PHILOSOPHY (FOS

High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management.

OBJECTIVE:To investigate the performance of high-order radiomics features and models based on T2-weighted fluid-attenuated inversion recovery (T2 FLAIR) in predicting the immunohistochemical biomarkers of glioma, in order to execute a non-invasive, more precise and personalized glioma disease management. METHODS:51 pathologically confirmed gliomas patients committed in our hospital from March 2015 to June 2018 were retrospective analysis, and Ki-67, vimentin, S-100 and CD34 immunohistochemical data were collected. The volumes of interest (VOIs) were manually sketched and the radiomics features were extracted. Feature reduction was performed by ANOVA+ Mann-Whiney, spearman correlation analysis, least absolute shrinkage and selection operator (LASSO) and Gradient descent algorithm (GBDT). SMOTE technique was used to solve the data bias between two groups. Comprehensive binary logistic regression models were established. Area under the ROC curves (AUC), sensitivity, specificity and accuracy were used to evaluate the predict performance of models. Models reliability were decided according to the standard net benefit of the decision curves. RESULTS:Four clusters of significant features were screened out and four predicting models were constructed. AUC of Ki-67, S-100, vimentin and CD34 models were 0.713, 0.923, 0.854 and 0.745, respectively. The sensitivities were 0.692, 0.893, 0.875 and 0.556, respectively. The specificities were: 0.667, 0.905, 0.722, and 0.875, with accuracy of 0.660, 0.898, 0.738, and 0.667, respectively. According to the decision curves, the Ki-67, S-100 and vimentin models had reference values. CONCLUSION:The radiomics features based on T2 FLAIR can potentially predict the Ki-67, S-100, vimentin and CD34 expression. Radiomics model were expected to be a computer-intelligent, non-invasive, accurate and personalized management method for gliomas

Clinical analysis of 152 cases of multiple primary malignant tumors in 15,398 patients with malignant tumors.

In this study, the etiology, clinical characteristics, and prognosis of multiple primary malignant tumors (MPMTs) were investigated. Furthermore, we analyzed the treatment factors associated with MPMTs.From 15,398 patients with malignant tumors presenting to The First Hospital of Jilin University, China, between January 2010 and December 2013, we identified and analyzed patients with MPMTs. Data were obtained retrospectively from the hospital database.The prevalence of MPMTs in this study was 0.99% (152/15398): 51 cases were synchronous MPMTs, and 101 cases were metachronous MPMTs. The mean time between the first and second primary cancer was 43.1 months. In this population, MPMTs were observed more frequently in patients with head and neck tumors (5.65%) and urinary tumors (4.19%); the prevalence of MPMTs in these patients was over 4-fold greater than the prevalence of MPMTs in all patients (0.99%). There were no cases of MPMTs in 132 cases of nervous system tumors and 404 cases of multiple myeloma. Nearly 50% (45.4%) of patients with MPMTs did not receive chemotherapy or radiotherapy before the second primary cancer was diagnosed. Eighty-five patients with MPMTs were followed for more than 2 years, and the 2-year cumulative survival rate was 40.8%.In this study, the prevalence of MPMTs was 0.99% (152/15398), which is consistent with the Chinese literature. Patients with head and neck tumors or urinary tumors are at greater risk of developing MPMTs. In addition to radiotherapy or chemotherapy, this study suggests that other factors may contribute to MPMTs