The China Hypertrophic Cardiomyopathy Project (CHCMP): The Rationale and Design of a Multicenter, Prospective, Registry Cohort Study.

Hypertrophic cardiomyopathy (HCM) is associated with adverse outcomes, such as heart failure, arrhythmia, and mortality. Sudden cardiac death (SCD) is a common cause of death in HCM patients, and identification of patients at a high risk of SCD is crucial in clinical practice. The China Hypertrophic Cardiomyopathy Project is a hospital-based, multicenter, prospective, registry cohort study of HCM patients, covering a total of 3000 participants and with a 5-year follow-up plan. A large number of demographic characteristics and clinical data will be fully collected to identify prognostic factors in Chinese HCM patients. Furthermore, the main purpose of this study is to integrate demographic and clinical characteristics to establish new 5-year SCD risk predictive equations for Chinese HCM patients by the use of machine learning technologies. The project has crucial clinical significance for risk stratification and determination of HCM patients with high risk of adverse outcomes. CLINICAL TRIALS REGISTRATION: ChiCTR2300070909.

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion.

Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be "patches" on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging.

In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

Deterministic signal associated with a random field.

Stochastic fields do not generally possess a Fourier transform. This makes the second-order statistics calculation very difficult, as it requires solving a fourth-order stochastic wave equation. This problem was alleviated by Wolf who introduced the coherent mode decomposition and, as a result, space-frequency statistics propagation of wide-sense stationary fields. In this paper we show that if, in addition to wide-sense stationarity, the fields are also wide-sense statistically homogeneous, then monochromatic plane waves can be used as an eigenfunction basis for the cross spectral density. Furthermore, the eigenvalue associated with a plane wave, exp[i(k · r-ωt)], is given by the spatiotemporal power spectrum evaluated at the frequency (k, ω). We show that the second-order statistics of these fields is fully described by the spatiotemporal power spectrum, a real, positive function. Thus, the second-order statistics can be efficiently propagated in the wavevector-frequency representation using a new framework of deterministic signals associated with random fields. Analogous to the complex analytic signal representation of a field, the deterministic signal is a mathematical construct meant to simplify calculations. Specifically, the deterministic signal associated with a random field is defined such that it has the identical autocorrelation as the actual random field. Calculations for propagating spatial and temporal correlations are simplified greatly because one only needs to solve a deterministic wave equation of second order. We illustrate the power of the wavevector-frequency representation with calculations of spatial coherence in the far zone of an incoherent source, as well as coherence effects induced by biological tissues.

Correlation-induced spectral changes in tissues.

We report experimental evidence of correlation-induced spectral changes in biological tissues. The overall spectral shift in our transmission measurements is to the red and the mean wavelength of the original spectrum is up 10% larger. These results indicate that the spectral changes due to elastic scattering are significant and likely to hinder all spectroscopic measurements based on the inelastic (i.e., emission and absorption) interaction between light and tissues. Thus, simultaneous morphology and spectral measurements are required for accurate measurements spectroscopic information.

Born approximation model for light scattering by red blood cells.

The primary role of a red blood cell (RBC) is delivering oxygen throughout our body. Abnormalities of this basic function lead to anemia and are caused by numerous diseases such as malaria and sickle cell anemia. As prompt and inexpensive tests for blood screening are in demand, we have developed a faster and reliable way to measure morphological parameters associated with the structure of red blood cells and the size distribution of the cells in a whole blood smear. Modeling the RBC shape under Born approximation, we are able to determine parameters of clinical relevance, such as the diameter, thickness and dimple size. From a measured quantitative phase image of a blood smear, we can determine the average and standard deviation of the red blood cell volume simultaneously, i.e., without analyzing each cell individually. This approach may open the door for a new generation of label-free, high-throughput blood testing.