The Research Progress of Monogenic Inherited Hypertension

Monogenic inherited hypertension, which is caused by a single gene mutation, generally conforms to the Mendel’s law, but its phenotype is affected by environmental factors as well. This type of hypertension is characterized by early onset (more common in adolescents), family history, severe hypertension, or refractory hypertension. It is often accompanied by abnormal hormone level and biochemical indicators, including low activity of plasma renin, abnormal potassium, and acid-base metabolization disorder. For adolescents with a family history of moderate to severe hypertension, hormone level (including plasma renin-angiotensin-aldosterone, cortisol, and sex hormone) and blood electrolytes should be measured and the detailed diagnosis should be determined according to medical history, physical signs, and test results. Currently, 17 kinds of monogenic hereditary hypertension have been clearly determined. Thanks to the development of gene detection technology, the diagnostic level of monogenic inherited hypertension has greatly improved and the pathogenesis has been gradually clarified. Our review mainly discussed the research progress in this field

LSTM Pose Machines

We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Enhanced Quadratic Video Interpolation

With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (e.g. acceleration) and successfully models the estimation of interpolated flow. However, its produced intermediate frames still contain some unsatisfactory ghosting, artifacts and inaccurate motion, especially when large and complex motion occurs. In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model. In particular, we adopt a rectified quadratic flow prediction (RQFP) formulation with least squares method to estimate the motion more accurately. Complementary with image pixel-level blending, we introduce a residual contextual synthesis network (RCSN) to employ contextual information in high-dimensional feature space, which could help the model handle more complicated scenes and motion patterns. Moreover, to further boost the performance, we devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process. The proposed EQVI model won the first place in the AIM2020 Video Temporal Super-Resolution Challenge.Comment: Winning solution of AIM2020 VTSR Challenge (in conjunction with ECCV 2020

Research Progress in Finerenone in Cardiovascular Diseases

Mineralocorticoid receptor antagonists (MRA) have significant therapeutic effects on heart failure, hypertension, chronic kidney disease and primary aldosteronism. However, steroid MRA can cause hyperkalemia, deterioration of renal insufficiency, menstrual disorder and male breast development, and consequently has found limited clinical applications. In recent years, basic and clinical studies have confirmed that finerenone is a new non-steroidal MRA with high receptor affinity and selectivity, which can decrease adverse effects such as hyperkalemia and exert powerful cardioprotective effects. Herein, the structure, function, pharmacological mechanism and adverse effects of finerenone are summarized, and its cardiovascular protective effects and clinical applications are described in detail, to aid in understanding of the roles of finerenone in treating cardiovascular diseases and to explore future directions

U-shaped relationship between managerial herd behavior and corporate financialization with the moderating effect of corporate governance: evidence from China

Based on behavioral finance theory, we discuss the influence of managers’ herd behavior on corporate financialization from the perspective of managers’ behavioral preferences. Empirical testing was conducted using data from nonfinancial listed firms on the Shanghai and Shenzhen  A-shares from 2007 to 2021 and a U-shaped relationship was found between managerial herd behavior and corporate financialization. When managerial herd behavior is within an appropriate range, the increase in managerial herd behavior has a negative influence on corporate financialization. In contrast, excessive managerial herd behavior leads to excessive corporate financialization. Additionally, corporate governance has a weakening effect on this relationship. Heterogeneity analyses indicate significant disparities in the effect of managerial herd behavior on corporate financialization among enterprises with diverse ownership structures. Finally, corporate financialization and innovation investments have an inverted U-shaped relationship, and their relationship is moderated positively by management herd behavior. Our results have strong practical significance for fostering the balanced growth of the financial sector and the real economy. First published online 05 January 202

Application of emerging technologies in ischemic stroke: from clinical study to basic research

Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke