Multidirectional flow and LRG1 in endothelial cells: potential atheroprotective role

Haemodynamic wall shear stress affects the function of arterial endothelial cells (ECs). Low magnitude, oscillatory and multidirectional shear have all been postulated to stimulate endothelial activation, whereas high magnitude and uniaxial shear are thought to promote endothelial homeostasis. The effects of shear interact with the effects of pro-inflammatory cytokines; they are mediated by complex signal transduction pathways and together may account for the patchy nature of atherosclerosis. The swirling-well system was used to investigate shear-induced endothelial activation. The method involves culturing ECs in standard multi-well plates on the platform of an orbital shaker to induce complex flow profiles. Computational fluid dynamic (CFD) simulation revealed that the swirling medium produces low magnitude multidirectional flow (LMMF) in the centre of the well and high magnitude uniaxial flow (HMUF) at the edge. A disadvantage of the method is that sheared ECs may release soluble mediators that become mixed in the swirling medium, and corrupt of apparent relations between shear and ECs properties. This drawback was resolved using a novel coating method that restricts cell growth to specific regions of the well. This modification permitted the demonstration that ECs do indeed release anti-inflammatory soluble mediators under HMMF. Leucine-rich α-2-glycoprotein 1 (LRG1) is a pro-angiogenic protein intimately linked with inflammation. Its role in endothelial activation was investigated. ECs activated by TNF-α treatment and LMMF showed higher LRG1 expression. The protein suppressed endothelial NF-κB signalling, EC adhesion molecules expression, and monocyte recruitment. Mechanistically, LRG1 caused TNFR1 shedding via the ALK5-SMAD2 pathway and the activation of ADAM10. LRG1 was highly expressed in ECs of stenotic arteries; it was found at high concentrations in the serum of critical limb ischemia patients and correlated with sTNFR1 concentrations. These data are consistent with a novel role for LRG1 in endothelial activation and a significant influence on atherogenesis.Open Acces

Precise rates in the law of logarithm for i.i.d. random variables

AbstractLet {X, Xn; n ≥ 1} be a sequence of i.i.d. random variables. Set Sn = X1 + X2 + … + Xn and Mn = maxk≤n |Sk|, n ≥ 1. By using the strong approximation method, we obtain that for any −1 < b ≤ 1, lim⁡ε↘0ε2b+2∑n=1∞(log⁡n)bnP(Mn≥εσnlog⁡n)=2E|N|(2b+2)b+1∑k=0∞(−1)k(2k+1)2b+2 if and only if Ex = 0 and Ex2 < ∞, which strengthen and extend the result of Gut and Spǎtaru [1], where N is the standard normal random variable. Furthermore, L2 convergence and a.s. convergence are also discussed

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. In this paper, we propose to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end, we introduce a novel approach, namely Anomaly Heterogeneity Learning (AHL), that simulates a diverse set of heterogeneous (seen and unseen) anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model. Further, AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art (SOTA) OSAD models in detecting both seen and unseen anomalies, achieving new SOTA performance on a large set of datasets, and 2) effectively generalize to unseen anomalies in new target domains.Comment: 18 pages, 5 figure

The Design and Realization for a Multiplex Time Sequence Controller

AbstractIn order to meet the demand of activating several devices at different moments, a multiplex time sequence controller is developed in this paper. When the controller receives the trigger signal for starting, the time sequential control circuit module, consisting of the microcontroller and the FPGA, it can generate a delay trigging signal according to the preset delay value, which will activate the testing device after being driven. The delay value is import by the computer or the dial on the panel. The real firing results show that the time sequence controller can realize the delay of 20-channel independently, one of which is able to be adjustable within 0∼10s, the maximum amplitude of output delay trigging signal is 12V, the width of the signal is 5ms and the error of the delay time is less than 2colons. The multiplex time sequence controller can satisfy the requirements of technical specifications of testing system in conventional shooting range, and it can meet the demand of activating several testing devices operating at different moments

Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting

Generation of molecules with desired chemical and biological properties such as high drug-likeness, high binding affinity to target proteins, is critical for drug discovery. In this paper, we propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our model assumes an energy-based model (EBM) in the latent space. Conditional on the latent vector, the molecule and its properties are modeled by a molecule generation model and a property regression model respectively. To search for molecules with desired properties, we propose a sampling with gradual distribution shifting (SGDS) algorithm, so that after learning the model initially on the training data of existing molecules and their properties, the proposed algorithm gradually shifts the model distribution towards the region supported by molecules with desired values of properties. Our experiments show that our method achieves very strong performances on various molecule design tasks

3D Stretchable Arch Ribbon Array Fabricated via Grayscale Lithography.

Microstructures with flexible and stretchable properties display tremendous potential applications including integrated systems, wearable devices and bio-sensor electronics. Hence, it is essential to develop an effective method for fabricating curvilinear and flexural microstructures. Despite significant advances in 2D stretchable inorganic structures, large scale fabrication of unique 3D microstructures at a low cost remains challenging. Here, we demonstrate that the 3D microstructures can be achieved by grayscale lithography to produce a curved photoresist (PR) template, where the PR acts as sacrificial layer to form wavelike arched structures. Using plasma-enhanced chemical vapor deposition (PECVD) process at low temperature, the curved PR topography can be transferred to the silicon dioxide layer. Subsequently, plasma etching can be used to fabricate the arched stripe arrays. The wavelike silicon dioxide arch microstructure exhibits Young modulus and fracture strength of 52 GPa and 300 MPa, respectively. The model of stress distribution inside the microstructure was also established, which compares well with the experimental results. This approach of fabricating a wavelike arch structure may become a promising route to produce a variety of stretchable sensors, actuators and circuits, thus providing unique opportunities for emerging classes of robust 3D integrated systems