Anti-Inflammatory Activity of Soluble Epoxide Hydrolase Inhibitors Based on Selenoureas Bearing an Adamantane Moiety

The inhibitory potency of the series of inhibitors of the soluble epoxide hydrolase (sEH) based on the selenourea moiety and containing adamantane and aromatic lipophilic groups ranges from 34.3 nM to 1.2 μM. The most active compound 5d possesses aliphatic spacers between the selenourea group and lipophilic fragments. Synthesized compounds were tested against the LPS-induced activation of primary murine macrophages. The most prominent anti-inflammatory activity, defined as a suppression of nitric oxide synthesis by LPS-stimulated macrophages, was demonstrated for compounds 4a and 5b. The cytotoxicity of the obtained substances was studied using human neuroblastoma and fibroblast cell cultures. Using these cell assays, the cytotoxic concentration for 4a was 4.7–18.4 times higher than the effective anti-inflammatory concentration. The genotoxicity and the ability to induce oxidative stress was studied using bacterial lux-biosensors. Substance 4a does not exhibit genotoxic properties, but it can cause oxidative stress at concentrations above 50 µM. Put together, the data showed the efficacy and safety of compound 4a.     

Introduction to the Special Issue on Meta-Learning

Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.     

Mice Lacking Gpr179 with Complete Congenital Stationary Night Blindness Are a Good Model for Myopia

Mutations in GPR179 are one of the most common causes of autosomal recessive complete congenital stationary night blindness (cCSNB). This retinal disease is characterized in patients by impaired dim and night vision, associated with other ocular symptoms, including high myopia. cCSNB is caused by a complete loss of signal transmission from photoreceptors to ON-bipolar cells. In this study, we hypothesized that the lack of Gpr179 and the subsequent impaired ON-pathway could lead to myopic features in a mouse model of cCSNB. Using ultra performance liquid chromatography, we show that adult Gpr179^−/− mice have a significant decrease in both retinal dopamine and 3,4-dihydroxyphenylacetic acid, compared to Gpr179^+/+ mice. This alteration of the dopaminergic system is thought to be correlated with an increased susceptibility to lens-induced myopia but does not affect the natural refractive development. Altogether, our data added a novel myopia model, which could be used to identify therapeutic interventions.     

A Novel Neurofilament Light Chain ELISA Validated in Patients with Alzheimer’s Disease,Frontotemporal Dementia,and Subjective Cognitive Decline,and the Evaluation of Candidate Proteins for Immunoassay Calibration

Neurofilament light chain (Nf-L) is a well-known biomarker for axonal damage; however, the corresponding circulating Nf-L analyte in cerebrospinal fluid (CSF) is poorly characterized. We therefore isolated new monoclonal antibodies against synthetic peptides, and these monoclonals were characterized for their specificity on brain-specific intermediate filament proteins. Two highly specific antibodies, ADx206 and ADx209, were analytically validated for CSF applications according to well-established criteria. Interestingly, using three different sources of purified Nf-L proteins, a significant impact on interpolated concentrations was observed. With a lower limit of analytical sensitivity of 100 pg/mL using bovine Nf-L as the calibrator, we were able to quantify the Nf-L analyte in each sample, and these Nf-L concentrations were highly correlated to the Uman diagnostics assay (Spearman rho = 0.97, p < 0.001). In the clinical diagnostic groups, the new Nf-L ELISA could discriminate patients with Alzheimer’s disease (AD, n = 20) from those with frontotemporal lobe dementia (FTD, n = 20) and control samples with subjective cognitive decline (SCD, n = 20). Henceforth, this novel Nf-L ELISA with well-defined specificity and epitopes can be used to enhance our understanding of harmonizing the use of Nf-L as a clinically relevant marker for neurodegeneration in CSF.     

三相异步电机新模型及其仿真与实验

介绍了三相异步电机的一个新模型。新模型由原始的相轴线模型经过变换得到。该模型可应用在电机定子三相参数不对称的状态。计算机仿真和电机模拟实验表明：在派克方程适用范围，新模型与PARK模型的响应完全一致：当电机定子三相参数不对称时，此时已超出PARK模型的适用范围，新模型的仿真计算结果与电机的实验数据相当吻合。新模型具有比PARK模型更广的适用范围。该模型在三相异步电机的监测及控制方面有广泛的应用前景。     

A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study

Magnetic Resonance Materials in Physics, Biology and Medicine - Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and...     

Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone

Drone racing is becoming a popular e‐sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a novel sensor fusion method called visual model‐predictive localization (VML). Within a small time window, VML approximates the error between the model prediction position and the visual measurements as a linear function. Once the parameters of the function are estimated by the RANSAC algorithm, this error model can be used to compensate the prediction in the future. In this way, outliers can be handled efficiently and the vision delay can also be compensated efficiently. Theoretical analysis and simulation results show the clear advantage compared with Kalman filtering when dealing with the occasional large outliers and vision delays that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named “Trashcan,” which was equipped with a Jevois smart camera for a total of 72 g. An average speed of 2 m/s is achieved while the maximum speed is 2.6 m/s. To the best of our knowledge, this flying platform is currently the smallest autonomous racing drone in the world, while still being one of the fastest autonomous racing drones.     

A performance evaluation of scalable live video streaming with nano data centers

To improve the efficiency and the quality of a service, a network operator may consider deploying a peer-to-peer architecture among controlled peers, also called here nano data centers, which contrast with the churn and resource heterogeneity of peers in uncontrolled environments. In this paper, we consider a prevalent peer-to-peer application: live video streaming. We demonstrate how nano data centers can take advantage of the self-scaling property of a peer-to-peer architecture, while significantly improving the quality of a live video streaming service, allowing smaller delays and fast channel switching. We introduce the branching architecture for nano datacenters (BAND), where a user can “pull” content from a channel of interest, or content could be “pushed” to it for relaying to other interested users. We prove that there exists an optimal trade-off point between minimizing the number of push, or the number of relaying nodes, and maintaining a robust topology as the number of channels and users get large, which allows scalability. We analyze the performance of content dissemination as users switch between channels, creating migration of nodes in the tree, while flow control insures continuity of data transmission. We prove that this p2p architecture guarantees a throughput independently of the size of the group. Analysis and evaluation of the model demonstrate that pushing content to a small number of relay nodes can have significant performance gains in throughput, start-up time, playback lags and channel switching delays.     

Siamese multi-layer perceptrons for dimensionality reduction and face identification

This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training with side information achieves comparable classification performance with the classical MLP training on fully labeled data. Besides, while the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence we also apply the siamese MLP for visualization of the dimensionality reduction to the 2-d and 3-d spaces.     

Fuzzy fault tolerant predictive control for a diesel engine air path

This paper proposes a Fuzzy Fault Tolerant Predictive Control (FFTPC) with integral action method for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. The proposed control strategy is based on a combination between Parallel Distributed Compensation (PDC) control law and Model Predictive Control (MPC) where the T-S fuzzy aspect uses the Unmeasurable Premise Variables (UPV). A T-S fuzzy observer provides an L2 norm estimation of system state vector and faults. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. The validity of the proposed Fault Tolerant Control (FTC) strategy is illustrated through an application to a Diesel Engine Air Path (DEAP) system.