Differential expressions of TGF-β1, HIF-1, VEGF, α-SMA and E-cadherin in renal tissues of a neonatal rat model of hydronephrosis

Purpose: To investigate the differential expressions of transforming growth factor-β1 (TGF-β1), hypoxia inductive factor-1 (HIF-1), vascular endothelial growth factor (VEGF), α-smooth muscle protein (α-SMA) and E-cadherin in renal tissues of neonatal rat model of hydronephrosis.Methods: The neonatal rats (90) were randomly divided into sham group and model group. The rats in the model group were further divided into two subgroups: week 1 and week 12 after relief of obstruction, with 30 rats in each group. Six rats were taken from each group for the determination of renal histopathological changes. Levels of TGF-β1, HIF-1, VEGF, α-SMA and E-cadherin in renal tissues were compared for different pathological grades and at different time points of obstruction relief.Results: With increase in Elder grade, the concentrations of TGF-β1, HIF-1, VEGF and α-SMA in renal tissues of hydronephrosis neonatal rats were gradually increased, while the expression level of Ecadherin gradually decreased (p < 0.05). However, the concentrations of TGF-β1, HIF-1, VEGF and α-SMA in renal tissues were significantly reduced, while the expression level of E-adherin was upregulated with time after relief of obstruction (p < 0.05).Conclusion: These findings are of great significance in determining the degree of kidney injury and recovery, and for the development of drugs for the treatment of renal injury

Fast and Unbiased Estimation of Volume Under Ordered Three-Class ROC Surface (VUS) With Continuous or Discrete Measurements

Receiver Operating Characteristic (ROC) surfaces have been studied in the literature essentially during the last decade and are considered as a natural generalization of ROC curves in three-class problems. The volume under the surface (VUS) is useful for evaluating the performance of a trichotomous diagnostic system or a three-class classifier's overall accuracy when the possible disease condition or sample belongs to one of three ordered categories. In the areas of medical studies and machine learning, the VUS of a new statistical model is typically estimated through a sample of ordinal and continuous measurements obtained by some suitable specimens. However, discrete scales of the prediction are also frequently encountered in practice. To deal with such scenario, in this paper, we proposed a unified and efficient algorithm of linearithmic order, based on dynamic programming, for unbiased estimation of the mean and variance of VUS with unidimensional samples drawn from continuous or non-continuous distributions. Monte Carlo simulations verify our theoretical findings and developed algorithms

Performance evaluation of a tidal current turbine with bidirectional symmetrical foils

As one might expect, tidal currents in terms of ebb and flood tides are approximately bidirectional. A Horizontal Axial Tidal Turbine (HATT) with unidirectional foils has to be able to face the current directions in order to maximize current energy harvesting. There are two regular solutions to keep a HATT always facing the direction of the flow, which are transferred from wind turbine applications. One is to yaw the turbine around the supporting structure with a yaw mechanism. The other is to reverse the blade pitch angle through 180° with a pitch-adjusting mechanism. The above solutions are not cost-effective in marine applications due to the harsh marine environment and high cost of installation and maintenance. In order to avoid the above disadvantages, a turbine with bidirectional foils is presented in this paper. A bare turbine with bidirectional foils is characterized in that it has nearly the same energy conversion capability in both tidal current directions without using the yaw or pitch mechanism. Considering the working conditions of the bidirectional turbine in which the turbine is installed on a mono-pile, the effect of the mono-pile on the turbine’s performance is evaluated in this paper, especially when the turbine is downstream of the mono-pile. The paper was focused on the evaluation of the hydrodynamic performance of the bidirectional turbine. The hydrodynamic performance of the bare bidirectional turbine without any supporting structure was evaluated based on a steady-state computational fluid dynamics (CFD) model and model tests. Performance comparison has been made between the turbine with bidirectional foils and the turbine with NACA foils. The effect of the mono-pile on the performance of the bidirectional turbine was studied by using the steady-state and the transient CFD model. The steady-state CFD model was used to evaluate the effect of the mono-pile clearance, which is the distance between the mono-pile and the turbine on the performance of the turbine. The transient CFD model was used to determine the time-dependent characteristics of the turbine, such as time-dependent power and drag coefficients. The results show that the bare bidirectional turbine has nearly the same energy conversion capability in both tidal current directions. The performance of the bidirectional turbine is inferior to the turbine with NACA foils. At the designed tip speed ratio, the power coefficient of the turbine with NACA foils is 0.4498, which increases by 1.6% compared to the 0.4338 of the bidirectional turbine. The turbine’s performance decreases due to the introduction of the mono-pile, and the closer the turbine is to the mono-pile, the greater effect on the turbine’s performance the mono-pile has. At the designed clearance of 1.5 DS, the presence of a mono-pile decreases the peak Cp value by 1.82% and 3.17% to a value of 0.4156 and 0.4004 for the turbine located in the mono-pile upstream and downstream, respectively. The mono-pile can result in the fluctuation of the turbine’s performance. This fluctuation will detrimentally harm the life of the turbine as it will lead to increased wear and fatigue issues

Semiparametric efficient estimation of genetic relatedness with machine learning methods

In this paper, we propose semiparametric efficient estimators of genetic relatedness between two traits in a model-free framework. Most existing methods require specifying certain parametric models involving the traits and genetic variants. However, the bias due to model misspecification may yield misleading statistical results. Moreover, the semiparametric efficient bounds for estimators of genetic relatedness are still lacking. In this paper, we develop semiparametric efficient estimators with machine learning methods and construct valid confidence intervals for two important measures of genetic relatedness: genetic covariance and genetic correlation, allowing both continuous and discrete responses. Based on the derived efficient influence functions of genetic relatedness, we propose a consistent estimator of the genetic covariance as long as one of genetic values is consistently estimated. The data of two traits may be collected from the same group or different groups of individuals. Various numerical studies are performed to illustrate our introduced procedures. We also apply proposed procedures to analyze Carworth Farms White mice genome-wide association study data.Comment: 46pages,9 tables, 1 figur

A scheme on indoor tracking of ship dynamic positioning based on distributed multi-sensor data fusion

Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference global positioning system (DGPS) and ultrasonic sensors. Other important factors, including the indoor temperature, position and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications

Blind separation and localization of dipole sources of MEG

We present a new approach to MEG inverse problem by modeling it into a standard blind source separation problem. In our approach, dipole sources and gain matrix are estimated without any knowledge about the head geometry and conductivity. Given the head model, we can compute dipole locations further. Our matrix pencil method developed before is suitable for this task and is applied in the simulation. Simulation results are presented.published_or_final_versio

LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLAD

Large-scale point cloud semantic segmentation is an important task in 3D computer vision, which is widely applied in autonomous driving, robotics, and virtual reality. Current large-scale point cloud semantic segmentation methods usually use down-sampling operations to improve computation efficiency and acquire point clouds with multi-resolution. However, this may cause the problem of missing local information. Meanwhile, it is difficult for networks to capture global information in large-scale distributed contexts. To capture local and global information effectively, we propose an end-to-end deep neural network called LACV-Net for large-scale point cloud semantic segmentation. The proposed network contains three main components: 1) a local adaptive feature augmentation module (LAFA) to adaptively learn the similarity of centroids and neighboring points to augment the local context; 2) a comprehensive VLAD module (C-VLAD) that fuses local features with multi-layer, multi-scale, and multi-resolution to represent a comprehensive global description vector; and 3) an aggregation loss function to effectively optimize the segmentation boundaries by constraining the adaptive weight from the LAFA module. Compared to state-of-the-art networks on several large-scale benchmark datasets, including S3DIS, Toronto3D, and SensatUrban, we demonstrated the effectiveness of the proposed network

A Hybrid Wireless Image Transmission Scheme with Diffusion

We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves

Hydrodynamic characteristics of remora's symbiotic relationships

Symbiotic relationships have developed through natural evolution which can provide advantages to parties in terms of survival. For example, that of the remora fish attached to the body of a shark to compensate for their poor swimming ability. From the remora's perspective, this could be associated to an increased hydrodynamic efficiency in swimming and this needs to be investigated. To understand the remora's swimming strategy in the attachment state, a systematic study has been conducted using the commercial Computational Fluid Dynamics CFD software, STAR-CCM+ to analyse and compare the resistance characteristics of the remora in attached swimming conditions. Two fundamental questions are addressed: what is the effect of the developed boundary layer flow and the effect of the adverse pressure gradient on the remora's hydrodynamic characteristics? By researching the hydrodynamic characteristics of the remora on varying attachment locations, the remora's unique behaviours could be applied to autonomous underwater vehicles (AUVs), which currently cannot perform docking and recovery without asking the mother vehicle to come for a halt