ABO genotype alters the gut microbiota by regulating GalNAc levels in pigs.

peer reviewedThe composition of the intestinal microbiome varies considerably between individuals and is correlated with health1. Understanding to what extend and how host genetics contributes to this variation is paramount yet has proven difficult as few associations have been replicated, particularly in humans2. We herein study the effect of host genotype on the composition of the intestinal microbiota in a large mosaic pig population. We show that, under conditions of exacerbated genetic diversity and environmental uniformity, microbiota composition and abundance of specific taxa are heritable. We map a quantitative trait locus affecting the abundance of Erysipelotrichaceae species and show that it is caused by a 2.3-Kb deletion in the N-acetyl-galactosaminyl-transferase gene underpinning the ABO blood group in humans. We show that this deletion is a ≥3.5 million years old trans-species polymorphism under balancing selection. We demonstrate that it decreases the concentrations of N-acetyl-galactosamine in the gut thereby reducing the abundance of Erysipelotrichaceae that can import and catabolize N-acetyl-galactosamine. Our results provide very strong evidence for an effect of host genotype on the abundance of specific bacteria in the intestine combined with insights in the molecular mechanisms that underpin this association. They pave the way towards identifying the same effect in human rural populations

A Dynamic Part-Attention Model for Person Re-Identification

Person re-identification (ReID) is gaining more attention due to its important applications in pedestrian tracking and security prevention. Recently developed part-based methods have proven beneficial for stronger and explicit feature descriptions, but how to find real significant parts and reduce miscorrelation between images to improve accuracy of ReID still leaves much room to improve. In this paper, we propose a dynamic part-attention (DPA) method based on masks, which aims to improve the use of variable attention parts. Particularly, a two-branch network with a dynamic loss function is designed to extract features of the global image and the parts of the body separately. With the comprehensive but targeting learning strategy, the proposed method can capture discriminative features based, but not depending on, masks, which guides the whole network to focus on body features more consciously and achieves more robust performance. Our method achieves rank-1 accuracy of 91.68% on public dataset Market1501, and experimental results on three public datasets indicate that the proposed method is effective and achieves favorable accuracy when compared with the state-of-the-art methods

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness

Electromagnetic Characteristics Analysis of a High-Temperature Superconducting Field-Modulation Double-Stator Machine with Stationary Seal

This paper has proposed a high temperature superconducting (HTS) field-modulation double-stator (HTS-FMDS) machine with a stationary seal for low-speed and direct-drive applications. The originality of this paper lies in the HTS field-excitation windings, which were fed with a direct current in order to offer strong field excitation, were placed in the inner stator, while the armature windings were installed in the outer stator so that the stationary seal of the cryogenic cooling system could be achieved. Moreover, a ferromagnetic ring was mounted on the top of each HTS coil to prevent the quench of the HTS wires resulting from the armature-reaction magnetic-field. By using finite-element analysis (FEA), the influence of the armature-reaction magnetic-field on the critical current and the electromagnetic properties were carried out so as to verify the validity of the proposed machine

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians

Optimizing the Growth of Silage Maize by Adjusting Planting Density and Nitrogen Application Rate Based on Farmers’ Conventional Planting Habits

Silage maize is cultivated due to its high nutritional value as a forage. China’s recent agricultural policy promotes the popularization and cultivation of silage maize. The production of silage maize is affected by planting density and nitrogen application. Based on investigating the planting habits of local farmers, we adjusted the planting density and nitrogen application rate to optimize the growth of silage maize. This study was conducted to investigate the effects of planting density (65,000 plant ha−1 (D1), 80,000 plant ha−1 (D2), and 95,000 plant ha−1 (D3)) and nitrogen rate (150 kg ha−1 (N1), 230 kg ha−1 (N2), and 310 kg ha−1 (N3)) on growth, yield, and quality of silage maize using a two-factor random block design. Planting density and nitrogen fertilizer significantly affected plant height, stem diameter, leaf area index, crude protein, neutral detergent fiber, acid detergent fiber, and starch of silage maize. In summary, the combination of a planting density of 80,000 plants ha−1 and a nitrogen application rate of 310 kg ha−1 produced a higher crude protein and starch yield and better palatability and quality; this result can aid silage maize growth

Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

Augmented Reality (AR) is crucial for immersive Human⁻Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry. Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems

A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model

In this paper, a novel MRGP-SS method is proposed to deal with the reliability analysis problems under multiple failure modes. First, a random moving quadrilateral grid sampling (RMQGS) method is proposed to improve the randomness and uniformity of initial samples. Second, an adaptive procedure, which combines the multiple response Gaussian process (MRGP) model and the novel active learning functions, is proposed to efficiently and accurately produce surrogate models for failure surfaces. In this regard, two novel learning functions are introduced to adapt to different iterative cycles, one is employed to correct the quality of samples, and the other is used to search for the samples closest to the limit state surface. Third, the subset simulation (SS) is integrated into the adaptive MRGP model to estimate the failure probability under multiple failure modes with fewer function calls and time consumption. Numerical and engineering case studies are finally provided to demonstrate the effectiveness of the proposed method

Research on Variable-Universe Fuzzy Control Technology of an Electro-Hydraulic Hitch System

To improve the quality and control accuracy of the farming tractor electro-hydraulic hitch system, a variable-universe fuzzy control algorithm is introduced herein based on force–position mixed adjustment. (1) Background: This research sought to improve the operation quality and control precision of the tractor electro-hydraulic suspension operation system by solving the slow response and low precision problems in the target value control of the system. (2) Methods: According to the characteristics of the operating system, the working principle is discussed. The variable-universe fuzzy controller and the control module were designed based on MC9S12XS128. At the same time, we used Matlab/Simulink to study the step response, and field tests were carried out based on the existing test conditions. (3) Results: In the response stage, the variable-universe fuzzy control only needs 5.85 s, and there is no overshoot problem; in the normal tillage stage, the maximum tillage depth difference is only 1.6 cm, and the traction force is 428 N, which is closer to the expected value. (4) Conclusions: The farming quality and efficiency of the operating system were improved. Additionally, the operating system can also provide technical support for intelligent agricultural machinery and the field management mode in the future