Estimation of Confidence in the Dialogue based on Eye Gaze and Head Movement Information

In human-robot interaction, human mental states in dialogue have attracted attention to human-friendly robots that support educational use. Although estimating mental states using speech and visual information has been conducted, it is still challenging to estimate mental states more precisely in the educational scene. In this paper, we proposed a method to estimate human mental state based on participants’ eye gaze and head movement information. Estimated participants’ confidence levels in their answers to the miscellaneous knowledge question as a human mental state. The participants’ non-verbal information, such as eye gaze and head movements during dialog with a robot, were collected in our experiment using an eye-tracking device. Then we collect participants’ confidence levels and analyze the relationship between human mental state and non-verbal information. Furthermore, we also applied a machine learning technique to estimate participants’ confidence levels from extracted features of gaze and head movement information. As a result, the performance of a machine learning technique using gaze and head movements information achieved over 80 % accuracy in estimating confidence levels. Our research provides insight into developing a human-friendly robot considering human mental states in the dialogue

Purification and Characterization of a Novel Hypersensitive Response-Inducing Elicitor from Magnaporthe oryzae that Triggers Defense Response in Rice

<div><h3>Background</h3><p><em>Magnaporthe oryzae</em>, the rice blast fungus, might secrete certain proteins related to plant-fungal pathogen interactions.</p> <h3>Methodology/Principal Findings</h3><p>In this study, we report the purification, characterization, and gene cloning of a novel hypersensitive response-inducing protein elicitor (MoHrip1) secreted by <em>M. oryzae</em>. The protein fraction was purified and identified by de novo sequencing, and the sequence matched the genomic sequence of a putative protein from <em>M. oryzae</em> strain 70-15 (GenBank accession No. XP_366602.1). The elicitor-encoding gene <em>mohrip1</em> was isolated; it consisted of a 429 bp cDNA, which encodes a polypeptide of 142 amino acids with a molecular weight of 14.322 kDa and a pI of 4.53. The deduced protein, MoHrip1, was expressed in <em>E. coli</em>. And the expression protein collected from bacterium also forms necrotic lesions in tobacco. MoHrip1 could induce the early events of the defense response, including hydrogen peroxide production, callose deposition, and alkalization of the extracellular medium, in tobacco. Moreover, MoHrip1-treated rice seedlings possessed significantly enhanced systemic resistance to <em>M. oryzae</em> compared to the control seedlings. The real-time PCR results indicated that the expression of some pathogenesis-related genes and genes involved in signal transduction could also be induced by MoHrip1.</p> <h3>Conclusion/Significance</h3><p>The results demonstrate that MoHrip1 triggers defense responses in rice and could be used for controlling rice blast disease.</p> </div

Analysis and Design of a Compound-Structure Permanent-Magnet Motor for Hybrid Electric Vehicles

On the basis of the excellent driving force demand of hybrid electric vehicles (HEVs), this paper studies the torque property of the compound-structure permanent-magnet motor (CSPM motor) used for HEVs, which is influenced by magnetic field oversaturation and variable nonlinear parameters. Firstly, the system configuration of HEVs based on CSPM motor and its working mode are introduced. Next, the state equation of CSPM motor in three-phase stationary coordinate system is proposed in order to investigate its torque performance; then, the factors affecting the output torque are gained. Finite element method (FEM)-based electromagnetic parameters analysis and design is carried out, to raise the output torque and reduce the torque ripple of CSPM motor. Besides, optimized design parameters are used to establish the FEM model, and the simulation results of electromagnetic performances for the CSPM motor before and after optimization are given to verify the rationality of optimization

Coarse-to-fine pseudo supervision guided meta-task optimization for few-shot object classification

Abstract Few-Shot Learning (FSL) is a challenging and practical learning pattern, aiming to solve a target task which has only a few labeled examples. Currently, the field of FSL has made great progress, but largely in the supervised setting, where a large auxiliary labeled dataset is required for offline training. However, the unsupervised FSL (UFSL) problem where the auxiliary dataset is fully unlabeled has been seldom investigated despite of its significant value. This paper focuses on the more general and challenging UFSL problem and presents a novel method named Coarse-to-Fine Pseudo Supervision-guided Meta-Learning (C2FPS-ML) for unsupervised few-shot object classification. It first obtains prior knowledge from an unlabeled auxiliary dataset during unsupervised meta-training, and then use the prior knowledge to assist the downstream few-shot classification task. Coarse-to-Fine Pseudo Supervisions in C2FPS-ML aim to optimize meta-task sampling process in unsupervised meta-training stage which is one of the dominant factors for improving the performance of meta-learning based FSL algorithms. Human can learn new concepts progressively or hierarchically following the coarse-to-fine manners. By simulating this human’s behaviour, we develop two versions of C2FPS-ML for two different scenarios: one is natural object dataset and another one is other kinds of dataset (e.g., handwritten character dataset). For natural object dataset scenario, we propose to exploit the potential hierarchical semantics of the unlabeled auxiliary dataset to build a tree-like structure of visual concepts. For another scenario, progressive pseudo supervision is obtained by forming clusters in different similarity aspects and is represented by a pyramid-like structure. The obtained structure is applied as the supervision to construct meta-tasks in meta-training stage, and prior knowledge from the unlabeled auxiliary dataset is learned from the coarse-grained level to the fine-grained level. The proposed method sets the new state of the art on the gold-standard miniImageNet and achieves remarkable results on Omniglot while simultaneously increases efficiency

An Asparagine-Rich Protein Nbnrp1 Modulate Verticillium dahliae Protein PevD1-Induced Cell Death and Disease Resistance in Nicotiana benthamiana

PevD1 is a fungal protein secreted by Verticillium dahliae. Our previous researches showed that this protein could induce hypersensitive responses-like necrosis and systemic acquired resistance (SAR) in cotton and tobacco. To understand immune activation mechanisms whereby PevD1 elicits defense response, the yeast two-hybrid (Y2H) assay was performed to explore interacting protein of PevD1 in Arabidopsis thaliana, and a partner AtNRP (At5g42050) was identified. Here, AtNRP homolog in Nicotiana benthamiana was identified and designated as Nbnrp1. The Nbnrp1 could interact with PevD1 via Y2H and bimolecular fluorescence complementation (BiFC) analyses. Moreover, truncated protein binding assays demonstrated that the C-terminal 132 amino acid (development and cell death, DCD domain) of Nbnrp1 is required for PevD1-Nbnrp1 interaction. To further investigate the roles of Nbnrp1 in PevD1-induced defense response, Nbnrp1-overexpressing and Nbnrp1-silence transgenic plants were generated. The overexpression of Nbnrp1 conferred enhancement of PevD1-induced necrosis activity and disease resistance against tobacco mosaic virus (TMV), bacterial pathogen Pseudomonas syringae pv. tabaci and fungal pathogen V. dahliae. By contrast, Nbnrp1-silence lines displayed attenuated defense response compared with the wild-type. It is the first report that an asparagine-rich protein Nbnrp1 positively regulated V. dahliae secretory protein PevD1-induced cell death response and disease resistance in N. benthamiana

Performance Study and Multi-Index Synergistic Effect Analysis of Phosphogypsum-Based Composite Cementitious Material

The application of phosphogypsum in building materials can consume waste phosphogypsum and reduce ecological pressure. In this study, building phosphogypsum was used as the base material, and fly ash, lime, cement, and other materials were added to explore the performance of phosphogypsum-based cementitious composite building materials via orthogonal experimental method. Variance analysis and multiple regression analysis were used to summarize the performance variation of these phosphogypsum-based composite cementitious materials. This work demonstrates that the building phosphogypsum content and the water-cement mass ratio are significant factors affecting the thermal conductivity and mechanical properties of these materials scanning electron microscopy (SEM) analysis showed that the mechanical properties and thermal insulation properties of the prepared phosphogypsum-based composite cementitious materials were good in the C-S-H gel system and ettringite formation uniform specimens. Regression analysis showed a significant relationship between the building phosphogypsum content, fly ash content in the supplementary cementitious material, lime content, water-cement mass ratio, compressive strength, and thermal conductivity. The compressive strength and the thermal conductivity were analyzed by the index membership degree. The comprehensive performance of the phosphogypsum-based composite cementitious materials was evaluated, and basic theoretical research into the use of the phosphogypsum-based composite cementitious materials in a building non-load-bearing wall was carried out

Informative class-conditioned feature alignment for unsupervised domain adaptation

Abstract The goal of unsupervised domain adaptation is to learn a task classifier that performs well for the unlabeled target domain by borrowing rich knowledge from a well-labeled source domain. Although remarkable breakthroughs have been achieved in learning transferable representation across domains, two bottlenecks remain to be further explored. First, many existing approaches focus primarily on the adaptation of the entire image, ignoring the limitation that not all features are transferable and informative for the object classification task. Second, the features of the two domains are typically aligned without considering the class labels; this can lead the resulting representations to be domain-invariant but non-discriminative to the category. To overcome the two issues, we present a novel Informative Class-Conditioned Feature Alignment (IC2FA) approach for UDA, which utilizes a twofold method: informative feature disentanglement and class-conditioned feature alignment, designed to address the above two challenges, respectively. More specifically, to surmount the first drawback, we cooperatively disentangle the two domains to obtain informative transferable features; here, Variational Information Bottleneck (VIB) is employed to encourage the learning of task-related semantic representations and suppress task-unrelated information. With regard to the second bottleneck, we optimize a new metric, termed Conditional Sliced Wasserstein Distance (CSWD), which explicitly estimates the intra-class discrepancy and the inter-class margin. The intra-class and inter-class CSWDs are minimized and maximized, respectively, to yield the domain-invariant discriminative features. IC2FA equips class-conditioned feature alignment with informative feature disentanglement and causes the two procedures to work cooperatively, which facilitates informative discriminative features adaptation. Extensive experimental results on three domain adaptation datasets confirm the superiority of IC2FA

Brucella Omp25 Upregulates miR-155, miR-21-5p, and miR-23b to Inhibit Interleukin-12 Production via Modulation of Programmed Death-1 Signaling in Human Monocyte/Macrophages

Brucella spp. infection results in compromised Type1 (Th1) cellular immune response. Several reports have described an immunomodulatory function for Brucella major outer membrane protein Omp25. However, the mechanism by which Omp25 modulates macrophage dysfunction has not been defined. Herein, we reported that Omp25-deficient mutant of Brucella suis exhibited an enhanced ability to induce interleukin (IL)-12 whereas ectopic expression of Omp25 protein inhibited TLR agonists-induced IL-12 p70 production through suppression of both IL-12 p40 and p35 subunit expression in THP-1 cells. In addition, Omp25 significantly upregulated miR-155, -23b and -21-5p, as well as the immunomodulator molecule programmed death-1 (PD-1) in monocyte/macrophages. The upregulation of miR-155 and -23b correlated temporally with decreased TAB2 levels, IκB phosphorylation and IL-12 p40 levels by targeting TAB2 and il12B 3′ untranslated region (UTR), respectively, while miR-21-5p increase directly led to the reduction of lipopolysaccharide (LPS)/R848-induced IL-12 p35 protein by targeting il12A 3′UTR. Consistent with this finding, reduction of miR-155 and -23b attenuated the inhibitory effects of Omp25 on LPS/R848-induced IL-12 p40 expression at both transcriptional and posttranscriptional levels, while reduction of miR-21-5p attenuated the inhibitory effects of Omp25 on LPS/R848-induced IL-12 p35 expression at the posttranscriptional level, together significantly enhanced IL-12 p70 production upon LPS/R848 stimulation. We also found that blocking PD-1 signaling decreased the expression of miR-155, -23b and -21-5p induced by Omp25 and enhanced IL-12 production in monocyte/macrophages. Altogether, these data demonstrate that Brucella Omp25 induces miR-155, -23b and -21-5p to negatively regulate IL-12 production at both transcriptional and posttranscriptional levels via regulation of PD-1 signaling, which provides an entirely new mechanism underlying monocyte/macrophages dysfunction during Brucella spp. infection

Ultrasensitive Detection of Porcine Epidemic Diarrhea Virus from Fecal Samples Using Functionalized Nanoparticles.

Porcine epidemic diarrhea virus (PEDV) is the main causative agent of porcine diarrhea, which has resulted in devastating damage to swine industry and become a perplexed global problem. PEDV infection causes lesions and clinical symptoms, and infected pigs often succumb to severe dehydration. If there is not a timely and effective method to control its infection, PEDV will spread rapidly across the whole swine farm. Therefore, preclinical identification of PEDV is of great significance for preventing the outbreak and spread of this disease. In this study, a functionalized nanoparticles-based PCR method (UNDP-PCR) specific for PEDV was developed through systematic optimization of functionalized magnetic beads and gold nanoparticles which were further used to specifically enrich viral RNA from the lysate of PEDV stool samples, forming a MMPs-RNA-AuNPs complex. Then, oligonucleotides specific for PEDV coated on AuNPs were eluted from the complex and were further amplified and characterized by PCR. The detection limitation of the established UNDP-PCR method for PEDV was 25 copies in per gram PEDV stool samples, which is 400-fold more sensitive than conventional RT-PCR for stool samples. The UNDP-PCR for PEDV exhibited reliable reproducibility and high specificity, no cross-reaction was observed with other porcine viruses. In 153 preclinical fecal samples, the positive detection rate of UNDP-PCR specific for PEDV (30.72%) was much higher than that of conventional RT-PCR (5.88%) and SYBR Green real-time RT-PCR. In a word, this study provided a RNA extraction and transcription free, rapid and economical method for preclinical PEDV infection, which showed higher sensitivity, specificity and reproducibility, and exhibited application potency for evaluating viral loads of preclinical samples