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Public metabolites such as vitamins play critical roles in maintaining the ecological functions of microbial community. However, the biochemical and physiological bases for fine-tuning of public metabolites in the microbiome remain poorly understood. Here, we examine the interactions between myxobacteria and Phytophthora sojae, an oomycete pathogen of soybean. We find that host plant and soil microbes complement P. sojae's auxotrophy for thiamine. Whereas, myxobacteria inhibits Phytophthora growth by a thiaminase I CcThi1 secreted into extracellular environment via outer membrane vesicles (OMVs). CcThi1 scavenges the required thiamine and thus arrests the thiamine sharing behavior of P. sojae from the supplier, which interferes with amino acid metabolism and expression of pathogenic effectors, probably leading to impairment of P. sojae growth and pathogenicity. Moreover, myxobacteria and CcThi1 are highly effective in regulating the thiamine levels in soil, which is correlated with the incidence of soybean Phytophthora root rot. Our findings unravel a novel ecological tactic employed by myxobacteria to maintain the interspecific equilibrium in soil microbial community. The plant pathogen Phytophthora sojae requires exogenous thiamine for growth. Here, Li et al. show that soil myxobacteria inhibit Phytophthora's growth by scavenging thiamine through the secretion a thiaminase via outer membrane vesicles. [ABSTRACT FROM AUTHOR]

To investigate the effect of the angiotensin converting enzyme 2 (ACE2) on AT1R expression, ERK1/2 and STAT3 protein phosphorylation in rat vascular smooth muscle cells (VSMCs) was studied. VSMCs were transfected with a lentiviral vector including the ACE2 gene and with siRNA to regulate the level of ACE2 in VSMCs. The levels of mRNA and proteins of ACE2, AT1R, ERK1/2, p-ERK1/2, STAT3, and p-STAT3 in VSMCs were examined using real-time PCR and western blot. The proliferation of VSMCs was observed by CCK-8 assay and BrdU measurement. Upregulation of ACE2 inhibited the growth of cells elicited by angiotensin II (Ang II). ACE2 significantly suppressed the level of the AT1 receptor (AT1R) protein induced by Ang II and phosphorylated the ERK1/2 and STAT3 proteins in the downstream signaling pathway. The transcriptional and translational levels of ACE2 were significantly lower in the si-ACE2 group than in the control group. The level of AT1R mRNA and protein, both with the phosphorylation expression of ERK1/2 and STAT3 protein in the si-ACE2 group and the Ang II group, were significantly enhanced than those in the control group. ACE2 significantly inhibited the growth of VSMCs. ACE2 inhibited the proliferation of VSMCs by suppressing AT1R and the downstream ERK1/2 and STAT3 signaling axes. Also, Ang II enhanced the level of AT1R and phosphorylated ERK1/2 and STAT3 by inhibiting the level of the ACE2 mRNA and protein. [ABSTRACT FROM AUTHOR]

Li-rich manganese-based materials are considered to be the mainstream cathode materials for next-generation lithium-ion batteries due to high discharge capacity and low cost, but poor cycle life and high temperature performance limit their development. Herein, LiZr2(PO4)3 (LZPO) is coated on the surface of spherical Li1.2Mn0.54Ni0.13Co0.13O2 (LMNCO) material by a simple wet chemical method. The LZPO layer not only has the function of traditional coating layer to inhibit the occurrence of side reactions between electrolyte and LMNCO surface but also promotes the formation of spinel phase in the layered structure, increases the content of lattice oxygen, and reduces the content of absorbed oxygen. Thus, LZPO coated LMNCO has a more stable layered structure during cycling compared pure LMNCO, which improves effectively its long life and high temperature performance. The capacity loss rate of LZPO coated LMNCO is only 16.2% and 11.9% after 350 cycles at 25 °C and 200 cycles at 50 °C, respectively. Moreover, the capacity retention rate of the full cell composed of LZPO coated LMNCO and graphite is 70.7% after 200 cycles at 1.0 C. The coating layer toward stable surface structure can provide an idea for the modification of cathode materials, especially for Li-rich manganese-based materials. [ABSTRACT FROM AUTHOR]

The development of social networking services (SNSs) revealed a surge in image sharing. The sharing mode of multi-page photo collage (MPC), which posts several image collages at a time, can often be observed on many social network platforms, which enables uploading images and arrangement in a logical order. This study focuses on the construction of MPC for an image collection and its formulation as an issue of joint optimization, which involves not only the arrangement in a single collage but also the arrangement among different collages. Novel balance-aware measurements, which merge graphic features and psychological achievements, are introduced. Non-dominated sorting genetic algorithm is adopted to optimize the MPC guided by the measurements. Experiments demonstrate that the proposed method can lead to diverse, visually pleasant, and logically clear MPC results, which are comparable to manually designed MPC results. [ABSTRACT FROM AUTHOR]

Transformers, the dominant architecture for natural language processing, have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance. Transformers are sequence-to-sequence models, which use a self-attention mechanism rather than the RNN sequential structure. Thus, such models can be trained in parallel and can represent global information. This study comprehensively surveys recent visual transformer works. We categorize them according to task scenario: backbone design, high-level vision, low-level vision and generation, and multimodal learning. Their key ideas are also analyzed. Differing from previous surveys, we mainly focus on visual transformer methods in low-level vision and generation. The latest works on backbone design are also reviewed in detail. For ease of understanding, we precisely describe the main contributions of the latest works in the form of tables. As well as giving quantitative comparisons, we also present image results for low-level vision and generation tasks. Computational costs and source code links for various important works are also given in this survey to assist further development. [ABSTRACT FROM AUTHOR]

Lithium-rich manganese-based cathode materials have become one of the most concerned cathode materials for high-energy lithium-ion batteries. In order to improve its electrochemical performance, Li1.2Mn0.54Ni0.13Co0.13O2 with different content LiNbO3 coatings was synthesized by mechanical ball milling. The morphology, microstructure, and electrochemical properties of the samples were investigated by X-ray diffraction, scanning electron microscope, transmission electron microscope, galvanostatic charge/discharge, electrochemical impedance spectroscopy, and cyclic voltammetry. The results show that LiNbO3 coating not only protects the cathode material from the corrosion of electrolyte and HF but also improves the migration rate of Li+ in the interface region. Notably, the 5 wt% LiNbO3-coated Li1.2Mn0.54Ni0.13Co0.13O2 exhibits capacity retention of 89.9% under 0.1 C after 100 cycles. Besides, it has a higher discharge capacity than Li1.2Mn0.54Ni0.13Co0.13O2 at different rates. LiNbO3 coating is an effective way to improve its cycle stability and rate performance of Li1.2Mn0.54Ni0.13Co0.13O2. [ABSTRACT FROM AUTHOR]

Object detection is widely used in object tracking; anchor-free object tracking provides an end-to-end single-object-tracking approach. In this study, we propose a new anchor-free network, the Siamese center-prediction network (SiamCPN). Given the presence of referenced object features in the initial frame, we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations. Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction, SiamCPN directly obtains all information required for tracking, greatly simplifying the model. A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net. The model can accurately predict object location, implement appropriate corrections, and regress the size of the target bounding box. Compared to other leading Siamese networks, SiamCPN is simpler, faster, and more efficient as it uses fewer hyperparameters. Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks, and is comparable to other excellent trackers on LaSOT, VOT2016, and OTB-100 while improving inference speed 1.5 to 2 times. [ABSTRACT FROM AUTHOR]

Image captioning aims to translate an image to a complete and natural sentence. It involves both computer vision and natural language processing. Though image captioning has achieved good results under the rapid development of deep neural networks, excessively pursuing the evaluation results of the captioning models makes the generated text description too conservative in practical applications. It is necessary to increase the diversity of the text description and account for prior knowledge such as the user's favorite vocabularies and writing styles. In this paper, we study the personalized image captioning which can generate sentences to describe the user's own story and feelings of life with the most preferred word expression. Moreover, we propose cross-domain personalized image captioning (CDPIC) to learn domain-invariant captioning models which can be applied on different social media platforms. The proposed method can flexibly model user interest by embedding the user ID as an interest vector. To the best of our knowledge, we propose the first cross-domain personalized image captioning approach by combining the user interest modeling and a simple and effective domain-invariant constraint. The effectiveness of the proposed method is verified on datasets from the Instagram and Lookbook platforms. [ABSTRACT FROM AUTHOR]

Selective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods. [ABSTRACT FROM AUTHOR]

Abnormal proliferation of pulmonary artery smooth muscle cells (PASMCs) plays a dominant role in the development of pulmonary arterial hypertension (PAH). Some studies and our previous work found that disturbance of fatty acid metabolism existed in PAH. However, the mechanistic link between fatty acid catabolism and cell proliferation remains elusive. Here, we identified an essential role and signal pathway for the key rate-limiting enzyme of mitochondrial fatty acid β-oxidation, carnitine palmitoyltransferase (CPT) 1, in regulating PASMC proliferation in PAH. We found that CPT1 was highly expressed in rat lungs and pulmonary arteries in monocrotaline-induced PAH, accompanied by decreased adenosine triphosphate (ATP) production and downregulation of the AMPK-p53-p21 pathway. Platelet-derived growth factor (PDGF)-BB upregulated the expression of CPT1 in a dose- and time-dependent manner. PASMC proliferation and ATP production induced by PDGF-BB were partly reversed by the CPT1 inhibitor etomoxir (ETO). The overexpression of CPT1 in PASMCs also promoted proliferation and ATP production and subsequently inhibited the phosphorylation of AMPK, p53, as well as p21 in PASMCs. Furthermore, AMPK was activated by ETO, which increased the expression of p53 and p21, and the proportion of cells in the cell cycle G2/M phase in response to PDGF-BB stimulation in PASMCs. Our work reveals a novel mechanism of CPT1 regulating PASMC proliferation in PAH, and regulation of CPT1 may be a potential target for therapeutic intervention in PAH. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel multi-modal max-margin supervised topic model (MMSTM) for social event analysis by jointly learning the representation together with the classifier in a unified framework. Compared with existing methods, the proposed MMSTM model has several advantages. (1) The proposed model can utilize the classifier as the regularization term of our model to jointly learn the parameters in the generative model and max-margin classifier, and use the Gibbs sampling to learn parameters of the representation model and max-margin classifier by minimizing the expected loss function. (2) The proposed model is able to not only effectively mine the multi-modal property by jointly learning the latent topic relevance among multiple modalities for social event representation, but also exploit the supervised information by considering a discriminative max-margin classifier for event classification to boost the classification performance. (3) In order to validate the effectiveness of the proposed model, we collect a large-scale real-world dataset for social event analysis, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMSTM. [ABSTRACT FROM AUTHOR]

Sinoatrial node fibrosis is involved in the pathogenesis of sinus sick syndrome (SSS). Transient receptor potential (TRP) subfamily M member 7 (TRPM7) is implicated in cardiac fibrosis. However, the mechanisms underlying the regulation of sinoatrial node (SAN) fibrosis in SSS by TRPM7 remain unknown. The aim of this study was to investigate the role of angiotensin II (Ang II)/TRPM7/Smad pathway in the SAN fibrosis in rats with SSS. The rat SSS model was established with sodium hydroxide pinpoint pressing permeation. Forty-eight rats were randomly divided into six groups: normal control (ctrl), sham operation (sham), postoperative 1-, 2-, 3-, and 4-week SSS, respectively. The tissue explant culture method was used to culture cardiac fibroblasts (CFs) from rat SAN tissues. TRPM7 siRNA or encoding plasmids were used to knock down or overexpress TRPM7. Collagen (Col) distribution in SAN and atria was assessed using PASM-Masson staining. Ang II, Col I, and Col III levels in serum and tissues or in CFs were determined by ELISA. TRPM7, smad2 and p-smad2 levels were evaluated by real-time PCR, and/or western blot and immunohistochemistry. SAN and atria in rats of the SSS groups had more fibers and higher levels of Ang II, Col I and III than the sham rats. Similar findings were obtained for TRPM7 and pSmad2 expression. In vitro, Ang II promoted CFs collagen synthesis in a dose-dependent manner, and potentiated TRPM7 and p-Smad2 expression. TRPM7 depletion inhibited Ang II-induced p-Smad2 expression and collagen synthesis in CFs, whereas increased TRPM7 expression did the opposite. SAN fibrosis is regulated by the Ang II/TRPM7/Smad pathway in SSS, indicating that TRPM7 is a potential target for SAN fibrosis therapy in SSS. [ABSTRACT FROM AUTHOR]

Alloy components are designed and transferred into weld metal via electrode covering to address the deterioration of the microstructure and mechanical properties of underwater wet welds. Emphasis is placed on studying the effects that the Mo, Ti and B contents have on the microstructure, tensile strength and low-temperature toughness of the underwater wet welding joint. The as-welded metal obtained at a water depth of 3 m is analyzed. The results indicate that the addition of Mo depresses the formation of coarse pro-eutectoid ferrite. However, a higher Mo content (0.609 wt.%) results in the formation of lath-like bainite and martensite, which are harmful to the plasticity and toughness of the weld. Acicular ferrite nucleation increases with the combined addition of Ti and B. The deposited metal with the optimum alloy components ratio achieves good plasticity and toughness while maintaining its tensile strength of 592 MPa, with the impact toughness at 0 °C and elongation reaching 53.34 J and 16.2%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the task of assigning labels to the over-segmented image patches in a weakly supervised manner, in which the training images contain the labels but do not have the labels' locations in the images. We propose a unified discriminative graph inferring framework by simultaneously inferring patch labels and learning the patch appearance models. On one hand, graph inferring reasons the patch labels by a graph propagation procedure. The graph is constructed by connecting the nearest neighbors which share the same image label, and multiple correlations among patches and image labels are imposed as constraints to the inferring. On the other hand, for each label, the patches which do not contain the target label are adopted as negative samples to learn the appearance model. In this way, the predicted labels will be more accurate in the propagation. Graph inferring and the learned patch appearance models are finally embedded to complement each other in one unified formulation. Experiments on three public datasets demonstrate the effectiveness of our method in comparison with other baselines. [ABSTRACT FROM AUTHOR]

Social media sites (e.g., Flickr) generate a huge amount of landmark photos with temporal information in the real-world, such as the photos describing the events happening near landmarks, and those showing different seasonal sceneries. Analyzing this temporal information of landmarks can benefit various applications, such as landmark timeline construction and tour recommendation. In this paper, we propose a novel Incremental Spatio-Temporal Theme Model (ISTTM), which can incrementally mine temporal themes that characterize the temporal information of landmarks, by differentiating them from the other three kinds of themes, i.e., general themes shared by most of all landmarks, local themes related to certain landmarks and the background theme including non-informative content. ISTTM works in an online way and is capable of selectively processing the updates of the distributions on different types of themes. Based on the proposed ISTTM, we present a framework, namely Temporal Theme Analysis for Landmarks (TTAL), which enables both periodic theme detection from discovered temporal themes and temporal theme visualization by selecting the relevant photos. We have conducted experiments on a large-scale landmark dataset from Flickr. Qualitative and quantitative evaluation results demonstrate the effectiveness of the ISTTM as well as the TTAL framework. [ABSTRACT FROM AUTHOR]

Music onset detection is significant and essential for obtaining the high-level music features such as rhythm, beat, music paragraph and structure. The traditional methods for onset detection which employ Short Time Fourier Transform (STFT)-based or Wavelet Transform (WT)-based features to characterize music signal generally lack adaptiveness for representing the stationary and non-stationary part of the music signal. This will lead to the degraded performance for music note onset detection. To solve this problem, a new algorithm for note onset detection based on sparse decomposition is proposed. Firstly, the musical signals are sparsely decomposed with Matching Pursuit (MP), and then the hybrid detection algorithm which combines namely the Degree of Explanation (DE) and the Change of Partials (CP) is applied to the sparse representation of the music signal. Finally, a modified peak-picking algorithm is employed to generate onset vectors. The experiments on the dataset with 2050 onsets show that our results are superior to those of MIREX 2013. For the polyphonic music which is the most widely used form in our real life, the proposed algorithm has better performance than the other algorithms. [ABSTRACT FROM AUTHOR]

Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous measurements are caused by objects in close proximity. To tackle this problem, we propose a multiple information fusion-based multiple hypotheses tracking algorithm integrated with appearance feature, local motion pattern feature and repulsion-inertia model for multi-object tracking. Appearance model based on HSV-local binary patterns histogram and local motion pattern based on optical flow are adopted to describe objects. A likelihood calculation framework is proposed to incorporate the similarities of appearance, dynamic process and local motion pattern. To consider the changes in appearance and motion pattern over time, we make use of an effective template updating strategy for each object. In addition, a repulsion-inertia model is adopted to explore more useful information from ambiguous detections. Experimental results show that the proposed approach generates better trajectories with less missing objects and identity switches. [ABSTRACT FROM AUTHOR]

Underwater wet welding is a crucial repair and maintenance technology for nuclear plant. A boric acid environment raises a new challenge for the underwater welding maintenance of nuclear plant. This paper places emphasis on studying the influence of a boric acid environment in nuclear plant on the underwater welding process. Several groups of underwater wet welding experiments have been conducted in boric acid aqueous solution with different concentration (0-35000 ppm). The viscosity of the welding slag and the mechanical properties of welds, such as the hardness, strength, and elongation, have been studied. The results show that with increasing boric acid concentration, the viscosity of the slag decreases first and then increases at a lower temperature (less than 1441 °C). However, when the temperature is above 1480 °C, the differences between the viscosity measurements become less pronounced, and the viscosity tends to a constant value. The hardness and ductility of the joints can be enhanced significantly, and the maximum strength of the weld metal can be reached at 2300 ppm. [ABSTRACT FROM AUTHOR]

The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. After coding, sum-pooling combined with SPM is used to construct a single feature representation vector for each image. Extensive experimental results on several challenging benchmark datasets show that our LDC method outperforms six state-of-the-art image classification methods. [ABSTRACT FROM AUTHOR]

Cross-media retrieval aims to automatically perform the content-based search procedure among various media types (e.g., image, video and text), in which media representation plays an important role for providing the heterogeneous similarity measure. In this work, a novel semantic representation of cross-media, called accumulated reconstruction error vector (AREV), is proposed, which includes category-specific dictionary learning, media sample reconstruction, and accumulative reconstruction error concatenation. Instead of directly learning the correlation relationship among heterogeneous items in the same semantic groups, the AREV projects individually their original feature descriptions into a shared semantic space, in which each component is semantic consistent for various media types due to the consistency in category information. Experiments on the commonly used datasets, i.e. Wikipedia dataset and NUS-Wide dataset, show the good performance in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Most existing image classification algorithms mainly focus on dealing with images with only 'object' concepts. However, in real-world cases, a great variety of images contain 'verb-object' concepts, rather than only 'object' ones. The hierarchical structure embedded in these 'verb-object' concepts can help to enhance classification. However, traditional feature representation methods cannot utilize it. To tackle this problem, we present in this paper a novel approach, called inductive hierarchical nonnegative graph embedding. By assuming that those 'verb-object' concept images which share the same 'object' part but different 'verb' part have a specific hierarchical structure, we integrate this hierarchical structure into the nonnegative graph embedding technique, together with the definition of inductive matrix, to (1) conduct effective feature extraction from hierarchical structure, (2) easily transfer each new testing sample into its low-dimensional nonnegative representation, and (3) perform image classification of 'verb-object' concept images. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization demonstrate the classification power of proposed approach on 'verb-object' concept images classification. [ABSTRACT FROM AUTHOR]