Palladium phosphide nanoparticles as highly selective catalysts for the selective hydrogenation of acetylene

This work was supported by the National Key Research and Development Program of China (2016YFB0301601), National Natural Science Foundation of China.Peer reviewedPostprin

Pilus of Streptococcus pneumoniae: structure, function and vaccine potential

The pilus is an extracellular structural part that can be detected in some Streptococcus pneumoniae (S. pneumoniae) isolates (type I pili are found in approximately 30% of strains, while type II pili are found in approximately 20%). It is anchored to the cell wall by LPXTG-like motifs on the peptidoglycan. Two kinds of pili have been discovered, namely, pilus-1 and pilus-2. The former is encoded by pilus islet 1 (PI-1) and is a polymer formed by the protein subunits RrgA, RrgB and RrgC. The latter is encoded by pilus islet 2 (PI-2) and is a polymer composed mainly of the structural protein PitB. Although pili are not necessary for the survival of S. pneumoniae, they serve as the structural basis and as virulence factors that mediate the adhesion of bacteria to host cells and play a direct role in promoting the adhesion, colonization and pathogenesis of S. pneumoniae. In addition, as candidate antigens for protein vaccines, pili have promising potential for use in vaccines with combined immunization strategies. Given the current understanding of the pili of S. pneumoniae regarding the genes, proteins, structure, biological function and epidemiological relationship with serotypes, combined with the immunoprotective efficacy of pilins as protein candidates for vaccines, we here systematically describe the research status and prospects of S. pneumoniae pili and provide new ideas for subsequent vaccine research and development

A High-Order Kalman Filter Method for Fusion Estimation of Motion Trajectories of Multi-Robot Formation

Multi-robot motion and observation generally have nonlinear characteristics; in response to the problem that the existing extended Kalman filter (EKF) algorithm used in robot position estimation only considers first-order expansion and ignores the higher-order information, this paper proposes a multi-robot formation trajectory based on the high-order Kalman filter method. The joint estimation method uses Taylor expansion of the state equation and observation equation and introduces remainder variables on this basis, which effectively improves the estimation accuracy. In addition, the truncation error and rounding error of the filtering algorithm before and after the introduction of remainder variables, respectively, are compared. Our analysis shows that the rounding error is much smaller than the truncation error, and the nonlinear estimation performance is greatly improved

CEO Media Exposure and Green Technological Innovation Decision: Evidence from Chinese Polluting Firms

The upper echelons theory is utilized to establish how CEO’s attributes affect firm’s technological innovation decisions. The extant literature has largely ignored the impacts of CEO media exposure. An unbalanced panel data analysis is used to examine the effects of CEO media exposure on Chinese polluting firm’s green technological innovation. It is illustrated that CEO media exposure generally enhances Chinese polluting firms’ green technological innovation decisions. In addition, we find that firms with state ownership and environmental regulations all moderate positively the relationship between CEO media exposure and green technological innovation. The research suggests that CEO media exposure appears to be a stimulus to firm’s green technological innovation decisions

Towards Interpretation of Pairwise Learning

Recently, there are increasingly more attentions paid to an important family of learning problems called pairwise learning, in which the associated loss functions depend on pairs of instances. Despite the tremendous success of pairwise learning in many real-world applications, the lack of transparency behind the learned pairwise models makes it difficult for users to understand how particular decisions are made by these models, which further impedes users from trusting the predicted results. To tackle this problem, in this paper, we study feature importance scoring as a specific approach to the problem of interpreting the predictions of black-box pairwise models. Specifically, we first propose a novel adaptive Shapley-value-based interpretation method, based on which a vector of importance scores associated with the underlying features of a testing instance pair can be adaptively calculated with the consideration of feature correlations, and these scores can be used to indicate which features make key contributions to the final prediction. Considering that Shapley-value-based methods are usually computationally challenging, we further propose a novel robust approximation interpretation method for pairwise models. This method is not only much more efficient but also robust to data noise. To the best of our knowledge, we are the first to investigate how to enable interpretation in pairwise learning. Theoretical analysis and extensive experiments demonstrate the effectiveness of the proposed methods

The diverse actions of cytoskeletal vimentin in bacterial infection and host defense

International audienceBacterial infection is a major threat to human health, with infections resulting in considerable mortality, urging the need for a more profound understanding of bacteria-host interactions. During infection of cells, host cytoskeletal networks constantly interact with bacteria and are integral to their uptake. Vimentin, an intermediate filament protein, is one such cytoskeletal component that interacts with bacteria during infection. Although vimentin is predominantly present in the cytoplasm, it also appears in a secreted form or at the surface of multiple cell types, including epithelial cells, endothelial cells, macrophages and fibroblasts. As a cytoplasmic protein, vimentin participates in bacterial transportation and the consequential immune-inflammatory responses. When expressed on the cell surface, vimentin can be both pro-and anti-bacterial, favoring bacterial invasion in some contexts, but also limiting bacterial survival in others. Vimentin is also secreted and located extracellularly, where it is primarily involved in bacterial-induced inflammation regulation. Reciprocally, bacteria can also manipulate the fate of vimentin in host cells. Given that vimentin is not only involved in bacterial infection, but also the associated life-threatening inflammation, the use of vimentin-targeted drugs might offer a synergistic advantage. In this Review, we recapitulate the abundant evidence on vimentin and its dynamic changes in bacterial infection and speculate on its potential as an anti-bacterial therapeutic target

Parvalbumin and Somatostatin Interneurons Control Different Space-Coding Networks in the Medial Entorhinal Cortex

The medial entorhinal cortex (MEC) contains several discrete classes of GABAergic interneurons, but their specific contributions to spatial pattern formation in this area remain elusive. We employed a pharmacogenetic approach to silence either parvalbumin (PV)- or somatostatin (SOM)-expressing interneurons while MEC cells were recorded in freely moving mice. PV-cell silencing antagonized the hexagonally patterned spatial selectivity of grid cells, especially in layer II of MEC. The impairment was accompanied by reduced speed modulation in colocalized speed cells. Silencing SOM cells, in contrast, had no impact on grid cells or speed cells but instead decreased the spatial selectivity of cells with discrete aperiodic firing fields. Border cells and head direction cells were not affected by either intervention. The findings point to distinct roles for PV and SOM interneurons in the local dynamics underlying periodic and aperiodic firing in spatially modulated cells of the MEC

Pairwise Learning with Differential Privacy Guarantees

Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i.e., the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the learned models. To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and offline settings. Specifically, for the online setting, we first introduce a differentially private algorithm (called OnPairStrC) for strongly convex loss functions. Then, we extend this algorithm to general convex loss functions and give another differentially private algorithm (called OnPairC). For the offline setting, we also present two differentially private algorithms (called OffPairStrC and OffPairC) for strongly and general convex loss functions, respectively. These proposed algorithms can not only learn the model effectively from the data but also provide strong privacy protection guarantee for sensitive information in the training set. Extensive experiments on real-world datasets are conducted to evaluate the proposed algorithms and the experimental results support our theoretical analysis