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In the present work, two types of novel nano additives, titanium sulfonate ligand/black phosphorus (TiL4/BP) and titanium dioxide/black phosphorus (TiO2/BP) nanocomposites, were prepared. The tribological behavior of the steel/steel friction pairs lubricated by polyalphaolefins type 6 (PAO6) containing the nanocomposites under boundary lubrication was studied. The worn surfaces were analyzed using modern surface techniques. The experimental results show that the rubbed surfaces became smooth and showed little wear with the addition of the nanocomposites. TiO2/BP nanocomposites can significantly improve the lubricity of BP nanosheets under high contact stress. The synergistic roles of the load-bearing abilities and rolling effect of TiO2 nanoparticles, the slip induced by the BP with its layered structure, and the establishment of a tribofilm on the sliding interface are the basis of the tribological mechanisms.

Type I collagen, the most abundant and arguably the most complex molecule in the human body, is an ABB heterotrimer that self-assembles to form well-defined nanofibers. We herein for the first time report the construction of peptides that could simultaneously mimic the heterotrimer composition and the self-assembly features of Type I collagen.

Osteogenesis imperfecta (OI) is a hereditary bone disorder with various phenotypes ranging from mild multiple fractures to perinatal lethal cases, and it mainly results from the substitution of Gly by a bulkier residue in type I collagen. Triple-helical peptide models of Gly mutations have been widely utilized to decipher the etiology of OI, although these studies are mainly limited to characterizing the peptide features, such as stability and conformation in the solution state. Herein, we have constructed a new series of triple-helical peptides DD(GPO)5 ZPO(GPO)4 DD (Z=Ala, Arg, Asp, Cys, Glu, Ser, and Val) mimicking the most common types of observed OI cases. The inclusion of special terminal aspartic acids enables these collagen mimetic peptides to self-assemble to form nanomaterials upon the trigger of lanthanide ions. We have for the first time systematically evaluated the effect of different OI mutations on the aggregated state of collagen mimetic peptides. We have revealed that the identity of the Gly-substituting residue plays a determinant role in the morphology and secondary structure of the collagen peptide assemblies, showing that bulkier residues tend to result in a disruptive secondary structure and defective morphology, which lead to more severe OI phenotypes. These findings of osteogenesis imperfecta collagen mimetic peptides in the aggregation state provide novel perspectives on the molecular mechanism of osteogenesis imperfecta, and may aid the development of new therapeutic strategies.

Free online sexually explicit materials have become the major way for pornography viewers to consume pornographic materials. Most previous content analytic studies have focused on aggression and degradation behaviors in sexually explicit materials. Fewer studies have focused on the prevalence of depictions of individual sexual behaviors, overlooking that pornographic materials not only show viewers individual sexual behaviors but also provide sexual scripts, which contain a series of co-occurring sexual behaviors. Using the network analysis method, the current study examined the co-occurrence patterns of a large number of sexual behaviors depicted in free online sexually explicit materials. The study has revealed the primary sexual script depicted in popular online sexually explicit materials and predicted the potential effects of such a script.

Plane detection is a key pre-processing step in point clouds for many applications, such as buildings registration and automous cars. Many methods have been proposed for plane detection in point clouds, but most of them cannot meet the requirement of speedability. Therefore, in this paper, we propose a voxel-grid-center-constrained iterative adaptive plane extraction method that can meet the speedability without losing the accuracy of the detected planes. First, the point cloud is subdivided using an octree, and then a voxel grid is randomly selected for coplanar testing to find the plane in the point clouds. Second, in the plane detection phase, a voxel grid center constraint strategy is used to improve the efficiency of plane detection. Finally, an iterative adaptive plane extraction technique is used to improve the robustness. Experimental results demonstrate that the proposed method can achieve high performance on the synthesis datasets as well as on the real datasets.

The correct segmentation of clustered objects in indoor scenes is a critical step of machine understanding in the field of robotics. However, traditional segmentation methods can hardly meet the requirements of accuracy and real time. To this end, an unsupervised object segmentation method based on supervoxel and graph clustering is proposed in this paper. First, the background is removed based on RANSAC, and the remaining point cloud is over-segmented into supervoxels, which is used to improve the segmentation efficiency and to obtain fully connected graph. Second, a graph clustering method based on pure geometric features is proposed to obtain the corresponding subgraphs of each object using the concavity between supervoxels. Finally, a new refinement method for processing the boundaries at the intersection is adapted for obtaining the boundaries with point-level accuracy to improve the segmentation accuracy. The method is experimented on both researcher community public and self-collected datasets. Qualitative and quantitative results show the applicability of our method to robotic real-time object manipulation and its broad development prospect.

As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time., Comment: 8 pages, 1 figure 2 table. Update: new version of paper with up-to-date statistics and new co-authors

Type I collagen, the most abundant and arguably the most complex molecule in the human body, is an ABB heterotrimer that self-assembles to form well-defined nanofibers. We herein for the first time report the construction of peptides that could simultaneously mimic the heterotrimer composition and the self-assembly features of Type I collagen.

Fe3O4 nanospheres, MoS2 nanospheres and Fe3O4@MoS2 nanocomposites were used as lubricating oil additives for steel/steel contact and dispersed in PAO4 base oil. Tribological behavior and wear mechanisms of the steel/steel contacts were investigated. The results indicate that the friction coefficient increased with the addition of Fe3O4 nanospheres and decreased with the addition of MoS2 nanospheres. Their wear types belonged to abrasive wear and furrow wear respectively. In addition, Fe3O4@MoS2 nanocomposites as lubricating additive had excellent lubricating performance and energy-saving potential due to the formation of a stable adsorbed film and a robust tribo-film on the rubbing surfaces which resulted in a mild furrow wear for steel/steel contact.

While COVID-19 is becoming one of the most severe public health crises in the twenty-first century, media coverage about this pandemic is getting more important than ever to make people informed. Drawing on data scraped from Twitter, this study aims to analyze and compare the news updates of two main Spanish newspapers El Pa&iacute, s and El Mundo during the pandemic. Throughout an automatic process of topic modeling and network analysis methods, this study identifies eight news frames for each newspaper&rsquo, s Twitter account. Furthermore, the whole pandemic development process is split into three periods&mdash, the pre-crisis period, the lockdown period and the recovery period. The networks of the computed frames are visualized by these three segments. This paper contributes to the understanding of how Spanish news media cover public health crises on social media platforms.

Twitter is an important object of research in social science studies, and the emergence of numerous Twitter software tools has provided researchers with the possibility of gaining insights into Twitter data; however, at the same time, early stage researchers may experience difficulties in selecting the most suitable tool for their own projects. Due to personal or institutional financial constraints, the purchase of commercial software is not a wise investment for all researchers. Hence, this article aims to provide a review of nine different free-of-charge and low-cost software tools for studying Twitter and highlights their advantages and disadvantages, in order to serve as a guide for social science scholars. This review is divided into two parts: background information and data retrieval features of the software tools, and their data analysis features. At the end of the review, several recommendations and suggestions are made for the use of these tools.

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A photolithography etching technique was used to fabricate textured surfaces on steel samples. The friction and wear behavior of the textured surfaces were studied with surface contact sliding. The influence of the diameter and the density of the dimples were investigated. The results show that the textured surfaces with appropriate diameters and densities had excellent friction reducing and antiwear properties. Large-diameter dimples can destroy the integrity of the lubricating film, and low- or high-density dimples produce more iron sulfates and fewer ferrous sulfides on the rubbing surfaces due to the tribo-reactions, which resulted in higher friction coefficients. The tribo-chemical films, oil micro-reservoir and wear debris-containing roles of the dimples together help the textured surfaces to provide improved antiwear properties.