Low dose and fast grating-based x-ray phase-contrast imaging using the integrating-bucket phase modulation technique

X-ray phase-contrast imaging has experienced rapid development over the last few decades, and in this technology, the phase modulation strategy of phase-stepping is used most widely to measure the sample's phase signal. However, because of its discontinuous nature, phase-stepping has the defects of worse mechanical stability and high exposure dose, which greatly hinder its wide application in dynamic phase measurement and potential clinical applications. In this manuscript, we demonstrate preliminary research on the use of integrating-bucket phase modulation method to retrieve the phase information in grating-based X-ray phase-contrast imaging. Experimental results showed that our proposed method can be well employed to extract the differential phase-contrast image, compared with the current mostly used phase-stepping strategy, advantage of integrating-bucket phase modulation technique is that fast measurement and low dose are promising.Comment: 14 pages, 6 figure

Virtual Relational Knowledge Graphs for Recommendation

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type individually. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that it is not efficient nor effective to use every relation type for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which explicitly distinguish the influence of different relations for item representation learning. We first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for encoding nodes, which iteratively updates a node embedding only depending on the embedding of its own and its neighbors, but involve no additional training parameters. We also employ the LWS mechanism on a user-item bipartite graph for user representation learning, which utilizes encodings of items with relational knowledge to help training representations of users. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods

Potential Implications of Quercetin in Autoimmune Diseases

Autoimmune diseases are a worldwide health problem with growing rates of morbidity, and are characterized by breakdown and dysregulation of the immune system. Although their etiology and pathogenesis remain unclear, the application of dietary supplements is gradually increasing in patients with autoimmune diseases, mainly due to their positive effects, relatively safety, and low cost. Quercetin is a natural flavonoid that is widely present in fruits, herbs, and vegetables. It has been shown to have a wide range of beneficial effects and biological activities, including anti-inflammation, anti-oxidation, and neuroprotection. In several recent studies quercetin has reportedly attenuated rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, and systemic lupus erythematosus in humans or animal models. This review summarizes the evidence for the pharmacological application of quercetin for autoimmune diseases, which supports the view that quercetin may be useful for their prevention and treatment

Multifaceted oncostatin M: novel roles and therapeutic potential of the oncostatin M signaling in rheumatoid arthritis

Rheumatoid arthritis (RA) is a self-immune inflammatory disease characterized by joint damage. A series of cytokines are involved in the development of RA. Oncostatin M (OSM) is a pleiotropic cytokine that primarily activates the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway, and other physiological processes such as cell proliferation, inflammatory response, immune response, and hematopoiesis through its receptor complex. In this review, we first describe the characteristics of OSM and its receptor, and the biological functions of OSM signaling. Subsequently, we discuss the possible roles of OSM in the development of RA from clinical and basic research perspectives. Finally, we summarize the progress of clinical studies targeting OSM for the treatment of RA. This review provides researchers with a systematic understanding of the role of OSM signaling in RA, which can guide the development of drugs targeting OSM for the treatment of RA

The Influence of Physical Exercise Frequency and Intensity on Individual Entrepreneurial Behavior: Evidence from China

Physical exercise can benefit individuals’ physical and mental health and also influence individuals’ long-term behavioral choices. Doing exercise is particularly important given that physical exercise can impact individuals’ cognitive abilities and positive emotional states, which may further impact entrepreneurial behavior. Therefore, understanding the relationship between exercise and entrepreneurial behavior is essential, because it can provide policy suggestions for popularizing athletic activities and boosting entrepreneurship. Consequently, the present study examined whether physical exercise could predict entrepreneurial behavior and the possible psychological mechanisms within this relationship. Based on the 2017 Chinese General Social Survey (CGSS2017), this study tested the hypotheses using the Probit and Tobit models. The results showed that individuals’ physical exercise intensity and frequency positively affected their entrepreneurial behavior. In addition, five variables moderated the relationships between physical exercise and individual entrepreneurial behavior: urban–rural differences, education level, marital status, the existence of minor children, and age. Moreover, positive emotions and physical/mental health mediated the influence of physical exercise (exercise frequency and exercise intensity) on individual entrepreneurial behavior. Endogeneity explanations were ruled out by including instrumental variable, copula terms and adopting coarsened exact matching

Mechanical Properties and Microstructural Features of Biomass Fly Ash-Modified Self-Compacting Coal Gangue-Filled Backfill

To achieve sustainable utilization of a large amount of mine solid waste, this study investigated the performance of self-compacting coal gangue-filled backfill (SCFB) containing biomass fly ash (BFA) generated from biomass power plants as a supplementary cementitious material (SCM). The correlations between the physical structure and compressive strength of SCFB samples were obtained by ultrasonic pulse velocity (UPV). The failure process of the SCFB samples was monitored by the digital image correlation (DIC) technique, and the stress–strain relationship and failure pattern were also analyzed. The micro-morphological structure and hydration products of SCFB samples were evaluated by X-ray diffraction (XRD), scanning electron microscopy (SEM), and backscattered electron imaging (SEM-BSE). The results show that the usage of 30~40% BFA in SCFB improves the physical structure and strength of the samples. The compressive strength and UPV value of SCFB samples with different water-to-cement (w/c) ratios showed a similar trend of increasing and then gradually decreasing as the proportion of ordinary Portland cement (OPC) replaced by BFA increased. BFA exhibits better reactivity and filling effect in SCFB samples with a high w/c ratio. The peak stress of SCFB samples gradually decreases, and resistance to deformation gradually weakens with the increase in w/c ratios, while the DIC results further verify the mechanical experimental results. Microstructural analysis revealed that reducing the w/c ratio and incorporating specific ratios of BFA can reduce the thickness of the interface transition zone (ITZ) and porosity. The results of the study will provide theoretical guidance for the modification, stability monitoring, and strengthening of SCFB

Using Extended Technology Acceptance Model to Assess the Adopt Intention of a Proposed IoT-Based Health Management Tool

Advancements in IoT technology contribute to the digital progress of health science. This paper proposes a cloud-centric IoT-based health management framework and develops a system prototype that integrates sensors and digital technology. The IoT-based health management tool can collect real-time health data and transmit it to the cloud, thus transforming the signals of various sensors into shared content that users can understand. This study explores whether individuals in need tend to use the proposed IoT-based technology for health management, which may lead to the new development of digital healthcare in the direction of sensors. The novelty of this research lies in extending the research perspective of sensors from the technical level to the user level and explores how individuals understand and adopt sensors based on innovatively applying the IoT to health management systems. By organically combining TAM with MOA theory, we propose a comprehensive model to explain why individuals develop perceptions of usefulness, ease of use, and risk regarding systems based on factors related to motivation, opportunity, and ability. Structural equation modeling was used to analyze the online survey data collected from respondents. The results showed that perceived usefulness and ease of use positively impacted adoption intention, Perceived ease of use positively affected perceived usefulness. Perceived risk had a negative impact on adoption intention. Readiness was only positively related to perceived usefulness, while external benefits were positively related to perceived ease of use and negatively related to perceived risk. Facilitative conditions were positively correlated with perceived ease of use and negatively correlated with perceived risk. Technical efficacy was positively related to perceived ease of use and perceived usefulness. Overall, the research model revealed the cognitive mechanism that affects the intention of individuals to use the system combining sensors and the IoT and guides the digital transformation of health science