Impact of Road Bends on Traffic Flow in a Single-Lane Traffic System

Taking the characteristics of road bends as a research object, this work proposes the cellular model (CA) with road bends based on the NaSch model, with which the traffic flow is examined under different conditions, such as bend radius, bend arc length, and road friction coefficiency. The simulation results show that, with the increase of the bend radius, the peak flow will be continuously increased, and the fundamental diagram will become more similar to that of the classic NaSch model; the smaller the bend radius is, the easier it is for the occurrence of blockage; for different bend lengths, all the corresponding traffic flows show that the phenomenon of go-and-stop and the bends exert slight inhibitory effect on traffic flow; under the same bend radius, the inhibition effect of the bends on the traffic flow will be weakened with the increase of the friction coefficiency

NLRP1-Dependent Pyroptosis Leads to Acute Lung Injury and Morbidity in Mice

Acute inflammation in response to both exogenous and endogenous danger signals can lead to the assembly of cytoplasmic inflammasomes that stimulate the activation of caspase-1. Subsequently, caspase-1 facilitates the maturation and release of cytokines and also, under some circumstances, the induction of cell death by pyroptosis. Using a mouse line lacking expression of NLRP1, we show that assembly of this inflammasome in cells is triggered by a toxin from Anthrax and that it initiates caspase-1 activation and release of IL-1β. Furthermore, NLRP1 inflammasome activation also leads to cell death, which escalates over three days following exposure to the toxin and culminates in acute lung injury and death of the mice. We show that these events are not dependent on production of IL-1β by the inflammasome but are dependent on caspase-1 expression. In contrast, MDP mediated inflammasome formation is not dependent on NLRP1, but NLRP3. Taken together, our findings show that assembly of the NLRP1 inflammasome is sufficient to initiate pyroptosis, which subsequently leads to a self-amplifying cascade of cell injury within the lung from which the lung cannot recover, eventually resulting in catastrophic consequences for the organism

Activation of TRPV1 by Dietary Capsaicin Improves Endothelium-Dependent Vasorelaxation and Prevents Hypertension

Some plant-based diets lower the cardiometabolic risks and prevalence of hypertension. New evidence implies a role for the transient receptor potential vanilloid 1 (TRPV1) cation channel in the pathogenesis of cardiometabolic diseases. Little is known about impact of chronic TRPV1 activation on the regulation of vascular function and blood pressure. Here we report that chronic TRPV1 activation by dietary capsaicin increases the phosphorylation of protein kinase A (PKA) and eNOS and thus production of nitric oxide (NO) in endothelial cells, which is calcium dependent. TRPV1 activation by capsaicin enhances endothelium-dependent relaxation in wild-type mice, an effect absent in TRPV1-deficient mice. Long-term stimulation of TRPV1 can activate PKA, which contributes to increased eNOS phosphorylation, improves vasorelaxation, and lowers blood pressure in genetically hypertensive rats. We conclude that TRPV1 activation by dietary capsaicin improves endothelial function. TRPV1-mediated increase in NO production may represent a promising target for therapeutic intervention of hypertension

Unveiling the Effect of CaF₂ on the Microstructure and Transport Properties of Phosphosilicate Systems

As an effective flux, CaF2 is beneficial in improving the fluidity of slag in the steel-making process, which is crucial for dephosphorization. To reveal the existence form and functional mechanism of CaF2 in phosphosilicate systems, the microstructures and transport properties of CaO-SiO2-CaF2-P2O5 quaternary slag systems are investigated by molecular dynamics simulations (MD) combined with experiments. The results demonstrate that the Si-O coordination number does not vary significantly with the increasing CaF2 content, but the P-O coordination number dramatically decreases. CaF2 has a minor effect on the single [SiO4] but makes the structure of the silicate system simple. On the contrary, F− ions could reduce the stability of P-O bonds and promoted the transformation of [PO4] to [PO3F], which is beneficial for making the P element-enriched phosphate network structure more aggregated. However, the introduction of CaF2 does not alter the tetrahedral character of the original fundamental structural unit. In addition, the results of the investigation of the transport properties show that the self-diffusion coefficients of each ion are positively correlated with CaF2 content and arranged in the order of F− > Ca2+ > O2− ≈ P5+ > Si4+. Due to CaF2 reducing the degree of polymerization of the whole melts, the viscosity decreases from 0.39 to 0.13 Pa·s as the CaF2 content increases from 0% to 20%. Moreover, the viscosity of the melt shows an excellent linear dependence on the structural parameters

Hierarchical and Multiple-Perspective Interaction Network for Long Text Matching

Long text matching is widely used in various sub-tasks of natural language processing. However, conducting research in this field can be challenging due to excessive redundant and distracting information, the complex semantic context, and the limited availability of high quality public datasets. Existing long text matching methods generally do not fully use the rich local features embedded in text information, and focus more on encoding long text as fixed length vectors to calculate the semantic distance, disregarding the importance of feature interaction in the text matching process. Therefore, the performance of the relevant models needs to be improved. To address these problems, a hierarchical and multiple-perspective interaction network (HMIN) is proposed in this paper. First, the long text is encoded at the word and sentence levels to extract global features, while one-dimensional convolutional neural networks and attention mechanisms are used to focus on important local features in long texts. Second, the different types of features are compared separately using the comparison function, and then, the comparison results are aggregated. Finally, whether long texts are matched is determined in the prediction layer. We have conducted comparative experiments on two datasets, the results show that HMIN has an improvement in accuracy and F1 values compared with the same type of existing algorithms, and the related experimental analysis demonstrates the effectiveness of the proposed method

Research on the morphological structure of partial fracture healing process in diabetic mice based on synchrotron radiation phase-contrast imaging computed tomography and deep learning

The prevalence of diabetes mellitus has exhibited a notable surge in recent years, thereby augmenting the susceptibility to fractures and impeding the process of fracture healing. The primary objective of this investigation is to employ synchrotron radiation phase-contrast imaging computed tomography (SR-PCI-CT) to examine the morphological and structural attributes of different types of callus in a murine model of diabetic partial fractures. Additionally, a deep learning image segmentation model was utilized to facilitate both qualitative and quantitative analysis of callus during various time intervals. A total of forty male Kunming mice, aged five weeks, were randomly allocated into two groups, each consisting of twenty mice, namely, simple fracture group (SF) and diabetic fracture group (DF). Mice in DF group were intraperitoneally injected 60 mg/kg 1 % streptozotocin(STZ) solution for 5 consecutive days, and the standard for modeling was that the fasting blood glucose level was ≥11.1 mmol /l one week after the last injection of STZ. The right tibias of all mice were observed to have oblique fractures that did not traverse the entire bone. At three, seven, ten and fourteen days after the fracture occurred, the fractured tibias were extracted for SR-PCI-CT imaging and histological analysis. Furthermore, a deep learning image segmentation model was devised to automatically detect, categorize and quantitatively examine different types of callus. Image J software was utilized to measure the grayscale values of different types of callus and perform quantitative analysis. The findings demonstrated that: 1) SR-PCI-CT imaging effectively depicted the morphological attributes of different types of callus of fracture healing. The grayscale values of different types of callus were significantly different(P < 0.01). 2) In comparison to the SF group, the DF group exhibited a significant reduction in the total amount of callus during the same period (P < 0.01). Additionally, the peak of cartilage callus in the hypertrophic phase was delayed. 3) Histology provides the basis for training algorithms for deep learning image segmentation models. The deep-learning image segmentation models achieved accuracies of 0.69, 0.81 and 0.733 for reserve/proliferative cartilage, hypertrophic cartilage and mineralized cartilage, respectively, in the test set. The corresponding Dice values were 0.72, 0.83 and 0.76, respectively.In summary, SR-PCI-CT images are close to the histological level, and a variety of cartilage can be identified on synchrotron radiation CT images compared with histological examination, while artificial intelligence image segmentation model can realize automatic analysis and data generation through deep learning, and further determine the objectivity and accuracy of SR-PCI-CT in identifying various cartilage tissues. Therefore, this imaging technique combined with deep learning image segmentation model can effectively evaluate the effect of diabetes on the morphological and structural changes of callus during fracture healing in mice

Rapid and Facile Synthesis of High-Performance Silver Nanowires by a Halide-Mediated, Modified Polyol Method for Transparent Conductive Films

Silver nanowires are receiving increasing attention as a kind of prospective transparent and conductive material. Here, we successfully synthesized high-performance silver nanowires with a significantly decreased reaction time by a modified polyol method. The synthesis process involved the addition of halides, including NaCl and NaBr, to control the release rate of Ag+ ions, as Cl&minus; and Br&minus; ions react with Ag+ ions to form AgCl and AgBr with different solubilities. As a result, Ag+ ions could be slowly released by graded dissolution, and the formation of silver nanowires was promoted. The results showed that the concentration of the added halides played an important role in the morphology of the final product. High-quality silver nanowires with an average diameter of 70 nm and average length of 21&thinsp;&mu;m were obtained by optimizing the reaction parameters. Afterwards, a simple silver nanowire coating was applied in order to fabricate the transparent conductive films. The film that was based on the silver nanowires provided a transmittance of 91.2% at the 550 nm light wavelength and a sheet resistance of about 78.5 &Omega;&middot;sq&minus;1, which is promising for applications in flexible and transparent optoelectronic devices

A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome

Abstract After sequencing, it is common to screen ncRNA according to expression differences. But this may lose a lot of valuable information and there is currently no indicator to characterize the regulatory function and participation degree of ncRNA on transcriptome. Based on existing pathway enrichment methods, we developed a new algorithm to calculating the participation degree of ncRNA in transcriptome (PDNT). Here we analyzed multiple data sets, and differentially expressed genes (DEGs) were used for pathway enrichment analysis. The PDNT algorithm was used to calculate the Contribution value (C value) of each ncRNA based on its target genes and the pathways they participates in. The results showed that compared with ncRNAs screened by log2 fold change (FC) and p-value, those screened by C value regulated more DEGs in IPA canonical pathways, and their target DEGs were more concentrated in the core region of the protein–protein interaction (PPI) network. The ranking of disease critical ncRNAs increased integrally after sorting with C value. Collectively, we found that the PDNT algorithm provides a measure from another view compared with the log2FC and p-value and it may provide more clues to effectively evaluate ncRNA