Switch between critical percolation modes in city traffic dynamics

Percolation transition is widely observed in networks ranging from biology to engineering. While much attention has been paid to network topologies, studies rarely focus on critical percolation phenomena driven by network dynamics. Using extensive real data, we study the critical percolation properties in city traffic dynamics. Our results suggest that two modes of different critical percolation behaviors are switching in the same network topology under different traffic dynamics. One mode of city traffic (during nonrush hours or days off) has similar critical percolation characteristics as small world networks, while the other mode (during rush hours on working days) tends to behave as a 2D lattice. This switching behavior can be understood by the fact that the high-speed urban roads during nonrush hours or days off (that are congested during rush hours) represent effective long-range connections, like in small world networks. Our results might be useful for understanding and improving traffic resilience.Comment: 8 pages, 4 figures, Daqing Li, Ziyou Gao and H. Eugene Stanley are the corresponding authors ([email protected], [email protected], [email protected]

Identifying the most influential roads based on traffic correlation networks

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s)

Hepatic Autophagy Deficiency Remodels Gut Microbiota for Adaptive Protection via FGF15-FGFR4 Signaling

Background & aims: The functions of the liver and the intestine are closely tied in both physiological and pathologic conditions. The gut microbiota (GM) often cause deleterious effects during hepatic pathogenesis. Autophagy is essential for liver homeostasis, but the impact of hepatic autophagy function on liver-gut interaction remains unknown. Here we investigated the effect of hepatic autophagy deficiency (Atg5Δhep) on GM and in turn the effect of GM on the liver pathology. Methods: Fecal microbiota were analyzed by 16S sequencing. Antibiotics were used to modulate GM. Cholestyramine was used to reduce the enterohepatic bile acid (BA) level. The functional role of fibroblast growth factor 15 (FGF15) and ileal farnesoid X receptor (FXR) was examined in mice overexpressing FGF15 gene or in mice given a fibroblast growth factor receptor-4 (FGFR4) inhibitor. Results: Atg5Δhep causes liver injury and alterations of intestinal BA composition, with a lower proportion of tauro-conjugated BAs and a higher proportion of unconjugated BAs. The composition of GM is significantly changed with an increase in BA-metabolizing bacteria, leading to an increased expression of ileal FGF15 driven by FXR that has a higher affinity to unconjugated BAs. Notably, antibiotics or cholestyramine treatment decreased FGF15 expression and exacerbated liver injury. Consistently, inhibition of FGF15 signaling in the liver enhances liver injury. Conclusions: Deficiency of autophagy function in the liver can affect intestinal environment, leading to gut dysbiosis. Surprisingly, such changes provide an adaptive protection against the liver injury through the FGF15-FGFR4 signaling. Antibiotics use in the condition of liver injury may thus have unexpected adverse consequences via the gut-liver axis

A photo-triggered and photo-calibrated nitric oxide donor: rational design, spectral characterizations, and biological applications

Nitric oxide (NO) donors are valuable tools to probe the profound implications of NO in health and disease. The elusive nature of NO bio-relevance has largely limited the use of spontaneous NO donors and promoted the development of next generation NO donors, whose NO release is not only stimulated by a trigger, but also readily monitored via a judiciously built-in self-calibration mechanism. Light is without a doubt the most sensitive, versatile and biocompatible method of choice for both triggering and monitoring, for applications in complex biological matrices. Herein, we designed and synthesized an N-nitroso rhodamine derivative (NOD560) as a photo-triggered and photo-calibrated NO donor to address this need. NOD560 is essentially non-fluorescent. Upon irradiation by green light (532nm), it efficiently release NO and a rhodamine dye, the dramatic fluorescence turn-on from which could be harnessed to conveniently monitor the localization, flux, and dose of NO release. The potentials of NOD560 for in vitro biological applications were also exemplified in in vitro biological models, i.e. mesenchymal stem cell (MSC) migration suppression. NOD560 is expected to complement the existing NO donors and find widespread applications in chemical biological studies

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process

The potential benefit of pseudo high thermal conductivity for laser powder bed fusion additive manufacturing

This study examined the impact of transient pseudo high thermal conductivity to the fabrication of crack-free parts with Laser Powder-Bed-Fusion (L-PBF) based additive manufacturing (AM) method. Thermal diffusivity and thermal conductivity of L-PBF samples made by mixtures of IN939 alloy and Si powders were investigated. At temperatures above 800°C, the as-fabricated Si-doped IN939 was observed to exhibit an exceptionally high thermal conductivity, which can be attributed to the occurrence of endothermic reactions. This pseudo high thermal conductivity can effectively minimize the thermal stress and offers a potential solution to produce crack-free L-PBF parts for nonweldable alloys