Over 300 billion of cells die every day in the human body, producing a large number of endogenous apoptotic extracellular vesicles (apoEVs). Also, allogenic stem cell transplantation, a commonly used therapeutic approach in current clinical practice, generates exogenous apoEVs. It is well known that phagocytic cells engulf and digest apoEVs to maintain the body's homeostasis. In this study, we show that a fraction of exogenous apoEVs is metabolized in the integumentary skin and hair follicles. Mechanistically, apoEVs activate the Wnt/β-catenin pathway to facilitate their metabolism in a wave-like pattern. The migration of apoEVs is enhanced by treadmill exercise and inhibited by tail suspension, which is associated with the mechanical force-regulated expression of DKK1 in circulation. Furthermore, we show that exogenous apoEVs promote wound healing and hair growth via activation of Wnt/β-catenin pathway in skin and hair follicle mesenchymal stem cells. This study reveals a previously unrecognized metabolic pathway of apoEVs and opens a new avenue for exploring apoEV-based therapy for skin and hair disorders.
Dietary and symbiotic bacteria can exert powerful influence on metazoan lipid metabolism. Recent studies have emerged that microbiota have a role in animal obesity and related health disorders, but the mechanisms by which bacteria influence lipid storage in their host are unknown. To reduce the complexity of the relationship between gut microbiota and the host, Caenorhabditis elegans (C. elegans) has been chosen as a model organism to study interspecies interaction. Here, we demonstrate that feeding C. elegans with an opportunistic pathogenic bacterium Stenotrophomonas maltophilia (S. maltophilia) retards growth and promotes excessive neutral lipid storage. Gene expression analysis reveals that dietary S. maltophilia induces a lipogenic transcriptional response that includes the SREBP ortholog SBP-1, and fatty acid desaturases FAT-6 and FAT-7. Live imaging and ultrastructural analysis suggest that excess neutral lipid is stored in greatly expanded lipid droplets (LDs), as a result of enhanced endoplasmic reticulum (ER)-LD interaction. We also report that loss of function mutations in dpy-9 in C. elegans confers resistance to S. maltophilia. Dietary S. maltophilia induces supersized LDs by enhancing lipogenesis and ER-LD contacts in C. elegans. This work delineates a new model for understanding microbial regulation of metazoan physiology.
Dropshafts with tangential intakes are common structures in urban water drainage systems. The plunging and vortex flowpatterns are the typical flow regimes, which are mainly affected by the tangential intake design and flood discharge. However, thehydraulic flow transition between vortex and plunging flow patterns has received little attention, which may affect the dropshaftoperation during extreme flood discharge events. In this study, the effects of the flow discharge and contraction ratio on the pressuredistributions of vortex dropshafts are analysed based on a series of numerical simulations. The pressure distributions of the plungingand vortex flow patterns are compared; two hydraulic factors are used to distinguish rotational and non-rotational dropshaftoperations. For appropriate vortex dropshaft designs, the minimum to maximum wall pressure ratio should be greater than 0.1 at theintake cross-section and greater than 0.2 during the falling process. Based on this principle, flows in the annular dropshaft areclassified into non-rotation, transition, and rotation regimes. The results agree with the theoretical rotation intensity of the vortex flow,and an empirical relation is proposed. The present study is easy to implement and practically useful to assess the vortex dropshaftdesign for local water drainage systems. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service . There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.
Background Tea trees originated in southwest China 60 million or 70 million years ago. Written records show that Chinese ancestors had begun drinking tea over 3000 years ago. Nowadays, with the aging of populations worldwide and more people suffering from non-communicable diseases or poor health, tea beverages have become an inexpensive and fine complementary and alternative medicine (CAM) therapy. At present, there are 3 billion people who like to drink tea in the world, but few of them actually understand tea, especially on its development process and the spiritual and cultural connotations. Methods We searched PubMed, Google Scholar, Web of Science, CNKI, and other relevant platforms with the key word “tea”, and reviewed and analyzed tea-related literatures and pictures in the past 40 years about tea’s history, culture, customs, experimental studies, and markets. Results China is the hometown of tea, tea trees, tea drinking, and tea culture. China has the oldest wild and planted tea trees in the world, fossil of a tea leaf from 35,400,000 years ago, and abundant tea-related literatures and art works. Moreover, tea may be the first Chinese herbal medicine (CHM) used by Chinese people in ancient times. Tea drinking has many benefits to our physical health via its antioxidant, anti-inflammatory, immuno-regulatory, anticancer, cardiovascular-protective, anti-diabetic, and anti-obesity activities. At the moment, COVID-19 is wreaking havoc across the globe and causing severe damages to people’s health and lives. Tea has anti-COVID-19 functions via the enhancement of the innate immune response and inhibition of viral growth. Besides, drinking tea can allow people to acquire a peaceful, relaxed, refreshed and cheerful enjoyment, and even longevity. According to the meridian theory of traditional Chinese medicine, different kinds of tea can activate different meridian systems in the human body. At present, black tea (fermented tea) and green tea (non-fermented tea) are the most popular in the world. Black tea accounts for over 90% of all teas sold in western countries. The world’s top-grade black teas include Qi Men black in China, Darjeeling and Assam black tea in India, and Uva black tea in Sri Lanka. However, all top ten famous green teas in the world are produced in China, and Xi Hu Long Jing tea is the most famous among all green teas. More than 700 different kinds of components and 27 mineral elements can be found in tea. Tea polyphenols and theaflavin/thearubigins are considered to be the major bioactive components of black tea and green tea, respectively. Overly strong or overheated tea liquid should be avoided when drinking tea. Conclusions Today, CAM provides an array of treatment modalities for the health promotion in both developed and developing countries all over the world. Tea drinking, a simple herb-based CAM therapy, has become a popular man-made non-alcoholic beverage widely consumed worldwide, and it can improve the growth of economy as well. Tea can improve our physical and mental health and promote the harmonious development of society through its chemical and cultural elements.
One of the most interesting directions in quantum simulations with ultracold atoms is the expansion of our capability to investigate exotic topological matter. Using sophisticated atom-light couplings in an atomic system, scientists have demonstrated several iconic lattice models that exhibit non-trivial band topology in a controlled manner. With atom-light couplings in atomic systems, scientists have demonstrated several iconic lattice models that exhibit non-trivial band topology in controlled manners, which expands our capability to investigate exotic topological matter.
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
Electrochemical reduction of CO2 into value-added chemicals has been envisioned as a promising strategy to alleviate the issue of increasing CO2 emissions. However, the sluggish oxygen evolution reaction (OER), as the anodic reaction, typically consumes approximately 90% of the electricity input, necessitating the development of an efficient OER for energy-saving purposes. Herein, we developed a unique heterostructure of multi-double (bi)-shelled Co-based spheres via a facile template-free method, in which each bi-shelled structure is composed of Co9Se8/Co9S8/CoO (Co-S-Se) with a symmetric configuration. These heterogeneous nanospheres possess both sufficient heterointerfaces and a high density of active sites and exhibit excellent OER activity in alkaline media with a low overpotential of 226 mV at 10 mA cm⁻², a small Tafel slope of 46.5 mV dec⁻¹, and long-term durability over 15 h. As a proof and concept, when coupled with a cathodic CO2 reduction reaction, the electrochemical performance of Pd nanosheets (NSs) for CO2 reduction can be significantly enhanced in terms of product selectivity and energy input. Our study might provide insight into the development of efficient OER electrocatalysts for practical CO2 reduction reactions.
A passive detection method has been proposed in a prior paper to extract key parameters and detect faults using the ambient noise present in water pipeline networks. This paper presents field experiments and data processing results to provide systematic experimental validation of this method. Field experiments were carried out in operational water pipeline networks at the University of Canterbury campus and the Waimakariri District, New Zealand, during which ambient noise was measured by pairs of pressure sensors installed at selected hydrants on pipelines of different materials, network topologies and simulated faults. Auto-correlation and cross-correlation analysis of noise at a single sensor and sensor pairs were carried out to estimate the wave speed and to locate faults in the networks. Data processing results indicate that water usage generating pressure transients are the dominant sources of ambient noise in operational water pipeline networks. This type of ambient noise can also be utilized by the passive detection method to achieve similar wave speed estimation accuracy and fault detection performance as the conventional active pressure wave detection methods.
Droplet phase change is the key phenomenon for high heat transfer rates in spray or drop-wise cooling applications. Despite high cooling efficiency of the spray cooling technology, conventional fluids, such as water, cannot be used for thermal management of modern high heat flux devices due to their immense power density, resulting in early device failures. To address this issue, in this research, we experimentally study the evaporation performance for various volumes of the copper-alumina hybrid nanofluid (CAHF) droplet on a plain copper substrate and compare it with water (H2O) droplet in sub-boiling and boiling regimes (i.e., for substrate temperatures of 25–170 °C). We also numerically investigate and compare the internal velocity and thermal fields of CAHF and H2O droplets on a heated plain copper substrate. Besides the plain copper surface, we examine the phase change behaviour of the subsequent CAHF droplet over a heated residue surface that was obtained from the phase transition of the first CAHF droplet on a heated plain copper substrate. Our results demonstrate that the evaporation rate of CAHF droplets on a plain copper surface is up to 24% and an order of magnitude higher than water droplets in sub-boiling and nucleate boiling regimes, respectively. Moreover, the evaporation rate of the CAHF droplet on a residue surface increases up to 141% and 800% compared to that on a plain copper surface in sub-boiling and nucleate boiling regimes, respectively. Furthermore, the latent heat flux up to 10 times can be achieved using the CAHF droplet compared to H2O droplet on a plain copper substrate in the nucleate boiling region, making the CAHF a potential fluid for high heat flux cooling applications.
This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. This framework uses an existing Fast Fourier Transform (FFT) approach to identify experimental modal parameters from time-history data and employs a class of maximum-entropy probability distributions to account for the mismatch between the modal parameters. It also considers a parameterized probability distribution for capturing the variability of structural parameters across multiple data sets. In this framework, the computation is addressed through Expectation-Maximization (EM) strategies, empowered by Laplace approximations. As a result, a new rationale is introduced for assigning optimal weights to the modal properties when updating structural parameters. According to this framework, the modal features’ weights are equal to the inverse of the aggregate uncertainty, comprised of the identification and prediction uncertainties. The proposed framework is coherent in modeling the entire process of inferring structural parameters from response-only measurements and is comprehensive in accounting for different sources of uncertainty, including the variability of both modal and structural parameters over multiple data sets, as well as their identification uncertainties. Numerical and experimental examples are employed to demonstrate the HBM framework, wherein the environmental and operational conditions are almost constant. It is observed that the variability of parameters across data sets remains the dominant source of uncertainty while being much larger than the identification uncertainties.
The application of building information modeling (BIM) technology has effectively supported the high-quality development of building sustainability and informatization in China. However, few studies comprehensively analyzed the enacted policies, prevalent applications, and existing barriers of the latest application and development of BIM technology in building industry from building sustainability and informatization perspectives to provide effective consultation and guidelines for its rational scale application in China. This paper firstly made a statistical analysis on the policies and standards of BIM technology issued from 2011 to 2021 in China. Moreover, the latest application, development and existing issues of BIM technology in building sustainability and informatization were also comprehensively discussed and analyzed. The main conclusions indicated that the application status of BIM technology for building sustainability and informatization in China was large in quantity, wide in scope, but low in level. The existing issue and limitation in terms of BIM application in China was mainly due to the lack of standards and domestic-oriented tools. Finally, the future outlook and recommendations of BIM technology for building sustainability and informatization in China were also presented as avenues for upcoming research.
The emergence of multi-axis printing systems provides a new solution to print complex parts. However, the current process planning methods fail to balance the support-free requirement, the collision-free condition, as well as the mechanical performance at the same time when printing parts with complicated features. This paper proposes a new curved layer slicing and continuous printing path planning method that considers these factors. The proposed method makes use of two mutually orthogonal unit vector fields embedded on the tetrahedral mesh of the part: the filament orientation vector field and the printing orientation vector field, which indicate the filament orientation and the nozzle orientation at each position during the printing process respectively. A quantitative optimization model is established to compute the two optimal unit vector fields. Then a monotonically increasing scalar distance field is generated by integrating along the optimal printing orientation vector field, and the isosurfaces of the distance field can be used to naturally slice the part into curved layers. Finally, at each isosurface, a continuous printing path is planned along the optimal filament orientation field by interpolating isolines of a surface embedded scalar distance field. Both the simulation and physical experiments were conducted to confirms the effectiveness of the proposed method and its advantage over the existing methods in balance different factors including support-free condition, collision-free requirement, and mechanical performance.
Tropospheric delays (TDs) limit the accurate detection of slow slope motion using interferometric synthetic aperture radar (InSAR), especially in subtropical coastal regions prone to frequent changes in humidity. Although TDs can be estimated through external weather data, their spatiotemporal resolution and data availability are greatly limited, which is not applicable for individual slopes. This paper presents new TD correction methods for slopes with both small dimensions and small elevation changes. We simultaneously estimated and eliminated the TD signal from a line-of-sight (LOS) time series through a blind source separation (i.e., independent component analysis). The stratified TD sources were isolated according to a spatially elevation-linked and temporally periodic independent-component (IC), which was determined via a correlation test and power spectrum analysis. Hence, the TD was corrected without the use of any external weather products/meteorological data and had unprecedented spatiotemporal details equivalent to the synthetic aperture radar (SAR) images. The proposed method was verified using CosmoSkyMed (CSK) and Sentinel-1 (SNT-1) images covering a slope in Tai O, Lantau Island, Hong Kong, and validated using a series of geodetic, meteorological, and hydrological data. Up to 3–4-cm relative TDs were measured in the LOS directions of CSK and SNT-1. The relative TD exhibited a slower increment rate than the slope elevation and was largely affected by specific air conditions (e.g., temperature and humidity) on the SAR image-acquisition days. The analysis of InSAR data yielded reasonably good estimates of millimeter-scale downslope slips (due to increases in pore-water pressure in the wet season) and upslope rebound (due to soil shrinkage in the dry season). It was found that soil swelling and shrinkage of the slope (also known as seasonal ratcheting) and the reclamation were likely regulated by rainfall and sea levels, respectively. Although the slope motion in Tai O was determined to be small (i.e., seasonal variations of 10 mm), the TD correction reduced the root-mean-square error by 42.3%, such that InSAR time-series measurements with millimeter-level accuracy (potentially 1–3 mm) were obtained.
Small-scale, residential solar systems have been increasingly recognized as a key sector for future carbon emission reduction in cities. This study investigated customer preferences of solar thermal and photovoltaic systems through a crowdsourced discrete choice experiment and latent class choice modeling targeting Boston, Massachusetts and Atlanta, Georgia. Key motivating factors for adoption in both testbeds are installation cost, environmental benefits, and annual savings. Despite the latent classes’ similarity in their preferences of different system features, all classes present different socioeconomic characteristics across the two testbeds, indicating preference heterogeneity across cities. We also found that both cities have significant early adopters residing in lower-property-value regions, revealing a potential to achieve both carbon emission reduction and community renaissance objectives when combining infrastructure renovation projects with decentralized energy systems installation. This study presents a framework for assessing and understanding the social demand of decentralized energy systems to facilitate their future promotions.
Order-structured catalyst layers offer a promising solution to substantially reducing the Pt loading while maintaining the performance of proton exchange membrane fuel cells (PEMFCs). In this work, we develop a multiscale model to investigate the mass transport characteristics of a PEMFC with ordered catalyst layers. A Langmuir adsorption equation is proposed to describe the ionomer-Pt interfacial transport process in a local oxygen transport sub-model, which is integrated into a two-dimensional, two-phase cell-scale model. Simulations are validated against experimental data in the literature. Results show that the fuel cell with ordered catalyst layers can achieve much higher performance than that with conventional catalyst layers, due to the enhanced bulk and local oxygen transport. Moreover, both local oxygen transport resistances of ordered and conventional catalyst layers show an inverse proportional function of Pt loading, while the ordered catalyst layers exhibit a much smaller local oxygen transport resistance than their conventional counterparts. Under limiting current conditions, oxygen transport across the ionomer-Pt interface dominates the local transport resistance, thus hindering the cell performance. The effects of pore size of the ordered catalyst layers and relative humidity on the oxygen transport characteristics and cell performance are also investigated. This work provides new insights into the mass transport mechanisms in ordered catalyst layers, which will facilitate the development of high-performance PEMFCs with low Pt loading.
China implemented the first phase of its National Healthy Cities pilot program from 2016-20. Along with related urban health governmental initiatives, the program has helped put health on the agenda of local governments while raising public awareness. Healthy City actions taken at the municipal scale also prepared cities to deal with the COVID-19 pandemic. However, after intermittent trials spanning the past two decades, the Healthy Cities initiative in China has reached a crucial juncture. It risks becoming inconsequential given its overlap with other health promotion efforts, changing public health priorities in response to the pandemic, and the partial adoption of the Healthy Cities approach advanced by the World Health Organization (WHO). We recommend aligning the Healthy Cities initiative in China with strategic national and global level agendas such as Healthy China 2030 and the Sustainable Development Goals (SDGs) by providing an integrative governance framework to facilitate a coherent intersectoral program to systemically improve population health. Achieving this alignment will require leveraging the full spectrum of best practices in Healthy Cities actions and expanding assessment efforts. Funding Tsinghua-Toyota Joint Research Fund “Healthy city systems for smart cities” program.
Moisture has a great effect on the microstructure of mudstone and the stability of muddy strata. Due to many clay minerals in mudstone, swelling deformation and even disintegration occur during moisture diffusion, accompanied by energy storage and release. To reveal the deterioration mechanism and energy characteristics of mudstone induced by moisture diffusion, firstly, the Weibull distribution function is introduced into the FDEM-based moisture diffusion-fracture coupling model to consider the heterogeneity of swelling deformation and stress of mudstone minerals. Then, the microfracture behavior and energy evolution of heterogeneous mudstone during moisture diffusion are studied using the coupling model. Results indicate that the nonuniform swelling deformation between particles is the main reason for the microfracture, both tensile and shear failures occur in mudstone. The evolution of kinetic energy can be divided into three stages, while that of strain energy depends on the storage rate by swelling deformation and the release rate by microfracture in mudstone during moisture diffusion. When the storage rate is greater than the release rate, the evolution of strain energy is divided into two stages. Otherwise, it is divided into three stages. Besides, the effects of homogeneity index, swelling coefficient, and moisture content on the microcrack morphology and energy evolution are also discussed. Finally, the microcrack morphology and energy evolution of mudstone under different moisture loading conditions are investigated, where the microcrack morphology under the surrounding moisture condition agrees well with the experimental results. The results in this paper provide theoretical value for studying the swelling and microfracture mechanism of mudstone during moisture diffusion.
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