Bioinspired Injectable Self-Healing Hydrogel Sealant with Fault-Tolerant and Repeated Thermo-Responsive Adhesion for Sutureless Post-Wound-Closure and Wound Healing

Hydrogels with multifunctionalities,including sufficient bonding strength,injectability and self-healing capacity,responsive-adhesive ability,fault-tolerant and repeated tissue adhesion,are urgently demanded for invasive wound closure and wound healing.Motivated by the adhesive mechanism of mussel and brown algae,bioinspired dynamic bonds cross-linked multifunctional hydrogel adhesive is designed based on sodium alginate(SA),gelatin(GT)and protocatechualdehyde,with ferric ions added,for sutureless post-wound-closure.The dynamic hydrogel cross-linked through Schiff base bond,catechol-Fe coordinate bond and the strong interaction between GT with temperature-dependent phase transition and SA,endows the resulting hydrogel with sufficient mechanical and adhesive strength for efficient wound closure,injectability and self-healing capacity,and repeated closure of reopened wounds.Moreover,the temperature-dependent adhesive properties endowed mispositioning hydrogel to be removed/repositioned,which is conducive for the fault-tolerant adhesion of the hydrogel adhesives during surgery.Besides,the hydrogels present good biocompatibility,near-infrared-assisted photothermal antibacterial activity,antioxidation and repeated thermo-responsive reversible adhesion and good hemostatic effect.The in vivo incision closure evaluation demonstrated their capability to promote the post-wound-closure and wound healing of the incisions,indicating that the developed reversible adhesive hydrogel dressing could serve as versatile tissue sealant.

基于萘环稠合策略构筑间位双硼类纯绿光窄光谱发光材料

硼氮多重共振(MR)发光材料在宽色域有机发光二极管(OLEDs)中具有重要应用前景,因此受到广泛关注.其中,间位双硼骨架凭借其高效率和极窄的发光光谱,在蓝光发光材料的构筑中已展现出独特优势,但如何基于间位双硼骨架构筑绿光窄光谱发光分子却鲜有报道.本论文基于萘环稠合策略设计并合成了绿光发光分子BN-NAP和BNANAP,其最大发射峰分别为511和518 nm,并保持了窄光谱发光性质,其发光半峰宽(FWHM)仅为26和20 nm.利用BN-ANAP作为发光材料制备得到纯绿光OLED器件,其CIE坐标为[026,067],最大外量子效率(EQEmax)为21.0%,同时具有较小的效率滚降.

Human mobility data in the COVID-19 pandemic:characteristics,applications,and challenges

The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or simulate the spread of COVID-19.Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks.We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective.We identified three major sources of mobility data:public transit systems,mobile operators,and mobile phone applications.Four approaches have been commonly used to estimate human mobility:public transit-based flow,social activity patterns,index-based mobility data,and social media-derived mobility data.We compared mobility datasets’characteristics by assessing data privacy,quality,space–time coverage,high-performance data storage and processing,and accessibility.We also present challenges and future directions of using mobility data.This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Antibacterial conductive self-healable supramolecular hydrogel dressing for infected motional wound healing

Wounds on stretchable parts of the human body cause prolonged suffering and involve more severe healing processes than wounds on stationary parts. However, they have received insufficient attention compared with other types of chronic wounds. In this study, a novel supramolecular gelatin(GT) hydrogel composed of GT-graft-aniline tetramer and quaternized chitosan was presented. The hydrogel was crosslinked by monoaldehyde β-cyclodextrin via host-guest interaction and dynamic Schiff base and was free from permanent covalent bonds, heavy metals, and oxidants. Given its dynamic feature, the hydrogel exhibited flexibility, self-healing, and tissue adhesiveness and well adapted to motion wounds. Moreover, the hydrogel was bioactive with conductivity, antioxidant property, hemostatic effect, antibacterial, and photothermal effect(the killing ratio for methicillinresistant Staphylococcus aureus(MRSA) was higher than 99.8% after 5 min of near-infrared irradiation) and exhibited ondemand removability. In the full-thickness MRSA-infected motional wound healing experiment, this novel hydrogel exhibited a significantly enhanced wound healing efficacy with a fast wound closure ratio(about 99.0% for 14 days), mild inflammatory response, high level of collagen deposition, and enhanced re-epithelialization by downregulating interleukin-6 and CD68 and upregulating vascular endothelial growth factor. The results indicated that this hydrogel has great potential in wound healing and skin tissue engineering and serves as an inspiration for the design of supramolecular biomaterials.

A general-purpose framework for parallel processing of large-scale LiDAR data

Light detection and ranging(LiDAR)data are essential for scientific discoveries such as Earth and ecological sciences,environmental applications,and responding to natural disasters.While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands.Efficiently storing,managing,and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications.However,handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity.To tackle such challenges,we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle‘big’LiDAR data collections.The contributions of this research were(1)a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system,(2)two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks,and(3)by coupling existing LiDAR processing tools with Hadoop,a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application.A proof-of-concept prototype is presented here to demonstrate the feasibility,performance,and scalability of the proposed framework.

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties.We collect,process,and compute mobility data from four different sources.We further design a Responsive Index(RI)based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S.county level.We find statistically significant positive correlations in the RI between either two data sources,revealing their general similarity,albeit with varying Pearson’s r coefficients.Despite the similarity,however,mobility from each source presents unique and even contrasting characteristics,in part demonstrating the multifaceted nature of human mobility.The results suggest that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic.Most states present a positive difference in RI between their upper-income and lower-income counties,where diverging patterns in time series of mobility changes percentages can be found.The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy.However,what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales,as well as its relationship with the neighborhood character-istics of customers’origins.Based on the Point of Interest(POI)measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US,our study takes Lower Manhattan,New York City,as the pilot study,and aims to examine 1)the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak,2)the areas where restaurant customers live,and 3)the association between the neighborhood characteristics of these areas and lost customers.By doing so,we provide a geographic information system-based analytical frame-work integrating the big data mining,web crawling techniques,and spatial-economic modelling.Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Organic near-infrared photodetectors with photoconductivityenhanced performance

Organic near-infrared(NIR)photodetectors with essential applications in medical diagnostics,night vision,remote sensing,and optical communications have attracted intensive research interest.Compared with most conventional inorganic counterparts,organic semiconductors usually have higher absorption coefficients,and their thin active layer could be sufficient to absorb most incident light for effective photogeneration.However,due to the relatively poor charge mobility of organic materials,it remains challenging to inhibit the photogenerated exciton recombination and effectively extract carriers to their respective electrodes.Herein,this challenge was addressed by increasing matrix conductivities of a ternary active layer(D–A–D structure NIR absorber[2TT-oC6B]:poly(N,N′-bis-4-butylphenyl-N,N′-bisphenyl)benzidin[PolyTPD]:[6,6]-phenyl-C61-butyric acid methyl ester[PCBM]=1:1:1)upon in situ incident light illumination,significantly accelerating charge transport through percolated interpenetrating paths.The greatly enhanced photoconductivity under illumination is intrinsically related to the unique donor–acceptor molecular structures of PolyTPD and 2TT-oC6B,whereas stable intermolecular interaction has been verified by systematic molecular dynamics simulation.In addition,an ultrafast charge transfer time of 0.56 ps from the NIR aggregation-induced luminogens of 2TT-oC6B absorber to PolyTPD and PCBM measured by femtosecond transient absorption spectroscopy is beneficial for effective exciton dissociation.The solution-processed organic NIR photodetector exhibits a fast response time of 83μs and a linear dynamic range value of 111 dB under illumination of 830 nm.Therefore,our work has opened up a pioneering window to enhance photoconductivity through in situ photoirradiation and benefit NIR photodetectors as well as other optoelectronic devices.

Using geospatial social media data for infectious disease studies: a systematic review

Geospatial social media(GSM)data has been increasingly used in public health due to its rich,timely,and accessible spatial information,particularly in infectious disease research.This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022.These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood,county,state,and country.We categorized these studies into three major infectious disease research domains:surveillance,explanation,and prediction.With the assistance of advanced computing,statistical and spatial methods,GSM data has been widely and deeply applied to these domains,particularly in surveillance and explanation domains.We further identified four knowledge gaps in terms of contextual information use,application scopes,spatiotemporal dimension,and data limitations and proposed innovation opportunities for future research.Ourfindings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

Big Data and cloud computing:innovation opportunities and challenges

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business,sciences and engineering.At the same time,Big Data presents challenges for digital earth to store,transport,process,mine and serve the data.Cloud computing provides fundamental support to address the challenges with shared computing resources including computing,storage,networking and analytical software;the application of these resources has fostered impressive Big Data advancements.This paper surveys the two frontiers–Big Data and cloud computing–and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains.From the aspects of a general introduction,sources,challenges,technology status and research opportunities,the following observations are offered:(i)cloud computing and Big Data enable science discoveries and application developments;(ii)cloud computing provides major solutions for Big Data;(iii)Big Data,spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements;(iv)intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data;(v)open availability of Big Data and processing capability pose social challenges of geospatial significance and(vi)a weave of innovations is transforming Big Data into geospatial research,engineering and business values.This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume,velocity,variety and veracity into values of Big Data for local to global digital earth science and applications.

Taking the pulse of COVID-19:a spatiotemporal perspective

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

A visual-textual fused approach to automated tagging of flood-related tweets during a flood event

In recent years,social media such as Twitter have received much attention as a new data source for rapid flood awareness.The timely response and large coverage provided by citizen sensors significantly compensate the limitations of non-timely remote sensing data and spatially isolated river gauges.However,automatic extraction of flood tweets from a massive tweets pool remains a challenge.Taking the Houston Flood in 2017 as a study case,this paper presents an automated flood tweets extraction approach by mining both visual and textual information a tweet contains.A CNN architecture was designed to classify the visual content of flood pictures during the Houston Flood.A sensitivity test was then applied to extract flood-sensitive keywords that were further used to refine the CNN classified results.A duplication test was finally performed to trim the database by removing the duplicated pictures to create the flood tweets pool for the flood event.The results indicated that coupling CNN classification results with flood-sensitive words in tweets allows a significant increase in precision while keeps the recall rate in a high level.The elimination of tweets containing duplicated pictures greatly contributes to higher spatio-temporal relevance to the flood.

Spatiotemporal event detection: a review

The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious diseases.The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and crime.Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.This paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected events.The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection research.Guidance is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.

A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences

Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.

Introduction to social sensing and big data computing for disaster management

Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events.Social sensing enables all citizens to become part of a large sensor network,which is low cost,more comprehensive,and always broadcasting situational awareness information.However,data collected with social sensing is often massive,heterogeneous,noisy,unreliable from some aspects,comes in continuous streams,and often lacks geospatial reference information.Together,these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress.Meanwhile,big data computing methods and technologies such as high-performance computing,deep learning,and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion.This special issue captures recent advancements in leveraging social sensing and big data computing for supporting disaster management.Specifically analyzed within these papers are some of the promises and pitfalls of social sensing data for disaster relevant information extraction,impact area assessment,population mapping,occurrence patterns,geographical disparities in social media use,and inclusion in larger decision support systems.

Social distance integrated gravity model for evacuation destination choice

Evacuation is an effective and commonly taken strategy to minimize death and injuries from an incoming hurricane.For decades,interdisciplinary research has contributed to a better understanding of evacuation behavior.Evacuation destination choice modeling is an essential step for hurricane evacuation transportation planning.Multiple factors are identified associated with evacuation destination choices,in which long-term social factors have been found essential,yet neglected,in most studies due to difficulty in data collection.This study utilized long-term human movement records retrieved from Twitter to(1)reinforce the importance of social factors in evacuation destination choices,(2)quantify individual-level familiarity measurement and its relationship with an individual’s destination choice,(3)develop a big data approach for aggregated county-level social distance measurement,and(4)demonstrate how gravity models can be improved by including both social distance and physical distance for evacuation destination choice modeling.

A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints

In the Big Data era,Earth observation is becoming a complex process integrating physical and social sectors.This study presents an approach to generating a 100 m population grid in the Contiguous United States(CONUS)by disaggregating the US cen-sus records using 125 million of building footprints released by Microsoft in 2018.Land-use data from the OpenStreetMap(OSM),a crowdsourcing platform,was applied to trim original footprints by removing the non-residential buildings.After trimming,several metrics of building measurements such as building size and build-ing count in a census tract were used as weighting scenarios,with which a dasymetric model was applied to disaggregate the American Community Survey(ACS)5-year estimates(2013-2017)into a 100 m population grid product.The results confirm that the OSM trimming process removes non-residential buildings and thus provides a better representation of population distribution within complicated urban fabrics.The building size in the census tract is found in the optimal weighting scenario.The product is 2.5Gb in size containing 800 million populated grids and is currently hosted by ESRI(http://arcg.is/19S4qK)for visualization.The data can be accessed via https://doi.org/10.7910/DVN/DLGP7Y.With the accel-erated acquisition of high-resolution spatial data,the product could be easily updated for spatial and temporal continuity.

REGγ drives Lgr5^(+) stem cells to potentiate radiation induced intestinal regeneration

Leucine-rich repeat containing G protein-coupled receptor 5(Lgr5), a marker of intestinal stem cells(ISCs), is considered to play key roles in tissue homoeostasis and regeneration after acute radiation injury. However, the activation of Lgr5 by integrated signaling pathways upon radiation remains poorly understood. Here, we show that irradiation of mice with whole-body depletion or conditional ablation of REGγ in Lgr5^(+) stem cell impairs proliferation of intestinal crypts, delaying regeneration of intestine epithelial cells. Mechanistically, REGγ enhances transcriptional activation of Lgr5 via the potentiation of both Wnt and Hippo signal pathways. TEAD4 alone or cooperates with TCF4, a transcription factor mediating Wnt signaling, to enhance the expression of Lgr5. Silencing TEAD4 drastically attenuated β-catenin/TCF4 dependent expression of Lgr5. Together, our study reveals how REGγ controls Lgr5 expression and expansion of Lgr5+stem cells in the regeneration of intestinal epithelial cells.Thus, REGγ proteasome appears to be a potential therapeutic target for radiation-induced gastrointestinal disorders.

Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives

A spatial web portal(SWP)provides a web-based gateway to discover,access,manage,and integrate worldwide geospatial resources through the Internet and has the access characteristics of regional to global interest and spiking.Although various technologies have been adopted to improve SWP performance,enabling high-speed resource access for global users to better support Digital Earth remains challenging because of the computing and communication intensities in the SWP operation and the dynamic distribution of end users.This paper proposes a cloud-enabled framework for high-speed SWP access by leveraging elastic resource pooling,dynamic workload balancing,and global deployment.Experimental results demonstrate that the new SWP framework outperforms the traditional computing infrastructure and better supports users of a global system such as Digital Earth.Reported methodologies and framework can be adopted to support operational geospatial systems,such as monitoring national geographic state and spanning across regional and global geographic extent.

Optimization for Micro-energy Grid Dispatch Based on Non-supplementary Fired Compressed Air Energy Storage Aided Energy Hub and Hybrid Hyper-spherical Search

Micro-energy grids have shown superiorities over traditional electricity and heating management systems.This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features.First,to enhance the ability to support new storage equipment,an energy hub model is proposed using the non-supplementary fired compressed air energy storage(NSF-CAES).This provides flexible dispatch for cooling,heating and electricity.Second,considering the unique characteristics of the NSF-CAES,a sliding time window(STW)method is designed for simple but effective energy dispatch.Third,for the optimization of energy dispatch,we blend the differential evolution(DE)with the hyper-spherical search(HSS)to formulate a hybrid DE-HSS algorithm,which enhances the global search ability and accuracy.Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.