The design and development of a smart bioadhesive hydrogel sealant with self-healing and excellent antibacterial activity to achieve high wound closure effectiveness and post-wound-closure care is highly desirable in clinical applications. In this work, a series of adhesive antioxidant antibacterial self-healing hydrogels with promising traits were designed through dual-dynamic-bond cross-linking among ferric iron (Fe), protocatechualdehyde (PA) containing catechol and aldehyde groups and quaternized chitosan (QCS) to enable the closure of skin incisions and promotion of methicillin-resistant Staphylococcus aureus (MRSA)-infected wound healing. The dual-dynamic-bond cross-linking of a pHsensitive coordinate bond (catechol−Fe) and dynamic Schiff base bonds with reversible breakage and re-formation equips the hydrogel with excellent autonomous healing and on-demand dissolution or removal properties. Additionally, the hydrogel presents injectability, good biocompatibility and antibacterial activity, multifunctional adhesiveness, and hemostasis as well as NIR responsiveness. The in vivo evaluation in a rat skin incision model and infected full-thickness skin wound model revealed the high wound closure effectiveness and post-wound-closure care of the smart hydrogels, demonstrating its great potential in dealing with skin incisions and infected full-thickness skin wounds.

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 computingand 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.

Recent experiments have shown that membrane-bound Ras proteins form transient, nanoscale signaling platforms that play a crucial role in high-fidelity signal transmission. However, a detailed characterization of these dynamic proteolipid substructures by high-resolution experimental techniques remains elusive. Here we use extensive semiatomic simulations to reveal the molecular basis for the formation and domain-specific distribution of Ras nanoclusters. As model systems, we chose the triply lipidated membrane targeting motif of H-ras (tH) and a large bilayer made up of di16∶0-PC (DPPC), di18∶2-PC (DLiPC), and cholesterol. We found that 4-10 tH molecules assemble into clusters that undergo molecular exchange in the sub-μs to μs time scale, depending on the simulation temperature and hence the stability of lipid domains. Driven by the opposite preference of tH palmitoyls and farnesyl for ordered and disordered membrane domains, clustered tH molecules segregate to the boundary of lipid domains. Additionally, a systematic analysis of depalmitoylated and defarnesylated tH variants allowed us to decipher the role of individual lipid modifications in domain-specific nanocluster localization and thereby explain why homologous Ras isoforms form nonoverlapping nanoclusters. Moreover, the localization of tH nanoclusters at domain boundaries resulted in a significantly lower line tension and increased membrane curvature. Taken together, these results provide a unique mechanistic insight into how protein assembly promoted by lipid-modification modulates bilayer shape to generate functional signaling platforms.lipid bilayer | lipidated Ras proteins | nanoclusters/nanodomains | plasma membrane | molecular dynamics simulation

The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

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