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The surface topography and chemistry of titanium–aluminum–vanadium (Ti6Al4V) implants play critical roles in the osteoblast differentiation of human bone marrow stromal cells (MSCs) and the creation of an osteogenic microenvironment. To assess the effects of a microscale/nanoscale (MN) topography, this study compared the effects of MN-modified, anodized, and smooth Ti6Al4V surfaces on MSC response, and for the first time, directly contrasted MN-induced osteoblast differentiation with culture on tissue culture polystyrene (TCPS) in osteogenic medium (OM). Surface characterization revealed distinct differences in microroughness, composition, and topography among the Ti6Al4V substrates. MSCs on MN surfaces exhibited enhanced osteoblastic differentiation, evidenced by increased expression of RUNX2, SP7, BGLAP, BMP2, and BMPR1A (fold increases: 3.2, 1.8, 1.4, 1.3, and 1.2). The MN surface also induced a pro-healing inflammasome with upregulation of anti-inflammatory mediators (170–200% increase) and downregulation of pro-inflammatory factors (40–82% reduction). Integrin expression shifted towards osteoblast-associated integrins on MN surfaces. RNA-seq analysis revealed distinct gene expression profiles between MSCs on MN surfaces and those in OM, with only 199 shared genes out of over 1000 differentially expressed genes. Pathway analysis showed that MN surfaces promoted bone formation, maturation, and remodeling through non-canonical Wnt signaling, while OM stimulated endochondral bone development and mineralization via canonical Wnt3a signaling. These findings highlight the importance of Ti6Al4V surface properties in directing MSC differentiation and indicate that MN-modified surfaces act via signaling pathways that differ from OM culture methods, more accurately mimicking peri-implant osteogenesis in vivo. 

Abstract Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long‐term, near‐source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub‐sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under‐ or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting‐related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long‐term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates. 

This work introduces a computational method for designing ceramic scaffolds fabricated via direct ink writing (DIW) for maximum bone growth, whereby the deposited rods are curvilinear. A mechanobiological model of bone adaptation is used to compute bone growth into the scaffold, taking into account the shape of the defect, the applied loading, and the density distribution of bone in which the scaffold is implanted. The method ensures smooth, continuously varying rod contours are produced which are ideal for the DIW process. The method uses level sets of radial basis functions to fully define the scaffold geometry with a small number of design variables, minimizing the optimization’s computational cost. Effective elastic and diffusive properties of the scaffold as a function of the scaffold design and the bone density are obtained from previously constructed surrogates. These property surrogates are in turn used to perform bone adaptation simulations of the scaffold-bone system. Design sensitivities of the bone ingrowth within the scaffold are efficiently obtained using a finite difference scheme implemented in parallel. A demonstration of the methodology on a scaffold implanted in a pig mandible is presented. The scaffold is optimized to maximize bone ingrowth with geometric constraints to conform to the manufacturing process. 

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers. 

Abstract The role of manganese (Mn) in ecosystem carbon (C) biogeochemical cycling is gaining increasing attention. While soil Mn is mainly derived from bedrock, atmospheric deposition could be a major source of Mn to surface soils, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g., industrialization and land‐use change due to agriculture), transport, and deposition, remains uncertain. Here, we use compiled emission data sets for each identified source to model and quantify the atmospheric Mn cycle by combining an atmospheric model and in situ atmospheric concentration measurements. We estimated global emissions of atmospheric Mn in aerosols (<10 μm in aerodynamic diameter) to be 1,400 Gg Mn year⁻¹. Approximately 31% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened Mn “pseudo” turnover times in 1‐m‐thick surface soils (ranging from 1,000 to over 10,000,000 years) by 1–2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn‐to‐N ratio of the atmospheric deposition in non‐desert dominated regions (between 5 × 10⁻⁵and 0.02) across industrialized areas, but that was still lower than soil Mn‐to‐N ratio by 1–3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, consisting with atmospheric Mn deposition enhancing carbon respiration as seen in in situ biogeochemical studies. 

Abstract This work introduces a computational method for designing bone scaffolds for maximum bone growth. A mechanobiological model of bone adaptation is used to compute the bone growth, taking into account the shape of the defect, the applied loading, and the existing density distribution of the bone in which the scaffold has been implanted. Numerical homogenization and a geometry projection technique are used to efficiently obtain surrogates of the effective elastic and diffusive properties of the scaffold as a function of the scaffold design and the bone density. These property surrogates are in turn used to perform bone adaptation simulations of the scaffold–bone system for a sampling of scaffold designs. Surrogates of the bone growth in the scaffold at the end of the simulated time and of the strain energy of the scaffold at implantation time are subsequently constructed from these simulations. Using these surrogates, we optimize the design of a scaffold implanted in a rabbit femur to maximize volume bone growth into the scaffold while ensuring a minimum stiffness at implantation. The results of the optimization demonstrate the effectiveness of the proposed method by showing that maximizing bone growth with a constraint on structural compliance renders scaffold designs with better bone growth than what would be obtained by only minimizing compliance. 

The design of the buffer manager in database management systems (DBMSs) is influenced by the performance characteristics of volatile memory (i.e., DRAM) and non-volatile storage (e.g., SSD). The key design assumptions have been that the data must be migrated to DRAM for the DBMS to operate on it and that storage is orders of magnitude slower than DRAM. But the arrival of new non-volatile memory (NVM) technologies that are nearly as fast as DRAM invalidates these previous assumptions.Researchers have recently designed Hymem, a novel buffer manager for a three-tier storage hierarchy comprising of DRAM, NVM, and SSD. Hymem supports cache-line-grained loading and an NVM-aware data migration policy. While these optimizations improve its throughput, Hymem suffers from two limitations. First, it is a single-threaded buffer manager. Second, it is evaluated on an NVM emulation platform. These limitations constrain the utility of the insights obtained using Hymem. In this paper, we present Spitfire, a multi-threaded, three-tier buffer manager that is evaluated on Optane Persistent Memory Modules, an NVM technology that is now being shipped by Intel. We introduce a general framework for reasoning about data migration in a multi-tier storage hierarchy. We illustrate the limitations of the optimizations used in Hymem on Optane and then discuss how Spitfire circumvents them. We demonstrate that the data migration policy has to be tailored based on the characteristics of the devices and the workload. Given this, we present a machine learning technique for automatically adapting the policy for an arbitrary workload and storage hierarchy. Our experiments show that Spitfire works well across different workloads and storage hierarchies. 

https://pirl.nvsl.io/2020/02/11.