CD105 Deficieny in Mouse Aorta-derived Mesenchymal Stem Cells Promotes An Enhanced Inflammatory Response to Lipolysaccharide.

Mesenchymal stem cells (MSCs) are being widely studied for their ability to regulate macrophage cell responses. Previous works have demonstrated that mouse aorta-derived MSC (mAo-MSC) support the macrophage inflammatory response. mAo-MSC have been characterized phenotypically for MSC-associated surface antigens and express CD90 and CD105 but do not express CD73. CD105, also known as endoglin, is a coreceptor in the TGFβ superfamily of receptors. Mouse adipose-derived MSC lacking CD105 have an increased capacity to regulate T-cells by reducing their proliferation while elevated CD105 expression is consistently associated with inflammatory disease. Therefore, we hypothesized that suppression of CD105 in mAo-MSC will reduce the immunosupportive capacity of the mAo-MSC. We used siRNA to reduce expression of CD105 in mAo- MSC and subsequently examined the effect of this deficiency on their response to lipopolysaccharide (LPS) and their ability to support the macrophage inflammatory response. Contrary to our hypothesis, CD105 deficient mAo-MSC cultured alone and in co-culture with macrophage secreted increased levels of the inflammatory indicators nitric oxide (NO) and interleukin 6 (IL-6) after exposure to LPS. The increase in NO and IL-6 observed in the co-cultures is additive and therefore points to the mAo- MSC as the primary origin. Overall our data suggest that CD105 acts as a regulator of the TLR-4 pathway and may represent an important target for modification of MSC to be used in therapeutics

Optimization of municipal solid waste management using externality costs

Economic and environmental impacts associated with solid waste management (SWM) systems should be considered to ensure sustainability of such systems. Societal life cycle costing (S-LCC) can be used for this purpose since it includes “budget costs” and “externality costs.” While budget costs represent market goods and services in monetary terms, i.e. economic impacts, externality costs include effects outside the economic system such as environmental impacts (translated in monetary terms).1 Numerous models have been developed to determine the environmental and economic impacts associated with SWM systems (e.g., EASETECH2) by using “what-if” scenario analyses. While these models are an essential foundation that enables a systematic integrated analysis of SWM systems, they do not provide information about the overall optimal solution as done with optimization models such as SWOLF.3 This study represents the first attempt to optimize SWM systems using externality costs in SWOLF. The assessment identifies the waste strategy that minimizes externality costs and other criteria (budget costs and landfilling) for a specific case study. The latter represents a hypothetical U.S. county with annual waste generation of 320,000 Mg. The externality cost includes the damage costs of fossil CO2, CH4, N2O, PM2.5, PM10, NOX, SO2 , VOC, CO, NH3, CO, Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. Table 1 shows the results of the optimization including: i) optimization criteria, ii) waste flows and iii) eco-efficiency indicator (ratio between externality costs and budget costs). Minimal externality costs are obtained when incinerating most of the waste (88%) and commingled collection of recyclables (12%). The eco-efficiency of this waste strategy corresponds to -0.6, i.e. its environmental benefits (negative externality costs) correspond to approximately half of its budget costs. On the other hand, there is the solution with minimal budget costs (100% of the waste is landfilled) in which the environmental load (positive externality cost) represent one third of the budget costs (positive eco-efficiency indicator). In between these options, there is a strategy with minimal landfilling in which the organic waste is sent to anaerobic digestion, the recyclables to a single stream MRF and the residual to a mixed waste MRF. Most of the externality costs of the three strategies stem from SO2, NOx and GHG as suggested by Woon & Lo.4 The case study shows that waste solutions identified by optimization modelling differ from common SWM systems selected for analysis in state-of-the-art accounting modelling Please click Additional Files below to see the full abstract

Evaluation of Externality Costs in Life-Cycle Optimization of Municipal Solid Waste Management Systems

The development of sustainable solid waste management (SWM) systems requires consideration of both economic and environmental impacts. Societal life-cycle costing (S-LCC) provides a quantitative framework to estimate both economic and environmental impacts, by including “budget costs” and “externality costs”. Budget costs include market goods and services (economic impact), whereas externality costs include effects outside the economic system (e.g., environmental impact). This study demonstrates the applicability of S-LCC to SWM life-cycle optimization through a case study based on an average suburban U.S. county of 500 000 people generating 320 000 Mg of waste annually. Estimated externality costs are based on emissions of CO₂, CH₄, N₂O, PM_2.5, PM₁₀, NO_<i>x</i>, SO₂, VOC, CO, NH₃, Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. The results indicate that incorporating S-LCC into optimized SWM strategy development encourages the use of a mixed waste material recovery facility with residues going to incineration, and separated organics to anaerobic digestion. Results are sensitive to waste composition, energy mix and recycling rates. Most of the externality costs stem from SO₂, NO_<i>x</i>, PM_2.5, CH₄, fossil CO₂, and NH₃ emissions. S-LCC proved to be a valuable tool for policy analysis, but additional data on key externality costs such as organic compounds emissions to water would improve future analyses

Characterisation of the crack tip plastic zone in fatigue via synchrotron X-ray diffraction

This paper describes a new methodology for characterising the plastic zoneahead of a fatigue crack. This methodology is applied to a set of experimentaldata obtained by synchrotron X-ray diffraction on a bainitic steel compact ten-sion specimen. The methodology is based on generating the equivalent VonMises strain field from the X-ray experimental elastic strain maps. Based onthe material response, a threshold is then applied on the equivalent strainmaps to identify the size and shape of the plastic zone. The experimental plas-tic zone lies between the plane strain and plane stress Westergaard's boundsbut closer to the plane strain theoretical prediction confirming that the volumeanalyzed is predominantly subjected to plane strain conditions. However, theobserved plastic zone has a somewhat flatter shape, extending further from thecrack plane but less extended in the crack growing direction.Engineering and Physical SciencesResearch Council, Grant/Award Numbers:EP/R00661X/1, EP/S019367/1, EP/P025021/1, EP/P025498/1; EuropeanSocial Found, Grant/Award Number:UMAJI84; Programa Operativo FEDER(Junta de Andalucia, Spain), Grant/AwardNumber: UMA18-FEDERJA-250; Fundingfor open access charge: Universidad deM alaga / CBU

To What Extent Does the Eddy Covariance Footprint Cutoff Influence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards?

Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (ustar), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values

Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High-Resolution Imagery in Commercial Vineyards

Estimation of surface energy fluxes using thermal remote sensing–based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in Ts and Tc estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%

The delay time distribution of supernovae from integral-field spectroscopy of nearby galaxies

Constraining the delay time distribution (DTD) of different supernova (SN) types can shed light on the time-scales of galaxy chemical enrichment and feedback processes affecting galaxy dynamics, and SN progenitor properties. Here, we present an approach to recover SN DTDs based on integral-field spectroscopy (IFS) of their host galaxies. Using a statistical analysis of a sample of 116 SNe in 102 galaxies, we evaluate different DTD models for SN types Ia (73), II (28), and Ib/c (15). We find the best SN Ia DTD fit to be a power law with an exponent α = -1.1 +/- 0.3 (50 per cent confidence interval (C.I.)), and a time delay (between star formation and the first SNe) Δ = 50-35+100 Myr (50 per cent C.I.). For core collapse (CC) SNe, both of the Zapartas et al. DTD models for single and binary stellar evolution are consistent with our results. For SNe II and Ib/c, we find a correlation with a Gaussian DTD model with σ = 82-23+129 Myr and σ = 56-9+141 Myr (50 per cent C.I.), respectively. This analysis demonstrates that IFS opens a new way of studying SN DTD models in the local Universe

Clinical care of incarcerated people with HIV, viral hepatitis, or tuberculosis

The burden of HIV/AIDS and other transmissible diseases is higher in prison and jail settings than in the non-incarcerated communities that surround them. In this comprehensive review, we discuss available literature on the topic of clinical management of people infected with HIV, hepatitis B and C viruses, and tuberculosis in incarcerated settings in addition to co-occurrence of one or more of these infections. Methods such as screening practices and provision of treatment during detainment periods are reviewed to identify the effect of community-based treatment when returning inmates into the general population. Where data are available, we describe differences in the provision of medical care in the prison and jail settings of low-income and middle-income countries compared with high-income countries. Structural barriers impede the optimal delivery of clinical care for prisoners, and substance use, mental illness, and infectious disease further complicate the delivery of care. For prison health care to reach the standards of community-based health care, political will and financial investment are required from governmental, medical, and humanitarian organisations worldwide. In this review, we highlight challenges, gaps in knowledge, and priorities for future research to improve health-care in institutions for prisoners

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management,with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown.The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient.From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University Aggie Air TM sUAS program over a commercial vineyard located near Lodi, California.This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2,14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (HandLE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity (u∗)that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications