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Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

Deforestation contributes 6–17% of anthropogenic carbon dioxide emissions. However, much uncertainty in the calculation of deforestation emissions stems from the inadequacy of forest carbon-density and deforestation data. Now an analysis provides the most-detailed estimate so far of the carbon density of vegetation and the associated carbon dioxide emissions from deforestation for ecosystems across the tropics. Deforestation contributes 6–17% of global anthropogenic CO 2 emissions to the atmosphere 1 . Large uncertainties in emission estimates arise from inadequate data on the carbon density of forests 2 and the regional rates of deforestation. Consequently there is an urgent need for improved data sets that characterize the global distribution of aboveground biomass, especially in the tropics. Here we use multi-sensor satellite data to estimate aboveground live woody vegetation carbon density for pan-tropical ecosystems with unprecedented accuracy and spatial resolution. Results indicate that the total amount of carbon held in tropical woody vegetation is 228.7 Pg C, which is 21% higher than the amount reported in the Global Forest Resources Assessment 2010 (ref.  3 ). At the national level, Brazil and Indonesia contain 35% of the total carbon stored in tropical forests and produce the largest emissions from forest loss. Combining estimates of aboveground carbon stocks with regional deforestation rates 4 we estimate the total net emission of carbon from tropical deforestation and land use to be 1.0 Pg C yr −1 over the period 2000–2010—based on the carbon bookkeeping model. These new data sets of aboveground carbon stocks will enable tropical nations to meet their emissions reporting requirements (that is, United Nations Framework Convention on Climate Change Tier 3) with greater accuracy.

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008-2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.

Although only a small percentage of global land cover, urban areas significantly alter climate, biogeochemistry, and hydrology at local, regional, and global scales. To understand the impact of urban areas on these processes, high quality, regularly updated information on the urban environment—including maps that monitor location and extent—is essential. Here we present results from efforts to map the global distribution of urban land use at 500m spatial resolution using remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach uses a supervised decision tree classification algorithm that we process using region-specific parameters. An accuracy assessment based on sites from a stratified random sample of 140 cities shows that the new map has an overall accuracy of 93% (k = 0.65) at the pixel level and a high level of agreement at the city scale (R2 = 0.90). Our results (available at http://sage.wisc.edu/urbanenvironment.html) also reveal that the land footprint of cities occupies less than 0.5% of the Earth’s total land area.

Toll-like receptors (TLRs) serve as drug targets for a variety of indications ranging from cancer to autoimmune disease.Biomaterials of both natural and synthetic origin have been increasingly investigated as TLR-interfacing agents for treating disease, some of which have been approved by the FDA as vaccine adjuvants.Engineering strategies which modify or harness these compounds provide new avenues to enhance drug delivery, receptor activation, and downstream immune activity.Strategies to modulate the innate immune system rely on a more nuanced approach, tailoring materials chemistry and biophysical pattern display to produce differential immune responses. These strategies often rely on a bottom-up understanding of TLR activity. Macromolecular Toll-like receptor (TLR) agents have been utilized as agonists and inhibitors in preclinical and clinical settings. These agents interface with the TLR class of innate immune receptors which recognize macromolecular ligands that are characteristic of pathogenic material. As such, many agents that have been historically investigated are derived from the natural macromolecules which activate or inhibit TLRs. This review covers recent research and clinically available TLR agents that are macromolecular or polymeric. Synthetic materials that have been found to interface with TLRs are also discussed. Assemblies of these materials are investigated in the context of improving stability or efficacy of ligands. Attention is given to strategies which modify or enhance the current agents and to future outlooks on the development of these agents.

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.

III-V semiconductor nanowires deterministically placed on top of silicon electronic platform would open many avenues in silicon-based photonics, quantum technologies and energy harvesting. For this to become a reality, gold-free site-selected growth is necessary. Here, we propose a mechanism which gives a clear route for maximizing the nanowire yield in the self-catalyzed growth fashion. It is widely accepted that growth of nanowires occurs on a layer-by-layer basis, starting at the triple-phase line. Contrary to common understanding, we find that vertical growth of nanowires starts at the oxide-substrate line interface, forming a ring-like structure several layers thick. This is granted by optimizing the diameter/height aspect ratio and cylindrical symmetry of holes, which impacts the diffusion flux of the group V element through the well-positioned group III droplet. This work provides clear grounds for realistic integration of III-Vs on silicon and for the organized growth of nanowires in other material systems. The ability to place perfectly aligned vertical nanowires at chosen positions on a silicon substrate is an important challenge in device fabrication. Here, the authors propose a mechanism to explain self-catalyzed III-V nanowire growth on silicon, providing valuable insights for growing high yield nanowire arrays.

The COSINUS (Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches) experiment aims at the detection of dark matter-induced recoils in sodium iodide (NaI) crystals operated as scintillating cryogenic calorimeters. The detection of both scintillation light and phonons allows performing an event-by-event signal to background discrimination, thus enhancing the sensitivity of the experiment. The choice of using NaI crystals is motivated by the goal of probing the long-standing DAMA/LIBRA results using the same target material. The construction of the experimental facility is foreseen to start by 2021 at the INFN Gran Sasso National Laboratory (LNGS) in Italy. It consists of a cryostat housing the target crystals shielded from the external radioactivity by a water tank acting, at the same time, as an active veto against cosmic ray-induced events. Taking into account both environmental radioactivity and intrinsic contamination of materials used for cryostat, shielding and infrastructure, we performed a careful background budget estimation. The goal is to evaluate the number of events that could mimic or interfere with signal detection while optimising the geometry of the experimental setup. In this paper we present the results of the detailed Monte Carlo simulations we performed, together with the final design of the setup that minimises the residual amount of background particles reaching the detector volume.

Coherent elastic neutrino–nucleus scattering (CEνNS) offers a unique way to study neutrino properties and to search for new physics beyond the Standard Model. Nuclear reactors are promising sources to explore this process at low energies since they deliver large fluxes of anti-neutrinos with typical energies of a few MeV. In this paper, a new-generation experiment to study CEνNS is described. The NUCLEUS experiment will use cryogenic detectors which feature an unprecedentedly low-energy threshold and a time response fast enough to be operated under above-ground conditions. Both sensitivity to low-energy nuclear recoils and a high event rate tolerance are stringent requirements to measuring CEνNS of reactor anti-neutrinos. A new experimental site, the Very-Near-Site (VNS), at the Chooz nuclear power plant in France is described. The VNS is located between the two 4.25 GWth reactor cores and matches the requirements of NUCLEUS. First results of on-site measurements of neutron and muon backgrounds, the expected dominant background contributions, are given. In this paper a preliminary experimental set-up with dedicated active and passive background reduction techniques and first background estimations are presented. Furthermore, the feasibility to operate the detectors in coincidence with an active muon veto at shallow overburden is studied. The paper concludes with a sensitivity study pointing out the physics potential of NUCLEUS at the Chooz nuclear power plant.

Rare-event search experiments located on-surface, such as short-baseline reactor neutrino experiments, are often limited by muon-induced background events. Highly efficient muon vetos are essential to reduce the detector background and to reach the sensitivity goals. We demonstrate the feasibility of deploying organic plastic scintillators at sub-Kelvin temperatures. For the NUCLEUS experiment, we developed a cryogenic muon veto equipped with wavelength shifting fibers and a silicon photo multiplier operating inside a dilution refrigerator. The achievable compactness of cryostat-internal integration is a key factor in keeping the muon rate to a minimum while maximizing coverage. The thermal and light output properties of a plastic scintillation detector were examined. We report first data on the thermal conductivity and heat capacity of the polystyrene-based scintillator UPS-923A over a wide range of temperatures extending below one Kelvin. The light output was measured down to 0.8 K and observed to increase by a factor of 1.61 ± 0.05 compared to 300 K. The development of an organic plastic scintillation muon veto operating in sub-Kelvin temperature environments opens new perspectives for rare-event searches with cryogenic detectors at sites lacking substantial overburden.