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Abstract During the 2015–2016 El Niño, the Amazon basin released almost one gigaton of carbon (GtC) into the atmosphere due to extreme temperatures and drought. The link between the drought impact and recovery of the total carbon pools and its biogeochemical drivers is still unknown. With satellite‐constrained net carbon exchange and its component fluxes including gross primary production and fire emissions, we show that the total carbon loss caused by the 2015–2016 El Niño had not recovered by the end of 2018. Forest ecosystems over the Northeastern (NE) Amazon suffered a cumulative total carbon loss of ∼0.6 GtC through December 2018, driven primarily by a suppression of photosynthesis whereas southeastern savannah carbon loss was driven in part by fire. We attribute the slow recovery to the unexpected large carbon loss caused by the severe atmospheric aridity coupled with a water storage deficit during drought. We show the attenuation of carbon uptake is three times higher than expected from the pre‐drought sensitivity to atmospheric aridity and ground water supply. Our study fills an important knowledge gap in our understanding of the unexpectedly enhanced response of carbon fluxes to atmospheric aridity and water storage deficit and its impact on regional post‐drought recovery as a function of the vegetation types and climate perturbations. Our results suggest that the disproportionate impact of water supply and demand could compromise resiliency of the Amazonian carbon balance to future increases in extreme events.

Abstract Tropical cyclones are an important cause of forest disturbance, and major storms caused severe structural damage and elevated tree mortality in coastal tropical forests. Model capabilities that can be used to understand post‐hurricane forest recovery are still limited. We use a vegetation demography model, the Functionally Assembled Terrestrial Ecosystem Simulator, coupled with the Energy Exascale Earth System Model Land Model (ELM‐FATES) to study the processes and the key factors regulating post‐hurricane forest recovery. We implemented hurricane‐induced forest damage, including defoliation, structural biomass reduction, and tree mortality, performed ensemble model simulations, and used random forest feature importance. For the simulation in the Luquillo Experimental Forest, Puerto Rico, we identified factors controlling the post‐hurricane forest recovery, and quantified the sensitivity of key model parameters to the post‐hurricane forest recovery. The results indicate a tendency for the Bisley forests to shift toward the light demanding plant functional type (PFT) when the pre‐hurricane biomass between the light demanding and shade tolerant PFTs is nearly equal and forests experience hurricane disturbance with mortality >60% for both the two PFTs. Under more realistic conditions where the shade tolerant PFT is initially dominant, mortality >80% is required for a shift toward dominance of the light demanding PFT at Bisley. Hurricane mortality and background mortality are the two major factors regulating post‐hurricane forest recovery in simulations. This research improves understanding of the ELM‐FATES model behavior associated with hurricane disturbance and provides guidance for dynamic vegetation model development in representing hurricane induced forest damage with varied intensities.

Abstract The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha−1, a mean of 174 Mg ha−1, and a standard deviation of 102 Mg ha−1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions.

Abstract Background Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well. Results Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites. Conclusions Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

This paper describes a series of endurance and material property tests conducted on a pneumatic, fabric-reinforced inflatable soft actuator made of Dragon Skin 30 silicone, which exhibited performance variations during operation. It is important to understand the level of variation over time and how it affects the motions of the soft actuators. The tests were designed to investigate the repeatability and durability of the actuator by measuring changes in its trajectories after long working periods, determining its failure pressure, and examining its elasticity through tensile tests. The experiments were performed on multiple soft actuators, and the results show pertinent information about the variation in their motion and how it relates to the material behavior of the silicone. This information enhances our understanding of the real-world behavior of silicone soft actuators and enables us to better control their performance in our applications.

BACKGROUND COVID-19 placed a significant burden on the global healthcare system. Strain in critical care capacity has been associated with increased COVID-19-related ICU mortality. This study evaluates the impact of an early warning system and response team implemented on medical floors to safely triage and care for critically ill patients on the floor and preserve ICU capacity. METHODS We conducted a multicenter, retrospective cohort study, comparing outcomes between intervention and control hospitals within a US eight-hospital urban network. Patients hospitalized with COVID-19 pneumonia between April 13 th , 2020 and June 19 th , 2020 were included in the study, which was a time of a regional surge of COVID-19 admissions. An automated, electronic early warning protocol to identify patients with moderate-severe hypoxemia on the medical floors and implement early interventions was implemented at one of the eight hospitals (“the intervention hospital”). RESULTS Among 1024 patients, 403 (39%) were admitted to the intervention hospital and 621 (61%) were admitted to one of the control hospitals. Adjusted for potential confounders, patients at the intervention hospital were less likely to be admitted to the ICU (HR = 0.73, 95% CI 0.53, 1.000, P = .0499) compared to the control hospitals. Patients admitted from the floors to the ICU at the intervention hospital had shorter ICU stay (HR for ICU discharge: 1.74; 95% CI 1.21, 2.51, P = .003). There was no significant difference between intervention and control hospitals in need for mechanical ventilation (OR = 0.93; 95% CI 0.38, 2.31; P = .88) or hospital mortality (OR = 0.79; 95% CI 0.52, 1.18; P = .25). CONCLUSION A protocol to conserve ICU beds by implementing an early warning system with a dedicated response team to manage respiratory distress on the floors reduced ICU admission and was not associated with worse outcomes compared to hospitals that managed similar levels of respiratory distress in the ICU.

Polylactic acid (PLA) is one of the market’s most commonly used biodegradable polymers, with diverse applications in additive manufacturing, specifically fused deposition modeling (FDM) 3D printing. The use of PLA in complex and sophisticated FDM applications is continually growing. However, the increased range of applications requires a better understanding of the material properties of this polymer. For example, recent studies have shown that PLA has the potential to be used in artificial heart valves. Still, the durability and longevity of this material in such a harsh environment are unknown, as heart valve failures have been attributed to salification. Additionally, there is a gap in the field for in situ material characterization of PLA surfaces during stiffening. The present study aims to benchmark different dynamic atomic force microscopy (AFM) techniques available to study the salification phenomenon of PLA at micro-scales using different PLA thin films with various salt concentrations (i.e., 10%, 15%, and 20% of sodium chloride (NaCl)). The measurements are conducted by tapping mode AFM, bimodal AFM, the force spectroscopy technique, and energy quantity analysis. These measurements showed a stiffening phenomenon occurring as the salt solution is increased, but the change was not equally sensitive to material property differences. Tapping mode AFM provided accurate topographical information, while the associated phase images were not considered reliable. On the other hand, bimodal AFM was shown to be capable of providing the topographical information and material compositional mapping through the higher eigenmode’s phase channel. The dissipated power energy quantities indicated that how the polymers become less dissipative as salt concentration increases can be measured. Lastly, it was shown that force spectroscopy is the most sensitive technique in detecting the differences in properties. The comparison of these techniques can provide a helpful guideline for studying the material properties of PLA polymers at micro- and nano-scales that can prove beneficial in various fields.

Abstract Background Obesity has been associated with chronic inflammation and oxidative stress. Both conditions play a determinant role in the pathogenesis of age-related diseases, such as immunosenescence. Adipose tissue can modulate the function of the immune system with the secretion of molecules influencing the phenotype of immune cells. The importance of the bone marrow (BM) in the maintenance of antigen-experienced adaptive immune cells has been documented in mice. Recently, some groups have investigated the survival of effector/memory T cells in the human BM. Despite this, whether high body mass index (BMI) may affect immune cells in the BM and the production of molecules supporting the maintenance of these cells it is unknown. Methods Using flow cytometry, the frequency and the phenotype of immune cell populations were measured in paired BM and PB samples obtained from persons with different BMI. Furthermore, the expression of BM cytokines was assessed. The influence of cytomegalovirus (CMV) on T cell subsets was additionally considered, dividing the donors into the CMV− and CMV+ groups. Results Our study suggests that increased BMI may affect both the maintenance and the phenotype of adaptive immune cells in the BM. While the BM levels of IL-15 and IL-6, supporting the survival of highly differentiated T cells, and oxygen radicals increased in overweight persons, the production of IFNγ and TNF by CD8+ T cells was reduced. In addition, the frequency of B cells and CD4+ T cells positively correlated with BMI in the BM of CMV− persons. Finally, the frequency of several T cell subsets, and the expression of senescence/exhaustion markers within these subpopulations, were affected by BMI. In particular, the levels of bona fide memory T cells may be reduced in overweight persons. Conclusion Our work suggests that, in addition to aging and CMV, obesity may represent an additional risk factor for immunosenescence in adaptive immune cells. Metabolic interventions may help in improving the fitness of the immune system in the elderly.

Although anti-retroviral therapy (ART) is successful in suppressing HIV-1 replication, HIV latently infected reservoirs are not eliminated, representing a major hurdle in efforts to eradicate the virus. Current strategies to eradicate HIV involve two steps: (1) the reactivation of latently infected cells with latency reversing agents (LRAs) to expose persisting HIV, and (2) the elimination of these cells with immune effectors while continuing ART to prevent reinfection. HIV-specific T cells (HSTs) can kill reactivated HIV-infected cells and are currently being evaluated in early-stage immunotherapy trials. HIV can mutate sequences in T cell epitopes and evade T cell-mediated killing of HIV-infected cells. However, by directing T cells to target multiple conserved, non-escaped HIV epitopes, the opportunity for viral escape can be reduced. Using a good manufacturing practice (GMP)-compliant platform, we manufactured HSTs against non-escape epitope targets (HST-NEETs) from HIV+ and HIV-seronegative donors. HST-NEETs expanded to clinically relevant numbers, lysed autologous antigen-pulsed targets, and showed a polyfunctional pro-inflammatory cytokine response. Notably, HST-NEETs recognized multiple conserved, non-escaped HIV epitopes and their common variants. We propose that HST-NEETs could be used to eliminate reactivated virus from latently infected cells in HIV+ individuals following LRA treatment. Additionally, HST-NEETs derived from HIV-negative individuals could be used post-transplant for HIV+ individuals with hematologic malignancies to augment anti-viral immunity and destroy residual infected cells. Keywords: HIV-specific T cells, immunotherapy, latent reservoir, viral escape, conserved epitopes

Abstract Background Antigen-experienced immune cells migrate back to the bone marrow (BM), where they are maintained in BM survival niches for an extended period. The composition of T cell subpopulations in the BM changes with age, leading to an accumulation of highly differentiated T cells and a loss of naïve T cells. While innate immune cells are also affected by age, little is known about interactions between different adaptive immune cell populations maintained in the BM. In this study, the phenotype and function of innate and adaptive immune cells isolated from human BM and peripheral blood (PB) was analysed in detail using flow cytometry, to determine if the accumulation of highly differentiated T and B cells, supported by the BM niches, limits the maintenance of other immune cells, or affects their functions such as providing protective antibody concentrations. Results Total T cells increase in the BM with age, as do highly differentiated CD8+ T cells which no longer express the co-stimulatory molecule CD28, while natural killer T (NKT) cells, monocytes, B cells, and naïve CD8+ T cells all decrease in the BM with age. A negative correlation of total T cells with B cells was observed in the BM. The percentage of B cells in the BM negatively correlated with highly differentiated CD8+CD28− T cells, replicative-senescent CD8+CD57+ T cells, as well as the CD8+CD28−CD57+ population. Similar correlations were seen between B cells and the frequency of highly differentiated T cells producing pro-inflammatory molecules in the BM. Interestingly, plasma concentrations of diphtheria-specific antibodies negatively correlated with highly differentiated CD8+CD57+ T cells as well as with exhausted central memory CD8+ and CD4+ T cells in the BM. A negative impact on diphtheria-specific antibodies was also observed for CD8+ T cells expressing senescence associated genes such as the cell cycle regulator p21 (CDKN1A), KLRG-1, and elevated levels of reactive oxygen species (ROS). Conclusion Our data suggest that the accumulation and maintenance of highly differentiated, senescent, and exhausted T cells in the BM, particularly in old age, may interfere with the survival of other cell populations resident in the BM such as monocytes and B cells, leading to reduced peripheral diphtheria antibody concentrations as a result. These findings further highlight the importance of the BM in the long-term maintenance of immunological memory.

Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)—namely, vegetation height, vegetation cover, and vertical structural complexity—to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.

Fire in tropical forests increases tree mortality, degrades forest structure, and reduces carbon stocks. Currently, there are large gaps in understanding how fire affects understory forest structure and composition, interactions with fire recurrence, and long-term impacts. Understanding these changes is critical to evaluate the present and future response of tropical forests to fire. We studied post-fire changes in understory regeneration in forests in Mato Grosso State, southern Amazonia, Brazil, aiming to answer the following questions: (i) does forest structure (basal area) and tree community composition vary with fire frequency and time since the last fire? (ii) does the response differ among strata (e.g., sapling, larger trees)? (iii) are changes in diversity associated with changes in forest structure? We surveyed trees and lianas in previously structurally intact forests that underwent selective logging, followed by different fire histories, including 5 and 16 years after once-burned, 5 years after three times burned, and unburned (control). Overall, species composition (abundance, richness, and number of families) and diversity were highest for the unburned treatment and lowest for the recurrent burned areas. Fire frequency negatively affected plant structure and basal area; basal area of small, medium, and large plants declined significantly by more than 50% in the most frequently burned areas. Richness was positively related to basal area in the three times burned sites and in the 16 years regenerating site for all strata. Our results demonstrate the negative influence of frequent fires on both the composition and structure of small trees in Amazonian forest. These changes to the cohort of small-sized trees may persist and have long-term impacts on forest structure, affecting the capacity, and direction of forest recovery. With wildfire widespread across the region and increasing in frequency, fire may negatively affect tree diversity in remaining selectively logged forests, and affect regional carbon cycling with consequences for the global vegetation carbon sink.

Forests of the Amazon Basin have experienced frequent and severe droughts in recent years with significant impacts on their carbon cycling. Here, using satellite LiDAR samples from 2003 to 2008, the authors show the long-term legacy of these droughts with persistent loss of carbon stocks after the 2005 drought.

Previous research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different data sources, vegetation types, and modeling algorithms influence AGB estimation is poorly understood. This research conducts a comparative analysis of different data sources and modeling approaches in improving AGB estimation. RapidEye-based spectral responses and textures, lidar-derived metrics, and their combination were used to develop AGB estimation models. The results indicated that (1) overall, RapidEye data are not suitable for AGB estimation, but when AGB falls within 50–150 Mg/ha, support vector regression based on stratification of vegetation types provided good AGB estimation; (2) Lidar data provided stable and better estimations than RapidEye data; and stratification of vegetation types cannot improve estimation; (3) The combination of lidar and RapidEye data cannot provide better performance than lidar data alone; (4) AGB ranges affect the selection of the best AGB models, and a combination of different estimation results from the best model for each AGB range can improve AGB estimation; (5) This research implies that an optimal procedure for AGB estimation for a specific study exists, depending on the careful selection of data sources, modeling algorithms, forest types, and AGB ranges.

Abstract Many currently used vaccines are less immunogenic in the elderly compared to young adults. The impact of latent infection with Cytomegalovirus (CMV) on vaccine-induced antibody responses has been discussed controversially. We have demonstrated that recall responses to diphtheria vaccination are frequently insufficient in elderly persons and that antibody concentrations decline substantially within 5 years. In the current study we show that within a cohort of healthy elderly (n = 87; median age 71 years, range 66–92) antibody responses to a booster vaccination against diphtheria do not differ between CMV-negative and CMV-positive individuals 4 weeks after vaccination.. However, the goal of diphtheria-vaccination is long-term protection and this is achieved by circulating anti-toxin antibodies. Diphtheria-specific antibody concentrations decline faster in CMV-positive compared to CMV-negative older adults leading to an increased proportion of persons without protective antibody concentrations 5 years after booster vaccination and endangering long-term protection. This finding could be relevant for vaccination schedules.

The peripheral T-cell pool undergoes a striking age associated remodeling which is accelerated by progressive insulin resistance. Exercise training is known to delay several aspects of T-cell senescence. The purpose of the current study was to investigate the effect of 3 weeks regular concentric or eccentric endurance exercise training on the composition of the T-cell compartment in pre-diabetic subjects. Sixteen male older adults with impaired glucose tolerance were recruited and performed either concentric exercise (CE) or eccentric exercise (EE) walking 3 times a week for 3 weeks. Fasting venous blood sampling was performed before training and after the training intervention. Various T-cell subpopulations were analyzed by flow cytometry. We did not find significant time × group effects (interaction) but found several significant time effects for cell type ratios and cell subsets proportions. There was an increase of the CD4+/CD8+ (0.55 ± 0.85%; p = 0.033) and CD4+/CD3+ ratio (5.63 ± 8.44%; p = 0.018) and a decrease of the CD8+/CD3+ ratio (-0.95 ± 1.64%; p = 0.049) after training. We found proportional increases of CD4+/CCR7+/CD45RO+ central memory cells (5.02 ± 7.68%; p = 0.030), naïve CD8+/CCR7+/CD45RO- (3.00 ± 6.68%; p = 0.047) and CD8+/CCR7+/CD45RO+ central memory cells (3.01 ± 3.70%; p = 0.009), while proportions of CD4+/CCR7-/CD45RO- TEMRA cells (-2.17 ± 4.66%; p = 0.012), CD8+/CCR7-/CD45RO- TEMRA cells (-5.11 ± 7.02%; p = 0.018) and CD16+ cells (-4.67 ± 6.45%; p = 0.016) decreased after training. 3 weeks of either CE or EE were effective in reversing hallmarks of T-cell senescence in pre-diabetic subjects. It is suggested that exercise stimulates production and mobilization of naïve T-cells, while differentiated TEMRA cells might disappear by apoptosis.

Diurnal moist convection is an important element of summer precipitation over Central Europe and the Alps. It is poorly represented in models using parameterized convection. In this study, we investigate the diurnal cycle of convection during 11 days in June 2007 using the COSMO model. The numerical simulations are compared with satellite measurements of GERB and SEVIRI, AVHRR satellite-based cloud properties and ground-based precipitation and temperature measurements. The simulations use horizontal resolutions of 12 km (convection-parameterizing model, CPM) and 2 km (convection-resolving model, CRM) and either a one-moment microphysics scheme (1M) or a two-moment microphysics scheme (2M).They are conducted for a computational domain that covers an extended Alpine area from Northern Italy to Northern Germany. The CPM with 1M exhibits a significant overestimation of high cloud cover. This results in a compensation effect in the top of the atmosphere energy budget due to an underestimation of outgoing longwave radiation (OLR) and an overestimation of reflected solar radiation (RSR). The CRM reduces high cloud cover and improves the OLR bias from a domain mean of −20.1 to −2.6 W/m2. When using 2M with ice sedimentation in the CRM, high cloud cover is further reduced. The stronger diurnal cycle of high cloud cover and associated convection over the Alps, compared to less mountainous regions, is well represented by the CRM but underestimated by the CPM. Despite substantial differences in high cloud cover, the use of a 2M has no significant impact on the diurnal cycle of precipitation. Furthermore, a negative mid-level cloud bias is found for all simulations.

CD4+ regulatory T cells have been intensively studied during aging, but little is still known about age-related changes of other regulatory T cell subsets. It was, therefore, the goal of the present study to analyze CD8+human leukocyte antigen–antigen D related (HLADR)+ T cells in old age, a cell population reported to have suppressive activity and to be connected to specific genetic variants. We demonstrate a strong increase in the number of CD8+HLADR+ T cells with age in a cohort of female Sardinians as well as in elderly male and female persons from Austria. We also show that CD8+HLADR+ T cells lack classical activation molecules, such as CD69 and CD25, but contain increased numbers of checkpoint inhibitory molecules, such as cytotoxic T lymphocyte-associated antigen 4, T cell immunoglobulin and mucin protein-3, LAG-3, and PD-1, when compared with their HLADR− counterparts. They also have the capacity to inhibit the proliferation of autologous peripheral blood mononuclear cells. This suppressive activity is, however, decreased when CD8+HLADR+ T cells from elderly persons are analyzed. In accordance with this finding, CD8+HLADR+ T cells from persons of old age contain lower percentages of checkpoint inhibitory molecules than young controls. We conclude that in spite of high abundance of a CD8+ regulatory T cell subset in old age its expression of checkpoint inhibitory molecules and its suppressive function on a per cell basis are reduced. Reduction of suppressive capacity may support uncontrolled subclinical inflammatory processes referred to as “inflamm-aging.”

Cerrado is the second largest biome in South America and accounted for the second largest contribution to carbon emissions in Brazil for the last 10 years, mainly due to land-use changes. It comprises approximately 2 million km2 and is divided into 22 ecoregions, based on environmental conditions and vegetation. The most dominant vegetation type is cerrado sensu stricto (cerrado ss), a savanna woodland. Quantifying variation of biomass density of this vegetation is crucial for climate change mitigation policies. Integrating remote sensing data with adequate allometric equations and field-based data sets can provide large-scale estimates of biomass. We developed individual-tree aboveground biomass (AGB) allometric models to compare different regression techniques and explanatory variables. We applied the model with the strongest fit to a comprehensive ground-based data set (77 sites, 893 plots, and 95,484 trees) to describe AGB density variation of cerrado ss. We also investigated the influence of physiographic and climatological variables on AGB density; this analysis was restricted to 68 sites because eight sites could not be classified into a specific ecoregion, and one site had no soil texture data. In addition, we developed two models to estimate plot AGB density based on plot basal area. Our data show that for individual-tree AGB models a) log-log linear models provided better estimates than nonlinear power models; b) including species as a random effect improved model fit; c) diameter at 30 cm above ground was a reliable predictor for individual-tree AGB, and although height significantly improved model fit, species wood density did not. Mean tree AGB density in cerrado ss was 22.9 tons ha-1 (95% confidence interval = ± 2.2) and varied widely between ecoregions (8.8 to 42.2 tons ha-1), within ecoregions (e.g. 4.8 to 39.5 tons ha-1), and even within sites (24.3 to 69.9 tons ha-1). Biomass density tended to be higher in sites close to the Amazon. Ecoregion explained 42% of biomass variation between the 68 sites (P < 0.01) and shows strong potential as a parameter for classifying regional biomass variation in the Cerrado.

Cytomegalovirus (CMV) has been described as a contributor to immunosenescence, thus exacerbating age-related diseases. In persons with latent CMV infection, the CD8+ T cell compartment is irreversibly changed, leading to the accumulation of highly differentiated virus-specific CD8+ T cells in the peripheral blood. The bone marrow (BM) has been shown to play a major role in the long-term survival of antigen-experienced T cells. Effector CD8+ T cells are preferentially maintained by the cytokine IL-15, the expression of which increases in old age. However, the impact of CMV on the phenotype of effector CD8+ T cells and on the production of T cell survival molecules in the BM is not yet known. We now show, using BM samples obtained from persons who underwent hip replacement surgery because of osteoarthrosis, that senescent CD8+ TEMRA cells with a bright expression of CD45RA and a high responsiveness to IL-15 accumulate in the BM of CMV-infected persons. A negative correlation was found between CMV antibody (Ab) titers in the serum and the expression of CD28 and IL-7Rα in CD8+TEMRAbright cells. Increased IL-15 mRNA levels were observed in the BM of CMV+ compared to CMV− persons, being particularly high in old seropositive individuals. In summary, our results indicate that a BM environment rich in IL-15 may play an important role in the maintenance of highly differentiated CD8+ T cells generated after CMV infection.

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon's vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999-2009, and low morning backscatter persisted for 2006-2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts.

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

X-ray spectroscopy of highly charged heavy ions is an important tool for the investigation of many topics in atomic physics. Such highly charged ions, in particular hydrogen-like uranium, are investigated at heavy ion storage rings, where high charge states can be produced in large quantities, stored for long times and cooled to low momentum spread of the ion beam. One prominent example is the determination of the 1s Lamb Shift in hydrogen-like heavy ions, which has been investigated at the Experimental Storage Ring (ESR) at the GSI Helmholtz Centre for Heavy Ion Research. Due to the large electron binding energies, the energies of the corresponding photon transitions are located in the X-ray regime. To determine the transition energies with high accuracy, highly resolving X-ray spectrometers are needed. One concept of such spectrometers is the concept of microcalorimeters, which, in contrast to semiconductor detectors, uses the detection of heat rather than charge to detect energy. Such detectors have been developed and successfully applied in experiments at the ESR. For experiments at the Facility for Antiproton and Ion Research (FAIR), the Stored Particles and Atoms Collaboration (SPARC) pursues the development of new microcalorimeter concepts and larger detector arrays. Next to fundamental investigations on quantum electrodynamics such as the 1s Lamb Shift or electron⁻electron interactions in two- and three-electron systems, X-ray spectroscopy may be extended towards nuclear physics investigations like the determination of nuclear charge radii.

The internal resistance is the key parameter for determining power, energy efficiency and lost heat of a lithium ion cell. Precise knowledge of this value is vital for designing battery systems for automotive applications. Internal resistance of a cell was determined by current step methods, AC (alternating current) methods, electrochemical impedance spectroscopy and thermal loss methods. The outcomes of these measurements have been compared with each other. If charge or discharge of the cell is limited, current step methods provide the same results as energy loss methods.

Despite sustained declines in Amazon deforestation, forest degradation from logging and fire continues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arc of deforestation. Limited data on the magnitude of carbon losses and rates of carbon recovery following forest degradation have hindered carbon accounting efforts and contributed to incomplete national reporting to reduce emissions from deforestation and forest degradation (REDD+). We combined annual time series of Landsat imagery and high-density airborne lidar data to characterize the variability, magnitude, and persistence of Amazon forest degradation impacts on aboveground carbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of the carbon stocks in intact forests, and differences persisted even after 15 years of regrowth. In comparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD. Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a history of one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD, respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires, selective logging before burning did not explain additional variability in modeled ACD loss and recovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure that can aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recovered faster than attributes of canopy structure that are critical for biodiversity in tropical forests, including the abundance of tall trees. We provide the first comprehensive look-up table of emissions factors for specific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon loss and recovery trajectories provide an important foundation for assessing the long-term contributions from forest degradation to regional carbon cycling and advance our understanding of the current state of frontier forests.

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution.

Gap phase dynamics are the dominant mode of forest turnover in tropical forests. However, gap processes are infrequently studied at the landscape scale. Airborne lidar data offer detailed information on three-dimensional forest structure, providing a means to characterize fine-scale (1 m) processes in tropical forests over large areas. Lidar-based estimates of forest structure (top down) differ from traditional field measurements (bottom up), and necessitate clear-cut definitions unencumbered by the wisdom of a field observer. We offer a new definition of a forest gap that is driven by forest dynamics and consistent with precise ranging measurements from airborne lidar data and tall, multi-layered tropical forest structure. We used 1000 ha of multi-temporal lidar data (2008, 2012) at two sites, the Tapajos National Forest and Ducke Reserve, to study gap dynamics in the Brazilian Amazon. Here, we identified dynamic gaps as contiguous areas of significant growth, that correspond to areas > 10 m2, with height

The light brown apple moth, Epiphyas postvittana is a key pest of wine grapes in Australia. Two parasitoids, Dolichogenidea tasmanica and Therophilus unimaculatus, attack the larval stage of this pest. D. tasmanica is dominant in vineyards, whereas T. unimaculatus is mainly active in native vegetation. We sought to understand why they differ in their use of habitats. Plants are a major component of habitats of parasitoids, and herbivore-infested plants influence parasitoid foraging efficiency by their architecture and emission of volatile chemicals. We investigated how different plant species infested by E. postvittana could affect the foraging success of the two parasitoid species in both laboratory and field experiments. Four common host-plant species were selected for this study. In paired-choice experiments to determine the innate foraging preferences for plants, both parasitoid species showed differences in innate search preferences among plant species. The plant preference of D. tasmanica was altered by oviposition experience with hosts that were feeding on other plant species. In a behavioral assay, the two parasitoid species allocated their times engaged in various types of behavior differently when foraging on different plant species. For both parasitoids, parasitism on Hardenbergia violacea was the highest of the four plant species. Significantly more larvae dropped from Myoporum insulare when attacked than from the other three host-plant species, which indicates that parasitism is also affected by interactions between plants and host insects. In vineyards, parasitism by D. tasmanica was significantly lower on M. insulare than on the other three host-plant species, but the parasitism rates were similar among the other three plant species. Our results indicate that plants play a role in the habitat preferences of these two parasitoid species by influencing their foraging behavior, and are likely to contribute to their distributions among habitats.

Changes in tropical-forest structure and aboveground biomass (AGB) contribute directly to atmospheric changes in CO 2 , which, in turn, bear on global climate. This paper demonstrates the capability of radar-interferometric phase-height time series at X-band (wavelength = 3 cm) to monitor changes in vertical structure and AGB, with sub-hectare and monthly spatial and temporal resolution, respectively. The phase-height observation is described, with a focus on how it is related to vegetation-density, radar-power vertical profiles, and mean canopy heights, which are, in turn, related to AGB. The study site covers 18 × 60 km in the Tapajós National Forest in the Brazilian Amazon. Phase-heights over Tapajós were measured by DLR’s TanDEM-X radar interferometer 32 times in a 3.2 year period from 2011–2014. Fieldwork was done on 78 secondary and primary forest plots. In the absence of disturbance, rates of change of phase-height for the 78 plots were estimated by fitting the phase-heights to time with a linear model. Phase-height time series for the disturbed plots were fit to the logistic function to track jumps in phase-height. The epochs of clearing for the disturbed plots were identified with ≈1-month accuracy. The size of the phase-height change due to disturbance was estimated with ≈2-m accuracy. The monthly time resolution will facilitate REDD+ monitoring. Phase-height rates of change were shown to correlate with LiDAR RH90 height rates taken over a subset of the TanDEM-X data’s time span (2012–2013). The average rate of change of phase-height across all 78 plots was 0.5 m-yr - 1 with a standard deviation of 0.6 m-yr - 1 . For 42 secondary forest plots, the average rate of change of phase-height was 0.8 m-yr - 1 with a standard deviation of 0.6 m-yr - 1 . For 36 primary forest plots, the average phase-height rate was 0.1 m-yr - 1 with a standard deviation of 0.5 m-yr - 1 . A method for converting phase-height rates to AGB-rates of change was developed using previously measured phase-heights and field-estimated AGB. For all 78 plots, the average AGB-rate was 1.7 Mg-ha - 1 -yr - 1 with a standard deviation of 4.0 Mg-ha - 1 -yr - 1 . The secondary-plot average AGB-rate was 2.1 Mg-ha - 1 -yr - 1 , with a standard deviation of 2.4 Mg-ha - 1 -yr - 1 . For primary plots, the AGB average rate was 1.1 Mg-ha - 1 -yr - 1 with a standard deviation of 5.2 Mg-ha - 1 -yr - 1 . Given the standard deviations and the number of plots in each category, rates in secondary forests and all forests were significantly different from zero; rates in primary forests were consistent with zero. AGB-rates were compared to change models for Tapajós and to LiDAR-based change measurements in other tropical forests. Strategies for improving AGB dynamical monitoring with X-band interferometry are discussed.

Fire is one of the main factors directly impacting Amazonian forest biomass and dynamics. Because of Amazonia’s large geographical extent, remote sensing techniques are required for comprehensively assessing forest fire impacts at the landscape level. In this context, Light Detection and Ranging (LiDAR) stands out as a technology capable of retrieving direct measurements of vegetation vertical arrangement, which can be directly associated with aboveground biomass. This work aims, for the first time, to quantify post-fire changes in forest canopy height and biomass using airborne LiDAR in western Amazonia. For this, the present study evaluated four areas located in the state of Acre, called Rio Branco, Humaitá, Bonal and Talismã. Rio Branco and Humaitá burned in 2005 and Bonal and Talismã burned in 2010. In these areas, we inventoried a total of 25 plots (0.25 ha each) in 2014. Humaitá and Talismã are located in an open forest with bamboo and Bonal and Rio Branco are located in a dense forest. Our results showed that even ten years after the fire event, there was no complete recovery of the height and biomass of the burned areas (p < 0.05). The percentage difference in height between control and burned sites was 2.23% for Rio Branco, 9.26% for Humaitá, 10.03% for Talismã and 20.25% for Bonal. All burned sites had significantly lower biomass values than control sites. In Rio Branco (ten years after fire), Humaitá (nine years after fire), Bonal (four years after fire) and Talismã (five years after fire) biomass was 6.71%, 13.66%, 17.89% and 22.69% lower than control sites, respectively. The total amount of biomass lost for the studied sites was 16,706.3 Mg, with an average loss of 4176.6 Mg for sites burned in 2005 and 2890 Mg for sites burned in 2010, with an average loss of 3615 Mg. Fire impact associated with tree mortality was clearly detected using LiDAR data up to ten years after the fire event. This study indicates that fire disturbance in the Amazon region can cause persistent above-ground biomass loss and subsequent reduction of forest carbon stocks. Continuous monitoring of burned forests is required for depicting the long-term recovery trajectory of fire-affected Amazonian forests.

Introduction. In this era of high-tech medicine, it is becoming increasingly important to assess patient satisfaction. There are several methods to do so, but these differ greatly in terms of cost, time, and labour and external validity. The aim of this study is to describe and compare the structure and implementation of different methods to assess the satisfaction of patients in an emergency department. Methods. The structure and implementation of the different methods to assess patient satisfaction were evaluated on the basis of a 90-minute standardised interview. Results. We identified a total of six different methods in six different hospitals. The average number of patients assessed was 5012, with a range from 230 (M5) to 20 000 patients (M2). In four methods (M1, M3, M5, and M6), the questionnaire was composed by a specialised external institute. In two methods, the questionnaire was created by the hospital itself (M2, M4).The median response rate was 58.4% (range 9–97.8%). With a reminder, the response rate increased by 60% (M3). Conclusion. The ideal method to assess patient satisfaction in the emergency department setting is to use a patient-based, in-emergency department-based assessment of patient satisfaction, planned and guided by expert personnel.

The impairment of immune functions in the elderly (immunosenescence) results in post-vaccination antibody titers that are significantly lower than in young individuals. It is, however, a controversial question whether also the quality of antibodies declines with age. In this study, we have therefore investigated the age-dependence of functional characteristics of antibody responses induced by vaccination with an inactivated flavivirus vaccine against tick-borne encephalitis (TBE). For this purpose, we quantified TBE virus-specific IgG and neutralizing antibody titers in post-vaccination sera from groups of young and elderly healthy adults and determined antibody avidities and NT/ELISA titer ratios (functional activity). In contrast to the quantitative impairment of antibody production in the elderly, we found no age-related differences in the avidity and functional activity of antibodies induced by vaccination, which also appeared to be independent of the age at primary immunization. There was no correlation between antibody avidity and NT/ELISA ratios suggesting that additional factors affect the quality of polyclonal responses, independent of age. Our work indicates that healthy elderly people are able to produce antibodies in response to vaccination with similar avidity and functional activity as young individuals, albeit at lower titers.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)—height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.