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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.

Objectives: To describe a suspected diphtheria outbreak in a Swiss asylum seeker reception centre, and to analyse its management response regarding testing and vaccination.Methods: We retrospectively analysed clinical, microbiology, and case management data of all asylum seekers tested for C. diphtheriae between 28th August and 31st December 2022 while residing at the centre. Results are reported descriptively.Results: Among 265 individuals tested, ten cases of cutaneous diphtheria, one simultaneous respiratory and cutaneous case, and nine respiratory carriers were identified. Mass throat screening, targeted throat testing and targeted wound testing yielded 4.8%, 4.3%, and 17.4% positive results, respectively. No respiratory carrier was identified among cutaneous cases undergoing a throat swab, and no symptomatic case was identified among individuals with unspecific throat symptoms. Rates of vaccination implementation of newly arriving asylum seekers before and after the outbreak were low (17.5% and 15.5%, respectively), as were rates of targeted vaccination among cases and close contacts.Conclusion: We provide evidence for transmission both prior to arrival and within the setting, suboptimal practices and timeliness of testing, and implementation gaps in vaccination.

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 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.

Summary: Background: Treatment with elexacaftor/tezacaftor/ivacaftor (ETI) improves multiple clinical outcomes in people with cystic fibrosis (pwCF) with at least one F508del allele. This study evaluated the real-world impact of ETI on lung function, nutritional status, pulmonary exacerbation frequency, and sweat chloride concentrations in a large group of pwCF. Methods: This observational cohort study used data from the German CF Registry for pwCF who received ETI therapy and were followed up for a period of 12 months. Findings: The study included 2645 pwCF from 67 centres in Germany (mean age 28.0 ± 11.5 years). Over the first year after ETI was initiated, percent predicted forced expiratory volume in 1 s (ppFEV1) increased by 11.3% (95% confidence interval [CI] 10.8–11.8, p

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.

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

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 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.

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.

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.

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.