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Tree-ring records can provide valuable information to advance our understanding of contemporary terrestrial carbon cycling and to reconstruct key metrics in the decades preceding monitoring data. The growing use of tree rings in carbon-cycle research is being facilitated by increasing recognition of reciprocal benefits among research communities. Yet, basic questions persist regarding what tree rings represent at the ecosystem level, how to optimally integrate them with other data streams, and what related challenges need to be overcome. It is also apparent that considerable unexplored potential exists for tree rings to refine assessments of terrestrial carbon cycling across a range of temporal and spatial domains. Here, we summarize recent advances and highlight promising paths of investigation with respect to (1) growth phenology, (2) forest productivity trends and variability, (3) CO₂ fertilization and water-use efficiency, (4) forest disturbances, and (5) comparisons between observational and computational forest productivity estimates. We encourage the integration of tree-ring data: with eddy-covariance measurements to investigate carbon allocation patterns and water-use efficiency; with remotely sensed observations to distinguish the timing of cambial growth and leaf phenology; and with forest inventories to develop continuous, annually-resolved and long-term carbon budgets. In addition, we note the potential of tree-ring records and derivatives thereof to help evaluate the performance of earth system models regarding the simulated magnitude and dynamics of forest carbon uptake, and inform these models about growth responses to (non-)climatic drivers. Such efforts are expected to improve our understanding of forest carbon cycling and place current developments into a long-term perspective.

Few studies have assessed the burden of Chagas disease in non-endemic countries and most of them are based on prevalence estimates from Latin American (LA) countries that likely differ from the prevalence in migrants living in Europe. The aim of this study was to systematically review the existing data informing current understanding of the prevalence of Chagas disease in LA migrants living in European countries. We conducted a systematic review and meta-analysis of studies reporting prevalence of Chagas disease in European countries belonging to the European Union (EU) before 2004 in accordance with the MOOSE guidelines and based on the database sources MEDLINE and Global Health. No restrictions were placed on study date, study design or language of publication. The pooled prevalence was estimated using random effect models based on DerSimonian & Laird method. We identified 18 studies conducted in five European countries. The random effect pooled prevalence was 4.2% (95%CI:2.2-6.7%); and the heterogeneity of Chagas disease prevalence among studies was high (I2 = 97%,p<0.001). Migrants from Bolivia had the highest prevalence of Chagas disease (18.1%, 95%CI:13.9-22.7%). Prevalence of Chagas in LA migrants living in Europe is high, particularly in migrants from Bolivia and Paraguay. Data are highly heterogeneous dependent upon country of origin and within studies of migrants from the same country of origin. Country-specific prevalence differs from the estimates available from LA countries. Our meta-analysis provides prevalence estimates of Chagas disease that should be used to estimate the burden of disease in European countries.

Understanding of the role social factors play in chronic pain is growing, with more adaptive and satisfying social relationships helping pain management. During the COVID-19 pandemic, social distancing measures facilitated a naturalistic study of how changes to social interaction affected chronic pain intensity. In a cross-sectional correlational design, questionnaire data was collected over a 38-day period during the March 2020 COVID-19 lockdown, individuals with chronic pain were asked about their current pain experience as well as notable social factors which might relate to pain. Multiple regression analysis revealed social satisfaction significantly predicted pain experience, with a reduction in social participation during COVID-19 lockdowns increasing pain disability, and increased social satisfaction associated with decreasing pain intensity. While pain management often focuses on the functional aspects of pain alleviation, these findings suggest psychological aspects of socialising satisfaction also impact pain experience. Pain management strategies should consider ways to increase social satisfaction in individuals with chronic pain, perhaps by facilitating socialisation in the home using remote communication methods similar to those which became popular during the COVID-19 lockdown.

The growing burden of diabetes worldwide is a threat to tuberculosis (TB) control. Drug-induced liver injury (DILI) due to TB drugs is a significant concern and there is currently limited evidence on the effect of diabetes on TB DILI. This study sought to investigate the effect of diabetes as a risk factor for DILI and to further study any potential co-factors. An unmatched case-control study. Cases were TB patients on 2RHZE/4RH presenting with DILI from 2013-2017 in Porto Alegre, Brazil. Controls were contemporaneous TB patients without DILI being treated in any one of the same five Porto Alegre TB clinics. The exposure variables were diabetes (main exposure variable), age, sex, alcohol misuse, human immunodeficiency virus (HIV), hepatitis C (HCV) and B (HBV) viruses, concomitant hepatotoxic drugs, other liver diseases and TB site. The outcome variable was the occurrence of DILI. Odds of DILI were increased by: older age group 51-60, 61-70 and 71-93 years (adjusted OR 2.39, 95%CI 1.30-4,38; adjusted OR 4.37, 2.28-8,35; adjusted OR 12.91, 5.81-28,66, respectively), HIV positive status (adjusted OR 3.59, 95%CI 2.25-5.73), HCV positive status (adjusted OR 3.49, 95%CI 1.96-6.21) and having concurrent pulmonary and extrapulmonary TB (adjusted OR 3.16, 95%CI 1.93-5.19). Diabetes, gender, and other hepatotoxic drugs were not associated with DILI. This study confirms the association between TB DILI and well-known risk factors but did not demonstrate increased odds of TB DILI in patients with diabetes.

Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.

Rising atmospheric CO₂ and warming spring temperatures increase vegetation growth and the terrestrial carbon sink. However, drought, heat stress, phenology, and resource limitations may stabilize or limit theses projected increases. We investigate the balance between these amplifying and stabilizing ecological factors by asking whether enhanced early-season growth leads to continued late-season growth. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) dataset, we identify three seasonal growth patterns based on early- and peak-season positive LAI anomalies: (1) amplification, where late-season LAI anomalies exceed earlier ones; (2) weak stabilization, where late-season anomalies remain similar or slightly lower; and (3) strong stabilization, where late-season anomalies become negative. Weak and strong stabilization events dominate across 67% and 26% of Northern Hemisphere ecosystems above 30°N, respectively. The absence of any trend in amplifying or stabilizing events suggests stabilizing factors seasonally offset CO₂ and temperature-induced spring greening. Terrestrial biosphere models underestimate strong stabilization and overestimate amplification events. This inconsistency arises from the models’ underestimation late-season LAI sensitivity to precipitation in water-limited regions; overlook negative legacy effects of early enhanced LAI on late-season soil moisture via evapotranspiration losses in energy-limited regions. Our findings suggest water/heat stress and resource limitations limit greening and the land carbon sink. Rising CO₂ and warming enhance vegetation greening, but drought, heat stress, and resource limits constrain this trend. Here, the authors show that within a year, increased early- and peak- season greenness often leads to late-season declines, highlighting water/heat stress limits on greening and the carbon sink.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in the world. AF increases the risk of stroke 5-fold, though the risk can be reduced with appropriate treatment. Therefore, early diagnosis is imperative but remains a global challenge. In low-and middle-income countries (LMICs), a lack of diagnostic equipment and under-resourced healthcare systems generate further barriers. The rapid development of digital technologies that are capable of diagnosing AF remotely and cost-effectively could prove beneficial for LMICs. However, evidence is lacking on what digital technologies exist and how they compare in regards to diagnostic accuracy. We aim to systematically review the diagnostic accuracy of all digital technologies capable of AF diagnosis. MEDLINE, Embase and Web of Science will be searched for eligible studies. Free text terms will be combined with corresponding index terms where available and searches will not be limited by language nor time of publication. Cohort or cross-sectional studies comprising adult (≥18 years) participants will be included. Only studies that use a 12-lead ECG as the reference test (comparator) and report outcomes of sensitivity, specificity, the diagnostic odds ratio (DOR) or the positive and negative predictive value (PPV and NPV) will be included (or if they provide sufficient data to calculate these outcomes). Two reviewers will independently assess articles for inclusion, extract data using a piloted tool and assess risk of bias using the QUADAS-2 tool. The feasibility of a meta-analysis will be determined by assessing heterogeneity across the studies, grouped by index device, diagnostic threshold and setting. If a meta-analysis is feasible for any index device, pooled sensitivity and specificity will be calculated using a random effect model and presented in forest plots. The findings of our review will provide a comprehensive synthesis of the diagnostic accuracy of available digital technologies capable for diagnosing AF. Thus, this review will aid in the identification of which devices could be further trialed and implemented, particularly in a LMIC setting, to improve the early diagnosis of AF. Systematic review registration: PROSPERO registration number is CRD42021290542. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290542.

Recent advances in bacterial whole-genome sequencing have resulted in a comprehensive catalog of antibiotic resistance genomic signatures in Mycobacterium tuberculosis . With a view to pre-empt the emergence of resistance, we hypothesized that pre-existing polymorphisms in susceptible genotypes (pre-resistance mutations) could increase the risk of becoming resistant in the future. We sequenced whole genomes from 3135 isolates sampled over a 17-year period. After reconstructing ancestral genomes on time-calibrated phylogenetic trees, we developed and applied a genome-wide survival analysis to determine the hazard of resistance acquisition. We demonstrate that M. tuberculosis lineage 2 has a higher risk of acquiring resistance than lineage 4, and estimate a higher hazard of rifampicin resistance evolution following isoniazid mono-resistance. Furthermore, we describe loci and genomic polymorphisms associated with a higher risk of resistance acquisition. Identifying markers of future antibiotic resistance could enable targeted therapy to prevent resistance emergence in M. tuberculosis and other pathogens. Signals of antimicrobial resistance in pathogen genomes may be detectable before the organism evolves an antimicrobial resistance phenotype. Here, the authors investigate this hypothesis using Mycobacterium tuberculosis data from Peru and identify candidate “pre-resistance” markers.

Summary Atmospheric carbon dioxide concentration ([CO2]) is increasing, which increases leaf‐scale photosynthesis and intrinsic water‐use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO2] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO2]‐driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO2] (iCO2) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre‐industrial times. Established theory, supported by experiments, indicates that iCO2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2, albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change. See also the Commentary on this article by Way et al., 229: 2383–2385.