Forest structure and individual tree inventories of northeastern Siberia along climatic gradients

We compile a data set of forest surveys from expeditions to the northeast of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia), and the Chukotka Autonomous Okrug (59–73∘ N, 97–169∘ E), performed between the years 2011 and 2021. The region is characterized by permafrost soils and forests dominated by larch (Larix gmelinii Rupr. and Larix cajanderi Mayr). Our data set consists of a plot database describing 226 georeferenced vegetation survey plots and a tree database with information about all the trees on these plots. The tree database, consisting of two tables with the same column names, contains information on the height, species, and vitality of 40 289 trees. A subset of the trees was subject to a more detailed inventory, which recorded the stem diameter at base and at breast height, crown diameter, and height of the beginning of the crown. We recorded heights up to 28.5 m (median 2.5 m) and stand densities up to 120 000 trees per hectare (median 1197 ha−1), with both values tending to be higher in the more southerly areas. Observed taxa include Larix Mill., Pinus L., Picea A. Dietr., Abies Mill., Salix L., Betula L., Populus L., Alnus Mill., and Ulmus L. In this study, we present the forest inventory data aggregated per plot. Additionally, we connect the data with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of 10 species groups, allometries depended also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here. The data can be used for studying the forest structure of northeastern Siberia and for the calibration and validation of remotely sensed data. They are available at https://doi.org/10.1594/PANGAEA.943547 (Miesner et al., 2022).</p

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation

ZMYND10 Is Mutated in Primary Ciliary Dyskinesia and Interacts with LRRC6

Defects of motile cilia cause primary ciliary dyskinesia (PCD), characterized by recurrent respiratory infections and male infertility. Using whole-exome resequencing and high-throughput mutation analysis, we identified recessive biallelic mutations in ZMYND10 in 14 families and mutations in the recently identified LRRC6 in 13 families. We show that ZMYND10 and LRRC6 interact and that certain ZMYND10 and LRRC6 mutations abrogate the interaction between the LRRC6 CS domain and the ZMYND10 C-terminal domain. Additionally, ZMYND10 and LRRC6 colocalize with the centriole markers SAS6 and PCM1. Mutations in ZMYND10 result in the absence of the axonemal protein components DNAH5 and DNALI1 from respiratory cilia. Animal models support the association between ZMYND10 and human PCD, given that zmynd10 knockdown in zebrafish caused ciliary paralysis leading to cystic kidneys and otolith defects and that knockdown in Xenopus interfered with ciliogenesis. Our findings suggest that a cytoplasmic protein complex containing ZMYND10 and LRRC6 is necessary for motile ciliary function

Methylation matters: binding of Ets-1 to the demethylated Foxp3 gene contributes to the stabilization of Foxp3 expression in regulatory T cells

The forkhead-box protein P3 (Foxp3) is a key transcription factor for the development and suppressive activity of regulatory T cells (Tregs), a T cell subset critically involved in the maintenance of self-tolerance and prevention of over-shooting immune responses. However, the transcriptional regulation of Foxp3 expression remains incompletely understood. We have previously shown that epigenetic modifications in the CpG-rich Treg-specific demethylated region (TSDR) in the Foxp3 locus are associated with stable Foxp3 expression. We now demonstrate that the methylation state of the CpG motifs within the TSDR controls its transcriptional activity rather than a Treg-specific transcription factor network. By systematically mutating every CpG motif within the TSDR, we could identify four CpG motifs, which are critically determining the transcriptional activity of the TSDR and which serve as binding sites for essential transcription factors, such as CREB/ATF and NF-κB, which have previously been shown to bind to this element. The transcription factor Ets-1 was here identified as an additional molecular player that specifically binds to the TSDR in a demethylation-dependent manner in vitro. Disruption of the Ets-1 binding sites within the TSDR drastically reduced its transcriptional enhancer activity. In addition, we found Ets-1 bound to the demethylated TSDR in ex vivo isolated Tregs, but not to the methylated TSDR in conventional CD4+ T cells. We therefore propose that Ets-1 is part of a larger protein complex, which binds to the TSDR only in its demethylated state, thereby restricting stable Foxp3 expression to the Treg lineage

Association of a TNFSF13B (BAFF) regulatory region single nucleotide polymorphism with response to rituximab in antineutrophil cytoplasmic antibody–associated vasculitis

Rituximab is effective at inducing and maintaining remission in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). The wide interpatient variability in the duration of B-cell depletion and time to relapse as well as the significant relapse risk after treatment, costs, and adverse event rates necessitate improved patient stratification.This study was supported by the National Institute of Health Research Cambridge Biomedical Research Centre (http://www.cambridge-brc.org.uk). F.A. has been supported by a European Renal Association-European Dialysis and Transplant Association long-term fellowship between September 2012 and September 2013. A.V. and D.M. were supported by the grant “A tailored approach to the immune monitoring and clinical management of viral and autoimmune diseases,” given by the Regione Emilia-Romagna within the Programma di Ricerca Regione-Università 2010–12

An intercomparison study of four different techniques for measuring the chemical composition of nanoparticles

Currently, the complete chemical characterization of nanoparticles (< 100 nm) represents an analytical challenge, since these particles are abundant in number but have negligible mass. Several methods for particle-phase characterization have been recently developed to better detect and infer more accurately the sources and fates of sub-100 nm particles, but a detailed comparison of different approaches is missing. Here we report on the chemical composition of secondary organic aerosol (SOA) nanoparticles from experimental studies of α-pinene ozonolysis at −50, −30, and −10 ∘C and intercompare the results measured by different techniques. The experiments were performed at the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN). The chemical composition was measured simultaneously by four different techniques: (1) thermal desorption–differential mobility analyzer (TD–DMA) coupled to a NO$^-_3$ chemical ionization–atmospheric-pressure-interface–time-of-flight (CI–APi–TOF) mass spectrometer, (2) filter inlet for gases and aerosols (FIGAERO) coupled to an I$^−$ high-resolution time-of-flight chemical ionization mass spectrometer (HRToF-CIMS), (3) extractive electrospray Na$^+$ ionization time-of-flight mass spectrometer (EESI-TOF), and (4) offline analysis of filters (FILTER) using ultra-high-performance liquid chromatography (UHPLC) and heated electrospray ionization (HESI) coupled to an Orbitrap high-resolution mass spectrometer (HRMS). Intercomparison was performed by contrasting the observed chemical composition as a function of oxidation state and carbon number, by estimating the volatility and comparing the fraction of volatility classes, and by comparing the thermal desorption behavior (for the thermal desorption techniques: TD–DMA and FIGAERO) and performing positive matrix factorization (PMF) analysis for the thermograms. We found that the methods generally agree on the most important compounds that are found in the nanoparticles. However, they do see different parts of the organic spectrum. We suggest potential explanations for these differences: thermal decomposition, aging, sampling artifacts, etc. We applied PMF analysis and found insights of thermal decomposition in the TD–DMA and the FIGAERO

High Gas-Phase Methanesulfonic Acid Production in the OH-Initiated Oxidation of Dimethyl Sulfide at Low Temperatures

Dimethyl sulfide (DMS) influences climate via cloud condensation nuclei (CCN) formation resulting from its oxidation products (mainly methanesulfonic acid, MSA, and sulfuric acid, H2SO4). Despite their importance, accurate prediction of MSA and H2SO4from DMS oxidation remains challenging. With comprehensive experiments carried out in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at CERN, we show that decreasing the temperature from +25 to -10 °C enhances the gas-phase MSA production by an order of magnitude from OH-initiated DMS oxidation, while H2SO4production is modestly affected. This leads to a gas-phase H2SO4-to-MSA ratio (H2SO4/MSA) smaller than one at low temperatures, consistent with field observations in polar regions. With an updated DMS oxidation mechanism, we find that methanesulfinic acid, CH3S(O)OH, MSIA, forms large amounts of MSA. Overall, our results reveal that MSA yields are a factor of 2-10 higher than those predicted by the widely used Master Chemical Mechanism (MCMv3.3.1), and the NOxeffect is less significant than that of temperature. Our updated mechanism explains the high MSA production rates observed in field observations, especially at low temperatures, thus, substantiating the greater importance of MSA in the natural sulfur cycle and natural CCN formation. Our mechanism will improve the interpretation of present-day and historical gas-phase H2SO4/MSA measurements

High Gas-Phase Methanesulfonic Acid Production in the OH-Initiated Oxidation of Dimethyl Sulfide at Low Temperatures

Dimethyl sulfide (DMS) influences climate via cloud condensation nuclei (CCN) formation resulting from its oxidation products (mainly methanesulfonic acid, MSA, and sulfuric acid, H$_{2}$SO$_{4}$). Despite their importance, accurate prediction of MSA and H$_{2}$SO$_{4}$ from DMS oxidation remains challenging. With comprehensive experiments carried out in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at CERN, we show that decreasing the temperature from +25 to −10 °C enhances the gas-phase MSA production by an order of magnitude from OH-initiated DMS oxidation, while H$_{2}$SO$_{4}$ production is modestly affected. This leads to a gas-phase H$_{2}$SO$_{4}$-to-MSA ratio (H$_{2}$SO$_{4}$/MSA) smaller than one at low temperatures, consistent with field observations in polar regions. With an updated DMS oxidation mechanism, we find that methanesulfinic acid, CH$_{3}$S(O)OH, MSIA, forms large amounts of MSA. Overall, our results reveal that MSA yields are a factor of 2–10 higher than those predicted by the widely used Master Chemical Mechanism (MCMv3.3.1), and the NO$_{x}$ effect is less significant than that of temperature. Our updated mechanism explains the high MSA production rates observed in field observations, especially at low temperatures, thus, substantiating the greater importance of MSA in the natural sulfur cycle and natural CCN formation. Our mechanism will improve the interpretation of present-day and historical gas-phase H$_{2}$SO$_{4}$/MSA measurements