Identification of Putative Cytoskeletal Protein Homologues in the Protozoan Host \u3cem\u3eHartmannella vermiformis\u3c/em\u3e as Substrates for Induced Tyrosine Phosphatase Activity Upon Attachment to the Legionnaires\u27 Disease Bacterium, \u3cem\u3eLegionella pneumophila\u3c/em\u3e

The Legionnaires\u27 disease bacterium, Legionella pneumophila, is a facultative intracellular pathogen that invades and replicates within two evolutionarily distant hosts, free living protozoa and mammalian cells. Invasion and intracellular replication within protozoa are thought to be major factors in the transmission of Legionnaires\u27 disease. We have recently reported the identification of a galactose/N-acetyl-d-galactosamine (Gal/GalNAc) lectin in the protozoan host Hartmannella vermiformis as a receptor for attachment and invasion by L. pneumophila (Venkataraman, C., B.J. Haack, S. Bondada, and Y.A. Kwaik. 1997. J. Exp. Med. 186:537–547). In this report, we extended our studies to the effects of bacterial attachment and invasion on the cytoskeletal proteins of H. vermiformis. We first identified the presence of many protozoan cytoskeletal proteins that were putative homologues to their mammalian counterparts, including actin, pp125FAK, paxillin, and vinculin, all of which were basally tyrosine phosphorylated in resting H. vermiformis. In addition to L. pneumophila–induced tyrosine dephosphorylation of the lectin, bacterial attachment and invasion was associated with tyrosine dephosphorylation of paxillin, pp125FAK, and vinculin, whereas actin was minimally affected. Inhibition of bacterial attachment to H. vermiformis by Gal or GalNAc monomers blocked bacteria-induced tyrosine dephosphorylation of detergent-insoluble proteins. In contrast, inhibition of bacterial invasion but not attachment failed to block bacteria-induced tyrosine dephosphorylation of H. vermiformis proteins. This was further supported by the observation that 10 mutants of L. pneumophila that were defective in invasion of H. vermiformis were capable of inducing tyrosine dephosphorylation of H. vermiformis proteins. Entry of L. pneumophila into H. vermiformis was predominantly mediated by noncoated receptor-mediated endocytosis (93%) but coiling phagocytosis was infrequently observed (7%). We conclude that attachment but not invasion by L. pneumophila into H. vermiformis was sufficient and essential to induce protein tyrosine dephosphorylation in H. vermiformis. These manipulations of host cell processes were associated with, or followed by, entry of the bacteria by a noncoated receptor-mediated endocytosis. A model for attachment and entry of L. pneumophila into H. vermiformis is proposed

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km <sup>2</sup> resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km <sup>2</sup> pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Dust in Supernovae and Supernova Remnants II: Processing and survival

Observations have recently shown that supernovae are efficient dust factories, as predicted for a long time by theoretical models. The rapid evolution of their stellar progenitors combined with their efficiency in precipitating refractory elements from the gas phase into dust grains make supernovae the major potential suppliers of dust in the early Universe, where more conventional sources like Asymptotic Giant Branch (AGB) stars did not have time to evolve. However, dust yields inferred from observations of young supernovae or derived from models do not reflect the net amount of supernova-condensed dust able to be expelled from the remnants and reach the interstellar medium. The cavity where the dust is formed and initially resides is crossed by the high velocity reverse shock which is generated by the pressure of the circumstellar material shocked by the expanding supernova blast wave. Depending on grain composition and initial size, processing by the reverse shock may lead to substantial dust erosion and even complete destruction. The goal of this review is to present the state of the art about processing and survival of dust inside supernova remnants, in terms of theoretical modelling and comparison to observations

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets

Prediction of kinetic parameters from body mounted IMU data using recurrent neural networks

Vertical ground reaction force (GRFz) is an excellent parameter for assessing lameness in horses, but cumbersome to obtain. Predicting GRFz using inertial sensors (IMU) information would solve this problem. This study compares GRFz curves and peak-GRFz values with predictions of GRFz from long short-term memory neural networks (LSTM), using IMU data. Twenty-four healthy horses, with IMU on the upper body (UB) and on each limb, were trotted on an instrumented treadmill. Measuring systems were time synchronised. Randomly extracted data from 16, 4, and 4 horses formed training, validation, and test datasets, respectively. LSTM with different input sets (all sensors (ALL), UB, withers, sacrum, or limbs only) were trained to predict GRFz curves and peak-GRFz. Peak-GRFz were also extracted from the predicted curves and calculated using the duty-factor method (DF), based on limb IMU signals. The best GRFz predictions were obtained with the ALL dataset, with mean RMSE of 0.37±0.04 (front limbs) and 0.29±0.03 (hind limbs). For peak-GRFz, the best results were obtained with extracted values from the predicted curves by the ALL dataset, with mean RMSE of 0.62±0.14 (front limbs) and 0.50±0.11 (hind limbs). Predicted peak-GRFz values with the ALL dataset had RMSE of 0.80±0.24 (front limbs) and 0.61±0.13 (hind limbs), while the DF had RMSE of 1.62±0.21 and 2.21±0.21. These results show the potential of machine learning for equine quantitative locomotion analysis. More data are needed to confirm the usability of LSTM for GRFz prediction, which is highly dependent on individual and environmental factors like speed, gait, and lameness

Prediction of kinetic parameters from body mounted IMU data using recurrent neural networks

Vertical ground reaction force (GRFz) is an excellent parameter for assessing lameness in horses, but cumbersome to obtain. Predicting GRFz using inertial sensors (IMU) information would solve this problem. This study compares GRFz curves and peak-GRFz values with predictions of GRFz from long short-term memory neural networks (LSTM), using IMU data. Twenty-four healthy horses, with IMU on the upper body (UB) and on each limb, were trotted on an instrumented treadmill. Measuring systems were time synchronised. Randomly extracted data from 16, 4, and 4 horses formed training, validation, and test datasets, respectively. LSTM with different input sets (all sensors (ALL), UB, withers, sacrum, or limbs only) were trained to predict GRFz curves and peak-GRFz. Peak-GRFz were also extracted from the predicted curves and calculated using the duty-factor method (DF), based on limb IMU signals. The best GRFz predictions were obtained with the ALL dataset, with mean RMSE of 0.37±0.04 (front limbs) and 0.29±0.03 (hind limbs). For peak-GRFz, the best results were obtained with extracted values from the predicted curves by the ALL dataset, with mean RMSE of 0.62±0.14 (front limbs) and 0.50±0.11 (hind limbs). Predicted peak-GRFz values with the ALL dataset had RMSE of 0.80±0.24 (front limbs) and 0.61±0.13 (hind limbs), while the DF had RMSE of 1.62±0.21 and 2.21±0.21. These results show the potential of machine learning for equine quantitative locomotion analysis. More data are needed to confirm the usability of LSTM for GRFz prediction, which is highly dependent on individual and environmental factors like speed, gait, and lameness