Into the unknown: The role of post-fire soil erosion in the carbon cycle.

Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires and highlights the need for more field data to further validate this initial assessment.

PlayMolecule Viewer: A Toolkit for the Visualization of Molecules and Other Data.

PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design efforts. By harnessing state-of-the-art web technologies such as WebAssembly, PlayMolecule Viewer integrates powerful Python libraries directly within the browser environment, which enhances its capabilities to manage multiple types of molecular data. With its intuitive interface, it allows users to easily upload, visualize, select, and manipulate molecular structures and associated data. The toolkit supports a wide range of common structural file formats and offers a variety of molecular representations to cater to different visualization needs. PlayMolecule Viewer is freely accessible at open.playmolecule.org, ensuring accessibility and availability to the scientific community and beyond.

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by ∼5 times and achieve a combined sampling of 1 µs for each complex, marking the longest simulations ever reported for this class of simulations.

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented KDEEP, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning. In this work, we present a new application to visualize the contribution of each input atom to the prediction made by the convolutional neural network, aiding in the interpretability of such predictions. The results suggest that KDEEP is able to learn meaningful chemistry signals from the data, but it has also exposed the inaccuracies of the current model, serving as a guideline for further optimization of our prediction tools.

Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management.

The extreme 2018 hot drought that affected central and northern Europe led to the worst wildfire season in Sweden in over a century. The Ljusdal fire complex, the largest area burnt that year (8995 ha), offered a rare opportunity to quantify the combined impacts of wildfire and post-fire management on Scandinavian boreal forests. We present chamber measurements of soil CO2 and CH4  fluxes, soil microclimate and nutrient content from five Pinus sylvestris sites for the first growing season after the fire. We analysed the effects of three factors on forest soils: burn severity, salvage-logging and stand age. None of these caused significant differences in soil CH4 uptake. Soil respiration, however, declined significantly after a high-severity fire (complete tree mortality) but not after a low-severity fire (no tree mortality), despite substantial losses of the organic layer. Tree root respiration is thus key in determining post-fire soil CO2 emissions and may benefit, along with heterotrophic respiration, from the nutrient pulse after a low-severity fire. Salvage-logging after a high-severity fire had no significant effects on soil carbon fluxes, microclimate or nutrient content compared with leaving the dead trees standing, although differences are expected to emerge in the long term. In contrast, the impact of stand age was substantial: a young burnt stand experienced more extreme microclimate, lower soil nutrient supply and significantly lower soil respiration than a mature burnt stand, due to a thinner organic layer and the decade-long effects of a previous clear-cut and soil scarification. Disturbance history and burn severity are, therefore, important factors for predicting changes in the boreal forest carbon sink after wildfires. The presented short-term effects and ongoing monitoring will provide essential information for sustainable management strategies in response to the increasing risk of wildfire.

Response of Calamagrostis angustifolia to burn frequency and seasonality in the Sanjiang Plain wetlands (Northeast China).

Fire is an important disturbance in many wetlands, which are key carbon reservoirs at both regional and global scales. However, the effects of fire on wetland vegetation biomass and plant carbon dynamics are poorly understood. We carried out a burn experiment in a Calamagrostis angustifolia wetland in Sanjiang Plain (Northeast China), which is widespread wetland type in China and frequently exposed to fire. Using a series of replicated experimental annual burns over a three-year period (spring and autumn burns carried out one, two or three times over three consecutive years), together with a control unburned treatment, we assessed the effect of burn seasonality and frequency on aboveground biomass, stem density, and carbon content of aboveground plant parts and ground litter. We found that burning promoted plant growth and hence plant biomass in burned sites compared to the unburned control, with this effect being greatest after three consecutive burn years. Autumn burns promoted higher stem density and more total aboveground biomass than spring burns after three consecutive burn years. Burning increased stem density significantly, especially in twice and thrice burned plots, with stem densities in September over 2000 N/m2, which was much higher than in the control plots (987 ± 190 N/m2). Autumn burns had a larger effect than spring burns on total plant biomass and litter accumulated (e.g. 1236 ± 295 g/m2 after thrice autumn burns compared 796.2 ± 66.6 g/m2 after thrice spring burns), except after two burn treatments. With time since burning, total biomass loads increased in spring-burned plots, while autumn-burned plots showed the opposite trend, declining towards values found at unburned plots in year three. Our results suggest that, at short fire return intervals, autumn burns lead to a more pronounced increase in aboveground biomass and carbon accumulation than spring burns; however, the effects of spring burns on biomass and carbon accumulation are longer lasting than those observed for autumn burns.

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either density functional theory (DFT) calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery (SBDD). The current version is available at PlayMolecule ( www.playmolecule.org ) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options.

Soil seal development under simulated rainfall: Structural, physical and hydrological dynamics.

This study delivers new insights into rainfall-induced seal formation through a novel approach in the use of X-ray Computed Tomography (CT). Up to now seal and crust thickness have been directly quantified mainly through visual examination of sealed/crusted surfaces, and there has been no quantitative method to estimate this important property. X-ray CT images were quantitatively analysed to derive formal measures of seal and crust thickness. A factorial experiment was established in the laboratory using open-topped microcosms packed with soil. The factors investigated were soil type (three soils: silty clay loam - ZCL, sandy silt loam - SZL, sandy loam - SL) and rainfall duration (2-14 min). Surface seal formation was induced by applying artificial rainfall events, characterised by variable duration, but constant kinetic energy, intensity, and raindrop size distribution. Soil porosities derived from CT scans were used to quantify the thickness of the rainfall-induced surface seals and reveal temporal seal micro-morphological variations with increasing rainfall duration. In addition, the water repellency and infiltration dynamics of the developing seals were investigated by measuring water drop penetration time (WDPT) and unsaturated hydraulic conductivity (Kun). The range of seal thicknesses detected varied from 0.6 to 5.4 mm. Soil textural characteristics and OM content played a central role in the development of rainfall-induced seals, with coarser soil particles and lower OM content resulting in thicker seals. Two different trends in soil porosity vs. depth were identified: i) for SL soil porosity was lowest at the immediate soil surface, it then increased constantly with depth till the median porosity of undisturbed soil was equalled; ii) for ZCL and SL the highest reduction in porosity, as compared to the median porosity of undisturbed soil, was observed in a well-defined zone of maximum porosity reduction c. 0.24-0.48 mm below the soil surface. This contrasting behaviour was related to different dynamics and processes of seal formation which depended on the soil properties. The impact of rainfall-induced surface sealing on the hydrological behaviour of soil (as represented by WDTP and Kun) was rapid and substantial: an average 60% reduction in Kun occurred for all soils between 2 and 9 min rainfall, and water repellent surfaces were identified for SZL and ZCL. This highlights that the condition of the immediate surface of agricultural soils involving rainfall-induced structural seals has a strong impact in the overall ability of soil to function as water reservoir.

Towards a global assessment of pyrogenic carbon from vegetation fires.

The production of pyrogenic carbon (PyC; a continuum of organic carbon (C) ranging from partially charred biomass and charcoal to soot) is a widely acknowledged C sink, with the latest estimates indicating that ~50% of the PyC produced by vegetation fires potentially sequesters C over centuries. Nevertheless, the quantitative importance of PyC in the global C balance remains contentious, and therefore, PyC is rarely considered in global C cycle and climate studies. Here we examine the robustness of existing evidence and identify the main research gaps in the production, fluxes and fate of PyC from vegetation fires. Much of the previous work on PyC production has focused on selected components of total PyC generated in vegetation fires, likely leading to underestimates. We suggest that global PyC production could be in the range of 116-385 Tg C yr(-1) , that is ~0.2-0.6% of the annual terrestrial net primary production. According to our estimations, atmospheric emissions of soot/black C might be a smaller fraction of total PyC (<2%) than previously reported. Research on the fate of PyC in the environment has mainly focused on its degradation pathways, and its accumulation and resilience either in situ (surface soils) or in ultimate sinks (marine sediments). Off-site transport, transformation and PyC storage in intermediate pools are often overlooked, which could explain the fate of a substantial fraction of the PyC mobilized annually. We propose new research directions addressing gaps in the global PyC cycle to fully understand the importance of the products of burning in global C cycle dynamics.

Pyrogenic organic matter production from wildfires: a missing sink in the global carbon cycle.

Wildfires release substantial quantities of carbon (C) into the atmosphere but they also convert part of the burnt biomass into pyrogenic organic matter (PyOM). This is richer in C and, overall, more resistant to environmental degradation than the original biomass, and, therefore, PyOM production is an efficient mechanism for C sequestration. The magnitude of this C sink, however, remains poorly quantified, and current production estimates, which suggest that ~1-5% of the C affected by fire is converted to PyOM, are based on incomplete inventories. Here, we quantify, for the first time, the complete range of PyOM components found in-situ immediately after a typical boreal forest fire. We utilized an experimental high-intensity crown fire in a jack pine forest (Pinus banksiana) and carried out a detailed pre- and postfire inventory and quantification of all fuel components, and the PyOM (i.e., all visually charred, blackened materials) produced in each of them. Our results show that, overall, 27.6% of the C affected by fire was retained in PyOM (4.8 ± 0.8 t C ha(-1)), rather than emitted to the atmosphere (12.6 ± 4.5 t C ha(-1)). The conversion rates varied substantially between fuel components. For down wood and bark, over half of the C affected was converted to PyOM, whereas for forest floor it was only one quarter, and less than a tenth for needles. If the overall conversion rate found here were applicable to boreal wildfire in general, it would translate into a PyOM production of ~100 Tg C yr(-1) by wildfire in the global boreal regions, more than five times the amount estimated previously. Our findings suggest that PyOM production from boreal wildfires, and potentially also from other fire-prone ecosystems, may have been underestimated and that its quantitative importance as a C sink warrants its inclusion in the global C budget estimates.

Quantity, composition and water contamination potential of ash produced under different wildfire severities.

Wildfires frequently threaten water quality through the transfer of eroded ash and soil into rivers and reservoirs. The ability to anticipate risks for water resources from wildfires is fundamental for implementing effective fire preparedness plans and post-fire mitigation measures. Here we present a new approach that allows quantifying the amount and characteristics of ash generated under different wildfire severities and its respective water contamination potential. This approach is applied to a wildfire in an Australian dry sclerophyll eucalypt forest, but can be adapted for use in other environments. The Balmoral fire of October 2013 affected 12,694 ha of Sydney's forested water supply catchment. It produced substantial ash loads that increased with fire severity, with 6, 16 and 34 Mg ha(-1) found in areas affected by low, high and extreme fire severity, respectively. Ash bulk density was also positively related to fire severity. The increase with fire severity in the total load and bulk density of the ash generated is mainly attributed to a combination of associated increases in (i) total amount of fuel affected by fire and (ii) contribution of charred mineral soil to the ash layer. Total concentrations of pollutants and nutrients in ash were mostly unrelated to fire severity and relatively low compared to values reported for wildfire ash in other environments (e.g. 4.0-7.3mg As kg(-1); 2.3-4.1 B mg kg(-1); 136-154 P mg kg(-1)). Solubility of the elements analysed was also low, less than 10% of the total concentration for all elements except for B (6-14%) and Na (30-50%). This could be related to a partial loss of soluble components by leaching and/or wind erosion before the ash sampling (10 weeks after the fire and before major ash mobilisation by water erosion). Even with their relatively low concentrations of potential pollutants, the substantial total ash loads found here represent a water contamination risk if transported into the hydrological network during severe erosion events. For example, up to 4 Mg of ash-derived P could be delivered into a single water supply reservoir.

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for Tensor-Net models, with performance gains ranging from 2x to 10x over previous, non-optimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology.

Soil moisture deficits and water table dynamics are major biophysical controls on peat and non-peat fires in Indonesia. Development of modern fire forecasting models in Indonesia is hampered by the lack of scalable hydrologic datasets or scalable hydrology models that can inform the fire forecasting models on soil hydrologic behaviour. Existing fire forecasting models in Indonesia use weather data-derived fire probability indices, which often do not adequately proxy the sub-surface hydrologic dynamics. Here we demonstrate that soil moisture and water table dynamics can be simulated successfully across tropical peatlands and non-peatland areas by using a process-based eco-hydrology model (ecosys) and publicly available data for weather, soil, and management. Inclusion of these modelled water table depth and soil moisture contents significantly improves the accuracy of a neural network model in predicting active fires at two-weekly time scale. This constitutes an important step towards devising an operational fire early warning system for Indonesia.

Machine learning coarse-grained potentials of protein thermodynamics.

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability.

Ras proteins are membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the properties of Ras in terms of dimerization/clustering on the cell membrane. To understand the effects of anionic lipids on key spatiotemporal properties of dimeric K-Ras4B, we perform all-atom molecular dynamics simulations of the dimer K-Ras4B in the presence and absence of Raf[RBD/CRD] effectors on two model anionic lipid membranes: one containing 78% mol DOPC, 20% mol DOPS, and 2% mol PIP2 and another one with enhanced concentration of anionic lipids containing 50% mol DOPC, 40% mol DOPS, and 10% mol PIP2. Analysis of our results unveils the orientational space of dimeric K-Ras4B and shows that the stability of the dimer is enhanced on the membrane containing a high concentration of anionic lipids in the absence of Raf effectors. This enhanced stability is also observed in the presence of Raf[RBD/CRD] effectors although it is not influenced by the concentration of anionic lipids in the membrane, but rather on the ability of Raf[CRD] to anchor to the membrane. We generate dominant K-Ras4B conformations by Markov state modeling and yield the population of states according to the K-Ras4B orientation on the membrane. For the membrane containing anionic lipids, we observe correlations between the diffusion of K-Ras4B and PIP2 and anchoring of anionic lipids to the Raf[CRD] domain. We conclude that the presence of effectors with the Raf[CRD] domain anchoring on the membrane as well as the membrane composition both influence the conformational stability of the K-Ras4B dimer, enabling the preservation of crucial interface interactions.

Effect of particle size on droplet infiltration into hydrophobic porous media as a model of water repellent soil.

The wettability of soil is of great importance for plants and soil biota, and in determining the risk for preferential flow, surface runoff, flooding,and soil erosion. The molarity of ethanol droplet (MED) test is widely used for quantifying the severity of water repellency in soils that show reduced wettability and is assumed to be independent of soil particle size. The minimum ethanol concentration at which droplet penetration occurs within a short time (≤ 10 s) provides an estimate of the initial advancing contact angle at which spontaneous wetting is expected. In this study, we test the assumption of particle size independence using a simple model of soil, represented by layers of small (~0.2-2 mm) diameter beads that predict the effect of changing bead radius in the top layer on capillary driven imbibition. Experimental results using a three-layer bead system show broad agreement with the model and demonstrate a dependence of the MED test on particle size. The results show that the critical initial advancing contact angle for penetration can be considerably less than 90° and varies with particle size, demonstrating that a key assumption currently used in the MED testing of soil is not necessarily valid.

Wildfire-Derived Pyrogenic Carbon Modulates Riverine Organic Matter and Biofilm Enzyme Activities in an In Situ Flume Experiment.

Wildfires produce large amounts of pyrogenic carbon (PyC), including charcoal, known for its chemical recalcitrance and sorption affinity for organic molecules. Wildfire-derived PyC can be transported to fluvial networks. Here it may alter the dissolved organic matter (DOM) concentration and composition as well as microbial biofilm functioning. Effects of PyC on carbon cycling in freshwater ecosystems remain poorly investigated. Employing in-stream flumes with a control versus treatment design (PyC pulse addition), we present evidence that field-aged PyC inputs to rivers can increase the dissolved organic carbon (DOC) concentration and alter the DOM composition. DOM fluorescence components were not affected by PyC. The in-stream DOM composition was altered due to leaching of pyrogenic DOM from PyC and possibly concurrent sorption of riverine DOM to PyC. Decreased DOM aromaticity indicated by a lower SUVA245 (-0.31 unit) and a higher pH (0.25 unit) was associated with changes in enzymatic activities in benthic biofilms, including a lower recalcitrance index (ß-glucosidase/phenol oxidase), suggesting preferential usage of recalcitrant over readily available DOM by biofilms. The deposition of particulate PyC onto biofilms may further modulate the impacts of PyC due to direct contact with the biofilm matrix. This study highlights the importance of PyC for in-stream biogeochemical organic matter cycling in fire-affected watersheds.