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The molecular basis of serpin polymerization The serpins are a family of proteins that can multimerize via β-sheet linkages. Accumulation of such multimers can give rise to diseases such as thrombosis, cirrhosis and dementia. While the structures of many serpins are known, the structure of the linkage between monomers was unclear. In this work, Huntington and colleagues have solved the crystal structure of an antithrombin dimer. They find that the high stability of the serpin polymer is due to a large domain swap between beta sheets of the neighbouring monomers. In addition, the structure explains the how certain pathogenic mutations stabilize a polymerogenic folding intermediate. Repeating intermolecular protein association by means of β-sheet expansion is the mechanism underlying a multitude of diseases including Alzheimer’s, Huntington’s and Parkinson’s and the prion encephalopathies 1 . A family of proteins, known as the serpins, also forms large stable multimers by ordered β-sheet linkages leading to intracellular accretion and disease 2 . These ‘serpinopathies’ include early-onset dementia caused by mutations in neuroserpin, liver cirrhosis and emphysema caused by mutations in α 1 -antitrypsin (α 1 AT), and thrombosis caused by mutations in antithrombin 3 . Serpin structure and function are quite well understood, and the family has therefore become a model system for understanding the β-sheet expansion disorders collectively known as the conformational diseases 4 . To develop strategies to prevent and reverse these disorders, it is necessary to determine the structural basis of the intermolecular linkage and of the pathogenic monomeric state. Here we report the crystallographic structure of a stable serpin dimer which reveals a domain swap of more than 50 residues, including two long antiparallel β-strands inserting in the centre of the principal β-sheet of the neighbouring monomer. This structure explains the extreme stability of serpin polymers, the molecular basis of their rapid propagation, and provides critical new insights into the structural changes which initiate irreversible β-sheet expansion.

Advanced membranes that enable ultrafast water flux while demonstrating anti-biofouling characteristics can facilitate sustainable water/wastewater treatment processes. MXenes, two-dimensional (2D) metal carbides and nitrides, have attracted attention for applications in water/wastewater treatment. In this work, we reported the antibacterial properties of micrometer-thick titanium carbide (Ti 3 C 2 T x ) MXene membranes prepared by filtration on a polyvinylidene fluoride (PVDF) support. The bactericidal properties of Ti 3 C 2 T x modified membranes were tested against Escherichia coli ( E . coli ) and Bacillus subtilis ( B . subtilis ) by bacterial growth on the membrane surface and its exposure to bacterial suspensions. The antibacterial rate of fresh Ti 3 C 2 T x MXene membranes reaches more than 73% against B . subtilis and 67% against E . coli as compared with that of control PVDF, while aged Ti 3 C 2 T x membrane showed over 99% growth inhibition of both bacteria under same conditions. Flow cytometry showed about 70% population of dead and compromised cells after 24 h of exposure of both bacterial strains. The damage of the cell surfaces was also revealed by scanning electron microscopy (SEM) and atomic force microscopy (AFM) analysis, respectively. The demonstrated antibacterial activity of MXene coated membranes against common waterborne bacteria, promotes their potential application as anti-biofouling membrane in water and wastewater treatment processes.

Forest dynamics are the processes of recruitment, growth, death, and turnover of the constituent tree species of the forest community. These processes are driven by disturbances both natural and anthropogenic. McDowell et al. review recent progress in understanding the drivers of forest dynamics and how these are interacting and changing in the context of global climate change. The authors show that shifts in forest dynamics are already occurring, and the emerging pattern is that global forests are tending toward younger stands with faster turnover as old-growth forest with stable dynamics are dwindling.

In tropical tree communities, processes occurring during early life stages play a critical role in shaping forest composition and diversity through differences in species' performance. Predicting the future of tropical forests depends on a solid understanding of the drivers of seedling survival. At the same time, factors determining spatial and temporal patterns of seedling survival can play a large role in permitting species coexistence in diverse communities. Using long-term data on the survival of more than 45 000 seedlings of 238 species in a Neotropical forest, we assessed the relative importance of key abiotic and biotic neighbourhood variables thought to influence individual seedling survival and tested whether species vary significantly in their responses to these variables, consistent with niche differences. At the community level, seedling survival was significantly correlated with plant size, topographic habitat, neighbourhood densities of conspecific seedlings, conspecific and heterospecific trees and annual variation in water availability, in descending order of effect size. Additionally, we found significant variation among species in their sensitivity to light and water availability, as well as in their survival within different topographic habitats, indicating the potential for niche differentiation among species that could allow for species coexistence.

Conspecific negative density-dependent establishment, in which local abundance negatively affects establishment of conspecific seedlings through host-specific enemies, can influence species diversity of plant communities, but the generality of this process is not well understood. We tested the strength of density dependence using the United States Forest Service's Forest Inventory and Analysis database containing 151 species from more than 200,000 forest plots spanning 4,000,000 square kilometers. We found that most species experienced conspecific negative density dependence (CNDD), but there was little effect of heterospecific density. Additionally, abundant species exhibited weaker CNDD than rarer species, and species-rich regions exhibited stronger CNDD than species-poor regions. Collectively, our results provide evidence that CNDD is a pervasive mechanism driving diversity across a gradient from boreal to subtropical forests.

Plant-soil feedbacks are known to play a central role in species co-existence, but conceptual frameworks for predicting their magnitude and direction are lacking. We ask whether co-occurring trees that associate with different types of mycorrhizal fungi, which are hypothesized to differ in terms of nutrient use and plant-soil feedbacks, differ in sapling establishment densities and probability of co-occurrence. Given that ectomycorrhizal (ECM) trees typically have fungal structures that protect roots from pathogens whereas arbuscular mycorrhizal (AM) trees do not, we hypothesized that ECM saplings would be clustered around ECM trees, while AM saplings would be suppressed near AM trees. Most previous studies have focused on seedlings, but here we examine whether the spatial signal is evident in later life stages. We measured the spatial associations of ~ 28,000 trees using point pattern analysis in a 25-ha old-growth forest where ECM trees comprised 72 % of total basal area and 42 % of the total stems, while AM trees comprised the remainder. Supporting our hypothesis, AM saplings were more inhibited by AM trees, while ECM saplings were more clustered around ECM trees. The spatial patterns of AM and ECM trees on saplings of the alternate mycorrhizal type were inhibited. To the extent that similar types of feedbacks occur for other AM and ECM trees, our results suggest that fundamental differences in the nature of local-scale biotic interactions between trees and their fungal symbionts may influence forest community assembly and ecosystem dynamics.

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km 2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.

Factors that control tree seedling dynamics are critical determinants of forest diversity. We examined the role of density-dependent mortality and abiotic factors in the differential establishment and survival of tree seedlings at three, large, mapped forest plots in Indiana, Virginia, and Wisconsin, USA. We tested whether seedling densities and seedling survival are related to local biotic and abiotic factors with generalized linear mixed models. Spatial point pattern analyses were utilized to determine if the distribution patterns of seedlings and saplings are consistent with a pattern generated by negative density-dependent mortality with respect to conspecific trees. Initial sampled seedling density for nearly a third of species showed a positive correlation with increasing conspecific basal area, indicating dispersal limitation, but few had any association with abiotic variables. By contrast, survival of seedlings over one year significantly declined with increasing conspecific basal area. Point pattern analyses indicated that nearly one-third of tree species had significantly over-dispersed point patterns of conspecific seedlings and saplings relative to adult densities; the majority of other species exhibited random spatial arrangements. Our results demonstrate-that negative conspecific density-dependent mortality of seedlings could generate the spatial patterns observed at later life stages. By differentially favoring seedlings of other species, this process may contribute to the maintenance of tree diversity in temperate forests, just as others have demonstrated for tropical forests.

Regulation of blood coagulation is critical for maintaining blood flow, while preventing excessive bleeding or thrombosis. One of the principal regulatory mechanisms involves heparin activation of the serpin antithrombin (AT). Inhibition of several coagulation proteases is accelerated by up to 10 000‐fold by heparin, either through bridging AT and the protease or by inducing allosteric changes in the properties of AT. The anticoagulant effect of short heparin chains, including the minimal AT‐specific pentasaccharide, is mediated exclusively through the allosteric activation of AT towards efficient inhibition of coagulation factors (f) IXa and Xa. Here we present the crystallographic structure of the recognition (Michaelis) complex between heparin‐activated AT and S195A fXa, revealing the extensive exosite contacts that confer specificity. The heparin‐induced conformational change in AT is required to allow simultaneous contacts within the active site and two distinct exosites of fXa (36‐loop and the autolysis loop). This structure explains the molecular basis of protease recognition by AT, and the mechanism of action of the important therapeutic low‐molecular‐weight heparins.