Parks Canada’s adaptation framework and workshop approach: Lessons learned across a diverse series of adaptation workshops

In 2017, the Canadian Parks Council Climate Change Working Group, a team of federal, provincial, and territorial representatives, developed a Climate Change Adaptation Framework for Parks and Protected Areas, guiding practitioners through a simple, effective five-step adaptation process. This framework was adapted by Parks Canada into a two-day adaptation workshop approach, with 11 workshops subsequently held from September 2017 to May 2019 at Parks Canada sites in the Yukon, Quebec, Manitoba, Alberta, Nova Scotia, British Columbia, Newfoundland, and Ontario. Lessons learned from each workshop have been integrated into the approach, with the development of tools and guidance for each phase of the process, and a shareable, visual “placemat” that describes each step of the framework, acting as a map for those navigating the process

Satellite Image Time Series Semantic Change Detection: Novel Architecture and Analysis of Domain Shift

Satellite imagery plays a crucial role in monitoring changes happening on Earth's surface and aiding in climate analysis, ecosystem assessment, and disaster response. In this paper, we tackle semantic change detection with satellite image time series (SITS-SCD) which encompasses both change detection and semantic segmentation tasks. We propose a new architecture that improves over the state of the art, scales better with the number of parameters, and leverages long-term temporal information. However, for practical use cases, models need to adapt to spatial and temporal shifts, which remains a challenge. We investigate the impact of temporal and spatial shifts separately on global, multi-year SITS datasets using DynamicEarthNet and MUDS. We show that the spatial domain shift represents the most complex setting and that the impact of temporal shift on performance is more pronounced on change detection than on semantic segmentation, highlighting that it is a specific issue deserving further attention

Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans

We propose an unsupervised method for parsing large 3D scans of real-world scenes into interpretable parts. Our goal is to provide a practical tool for analyzing 3D scenes with unique characteristics in the context of aerial surveying and mapping, without relying on application-specific user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical shapes. Our model provides an interpretable reconstruction of complex scenes and leads to relevant instance and semantic segmentations. To demonstrate the usefulness of our results, we introduce a novel dataset of seven diverse aerial LiDAR scans. We show that our method outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our method offers significant advantage over existing approaches, as it does not require any manual annotations, making it a practical and efficient tool for 3D scene analysis. Our code and dataset are available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parse

Sparsity regularization via tree-structured environments for disentangled representations

Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal representation learning -- the task of correctly mapping low-level observations to latent causal variables -- could advance scientific understanding by enabling inference of latent variables such as pathway activation. In this paper, we develop methods for inferring latent variables from multiple related datasets (environments) and tasks. As a running example, we consider the task of predicting a phenotype from gene expression, where we often collect data from multiple cell types or organisms that are related in known ways. The key insight is that the mapping from latent variables driven by gene expression to the phenotype of interest changes sparsely across closely related environments. To model sparse changes, we introduce Tree-Based Regularization (TBR), an objective that minimizes both prediction error and regularizes closely related environments to learn similar predictors. We prove that under assumptions about the degree of sparse changes, TBR identifies the true latent variables up to some simple transformations. We evaluate the theory empirically with both simulations and ground-truth gene expression data. We find that TBR recovers the latent causal variables better than related methods across these settings, even under settings that violate some assumptions of the theory

PhyloGFN: Phylogenetic inference with generative flow networks

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods

Multisensory gaze stabilization in response to subchronic alteration of vestibular type I hair cells

The functional complementarity of the vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) allows for optimal combined gaze stabilization responses (CGR) in light. While sensory substitution has been reported following complete vestibular loss, the capacity of the central vestibular system to compensate for partial peripheral vestibular loss remains to be determined. Here, we first demonstrate the efficacy of a 6-week subchronic ototoxic protocol in inducing transient and partial vestibular loss which equally affects the canal- and otolith-dependent VORs. Immunostaining of hair cells in the vestibular sensory epithelia revealed that organ-specific alteration of type I, but not type II, hair cells correlates with functional impairments. The decrease in VOR performance is paralleled with an increase in the gain of the OKR occurring in a specific range of frequencies where VOR normally dominates gaze stabilization, compatible with a sensory substitution process. Comparison of unimodal OKR or VOR versus bimodal CGR revealed that visuo-vestibular interactions remain reduced despite a significant recovery in the VOR. Modeling and sweep-based analysis revealed that the differential capacity to optimally combine OKR and VOR correlates with the reproducibility of the VOR responses. Overall, these results shed light on the multisensory reweighting occurring in pathologies with fluctuating peripheral vestibular malfunction

Sparsity regularization via tree-structured environments for disentangled representations

Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal representation learning -- the task of correctly mapping low-level observations to latent causal variables -- could advance scientific understanding by enabling inference of latent variables such as pathway activation. In this paper, we develop methods for inferring latent variables from multiple related datasets (environments) and tasks. As a running example, we consider the task of predicting a phenotype from gene expression, where we often collect data from multiple cell types or organisms that are related in known ways. The key insight is that the mapping from latent variables driven by gene expression to the phenotype of interest changes sparsely across closely related environments. To model sparse changes, we introduce Tree-Based Regularization (TBR), an objective that minimizes both prediction error and regularizes closely related environments to learn similar predictors. We prove that under assumptions about the degree of sparse changes, TBR identifies the true latent variables up to some simple transformations. We evaluate the theory empirically with both simulations and ground-truth gene expression data. We find that TBR recovers the latent causal variables better than related methods across these settings, even under settings that violate some assumptions of the theory

Bacteria isolated from lung modulate asthma susceptibility in mice

Asthma is a chronic, non-curable, multifactorial disease with increasing incidence in industrial countries. This study evaluates the direct contribution of lung microbial components in allergic asthma in mice. Germ-Free and Specific-Pathogen-Free mice display similar susceptibilities to House Dust Mice-induced allergic asthma, indicating that the absence of bacteria confers no protection or increased risk to aeroallergens. In early life, allergic asthma changes the pattern of lung microbiota, and lung bacteria reciprocally modulate aeroallergen responsiveness. Primo-colonizing cultivable strains were screened for their immunoregulatory properties following their isolation from neonatal lungs. Intranasal inoculation of lung bacteria influenced the outcome of allergic asthma development: the strain CNCM I 4970 exacerbated some asthma features whereas the pro-Th1 strain CNCM I 4969 had protective effects. Thus, we confirm that appropriate bacterial lung stimuli during early life are critical for susceptibility to allergic asthma in young adults

Ship-board determination of whole-rock (ultra-)trace element concentrations by laser ablation-inductively coupled plasma mass spectrometry analysis of pressed powder pellets aboard the D/V Chikyu

The Oman Drilling Project (OmanDP), performed under the International Continental Scientific Drilling Program (ICDP), is an international scientific research project that undertook drilling at a range of sites in the Semail ophiolite (Oman) to collect core samples spanning the stratigraphy of the ophiolite, from the upper oceanic crust down to the basal thrust. The cores were logged to International Ocean Discovery Program (IODP) standards aboard the D/V Chikyu. During ChikyuOman2018 Leg 3 (July-August 2018), participants described cores from the crust-mantle transition (CM) sites. The main rock types recovered at these sites were gabbros, dunites and harzburgites, rocks typically forming the base of the oceanic crust and the shallow mantle beneath present-day spreading centres. In addition to the core description, selected samples were analysed by X-ray fluorescence spectrometry (XRF) for their chemical compositions, including major, minor and some trace elements. To complement these standard procedures, we developed new approaches to measure ultra-trace element concentrations using a procedure adapted from previous works to prepare fine-grained pressed powder pellets coupled with laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS) analysis using instrumentation aboard the D/V Chikyu. First, three (ultra)mafic reference materials were investigated to test and validate our procedure (BHVO-2, BIR-1a and JP-1), and then the procedure was applied to a selection of gabbro and dunite samples from the CM cores to explore the limitations of the method in its current stage of development. The obtained results are in good agreement with preferred values for the reference materials and with subsequent solution replicate analyses of the same samples performed in shore-based laboratories following Leg 3 for the CM samples. We describe this procedure for the determination of 37 minor and (ultra-)trace elements (transition elements and Ga, Li and Large-Ion Lithophile Elements (LILE), Rare Earth Elements (REE), High-Field-Strength Elements (HFSE), U, Th, and Pb) in mafic and ultramafic rocks. The presented method has the major advantage that it allows the determination at sea of the (ultra-)trace element concentrations in a "dry", safe way, without using acid reagents. Our new approach could be extended for other elements of interest and/or be improved to be adapted to other rock materials during future ocean drilling operations aboard the D/V Chikyu and other platforms.This research used samples and/or data provided by the Oman Drilling Project. The Oman Drilling Project (OmanDP) has been possible through co-mingled funds from the International Continental Scientific Drilling Project (ICDP; Peter B. Kelemen, Juerg Matter, Damon A. H. Teagle Lead PIs), the Sloan Foundation – Deep Carbon Observatory (grant no. 2014-3-01, Kele- men PI), the National Science Foundation (grant no. NSF-EAR- 1516300, Kelemen lead PI), NASA – Astrobiology Institute (grant no. NNA15BB02A, Templeton PI), the German Research Founda-tion (DFG: grant no. KO 1723/21-1, Koepke PI), the Japanese Society for the Promotion of Science (JSPS (grant no. 16H06347), Michibayashi PI; and KAKENHI (grant no. 16H02742), Takazawa PI), the European Research Council (Adv: grant no. 669972; Jamveit PI), the Swiss National Science Foundation (SNF: grant no. 20FI21_163073, Früh-Green PI), JAMSTEC, the TAMU-JR Science Operator, and contributions from the Sultanate of Oman Ministry of Regional Municipalities and Water Resources, the Oman Public Authority of Mining, Sultan Qaboos University, CNRS- Univ. Montpellier, Columbia University of New York, and the University of Southampton. Mathieu Rospabé’s participation in onsite and shipboard operations was made possible through a financial support provided by the Centre National de la Recherche Scientifique-Institut National des Sciences de l’Univers (CNRSINSU), IODP-France (regular fund

Paradigms of Lung Microbiota Functions in Health and Disease, Particularly, in Asthma

Improvements in our knowledge of the gut microbiota have broadened our vision of the microbes associated with the intestine. These microbes are essential actors and protectors of digestive and extra-digestive health and, by extension, crucial for human physiology. Similar reconsiderations are currently underway concerning the endogenous microbes of the lungs, with a shift in focus away from their involvement in infections toward a role in physiology. The discovery of the lung microbiota was delayed by the long-held view that the lungs of healthy individuals were sterile and by sampling difficulties. The lung microbiota has a low density, and the maintenance of small numbers of bacteria seems to be a critical determinant of good health. This review aims to highlight how knowledge about the lung microbiota can change our conception of lung physiology and respiratory health. We provide support for this point of view with knowledge acquired about the gut microbiota and intestinal physiology. We describe the main characteristics of the lung microbiota and its functional impact on lung physiology, particularly in healthy individuals, after birth, but also in asthma. We describe some of the physiological features of the respiratory tract potentially favoring the installation of a dysbiotic microbiota. The gut microbiota feeds and matures the intestinal epithelium and is involved in immunity, when the principal role of the lung microbiota seems to be the orientation and balance of aspects of immune and epithelial responsiveness. This implies that the local and remote effects of bacterial communities are likely to be determinant in many respiratory diseases caused by viruses, allergens or genetic deficiency. Finally, we discuss the reciprocal connections between the gut and lungs that render these two compartments inseparable