Generally caused by extreme events, landslides cause severe landscape modifications and may endanger local population. It is important to be able to map them in order to better understand landscape evolution. 3D LiDAR point clouds (PCs) are a relevant choice compared to 2D imagery to directly sense ground shape modification under vegetated areas. Most of the studies propose to rely on rasterization of PCs, or multistep semi-automatic process with tedious manual results refinement in the 3D PCs. In this study, we aim at experimenting a deep learning method to directly extract landslide sources and deposits from raw 3D PCs. To this end, we train an Encoder Fusion SiamKPConv network, designed for 3D PCs change detection, for the specific task of landslides identification in PCs acquired before and after Kaikōura earthquake (New-Zealand). The experimental results (93.87% of accuracy) show the relevance of this model.

Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.

MetaNetX.org is a website for accessing, analysing and manipulating genome-scale metabolic networks (GSMs) as well as biochemical pathways. It consistently integrates data from various public resources and makes the data accessible in a standardized format using a common namespace. Currently, it provides access to hundreds of GSMs and pathways that can be interactively compared (two or more), analysed (e.g. detection of dead-end metabolites and reactions, flux balance analysis or simulation of reaction and gene knockouts), manipulated and exported. Users can also upload their own metabolic models, choose to automatically map them into the common namespace and subsequently make use of the website's functionality.

This paper reports on a study to examine the similarities and differences in experiencing an interpersonal scenario with real and virtual humans. A system that allows medical students to interview a life-size virtual patient using natural speech and gestures was used as a platform for this comparison. Study participants interviewed either a virtual patient or a standardized patient, an actor trained to represent a medical condition. Subtle yet substantial differences were found in the participants’ rapport with the patient and the flow of the conversation. The virtual patient’s limited expressiveness was a significant source of these differences. However, overall task performance was similar, as were perceptions of the educational value of the interaction.