« The Othello Syndrome » de Uri Caine

Il n’y a pas de prélude, l’opéra commence immédiatement allegro agitato sur un rapide glissando de cordes menant à un formidable coup de tonnerre qui ébranle la salle. Le rideau s’ouvre à la troisième mesure sur un port de Chypre. Une violente tempête fait rage ! Un chœur attend avec impatience le retour d’Otello, général de la flotte vénitienne et gouverneur de l’île. (Ola ! Bougez pas !) L’action se passe à Chypre. On est sur le terrain d’emblée ! Vous le savez, tout n’est pas simple à Chyp..

Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models

Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology. However, the interpretability of these approaches is still limited. In particular, they do not report whether high-attention regions are positively or negatively associated with the class labels or how well these regions correspond to previously established clinical and biological knowledge. We address this by introducing a post-training methodology to analyse MIL models. Firstly, we introduce prediction-attention-weighted (PAW) maps by combining tile-level attention and prediction scores produced by a refined encoder, allowing us to quantify the predictive contribution of high-attention regions. Secondly, we introduce a biological feature instantiation technique by integrating PAW maps with nuclei segmentation masks. This further improves interpretability by providing biologically meaningful features related to the cellular organisation of the tissue and facilitates comparisons with known clinical features. We illustrate the utility of our approach by comparing PAW maps obtained for prostate cancer diagnosis (i.e. samples containing malignant tissue, 381/516 tissue samples) and prognosis (i.e. samples from patients with biochemical recurrence following surgery, 98/663 tissue samples) in a cohort of patients from the international cancer genome consortium (ICGC UK Prostate Group). Our approach reveals that regions that are predictive of adverse prognosis do not tend to co-locate with the tumour regions, indicating that non-cancer cells should also be studied when evaluating prognosis

Ontogenetic trajectories of body coloration reveal its function as a multicomponent nonsenescent signal

The understanding of developmental patterns of body coloration is challenging because of the multicomponent nature of color signals and the multiple selective pressures acting upon them, which further depend on the sex of the bearer and area of display. Pigmentary colors are thought to be strongly involved in sexual selection, while structural colors are thought to generally associate with conspecifics interactions and improve the discrimination of pigmentary colors. Yet, it remains unclear whether age dependency in each color component is consistent with their potential function. Here, we address lifelong ontogenetic variation in three color components (i.e. UV, pigmentary, and skin background colors) in a birth cohort of common lizards Zootoca vivipara across three ventral body regions (i.e. throat, chest, and belly). All three color components developed sexual dichromatism, with males displaying stronger pigmentary and UV colors but weaker skin background coloration than females. The development of color components led to a stronger sexual dichromatism on the concealed ventral region than on the throat. No consistent signs of late‐life decay in color components were found except for a deceleration of UV reflectance increase with age on the throat of males. These results suggest that body color components in common lizards are primarily nonsenescent sexual signals, but that the balance between natural and sexual selection may be altered by the conspicuousness of the area of display. These results further support the view that skin coloration is a composite trait constituted of multiple color components conveying multiple signals depending on age, sex, and body location

Hijacking of the Pleiotropic Cytokine Interferon-γ by the Type III Secretion System of Yersinia pestis

Yersinia pestis, the causative agent of bubonic plague, employs its type III secretion system to inject toxins into target cells, a crucial step in infection establishment. LcrV is an essential component of the T3SS of Yersinia spp, and is able to associate at the tip of the secretion needle and take part in the translocation of anti-host effector proteins into the eukaryotic cell cytoplasm. Upon cell contact, LcrV is also released into the surrounding medium where it has been shown to block the normal inflammatory response, although details of this mechanism have remained elusive. In this work, we reveal a key aspect of the immunomodulatory function of LcrV by showing that it interacts directly and with nanomolar affinity with the inflammatory cytokine IFNγ. In addition, we generate specific IFNγ mutants that show decreased interaction capabilities towards LcrV, enabling us to map the interaction region to two basic C-terminal clusters of IFNγ. Lastly, we show that the LcrV-IFNγ interaction can be disrupted by a number of inhibitors, some of which display nanomolar affinity. This study thus not only identifies novel potential inhibitors that could be developed for the control of Yersinia-induced infection, but also highlights the diversity of the strategies used by Y. pestis to evade the immune system, with the hijacking of pleiotropic cytokines being a long-range mechanism that potentially plays a key role in the severity of plague

Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time‐series data

Abstract Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions non‐parametrically from time‐series data. NODEs, however, are slow to fit, and inferred interactions usually are not compared with the ground truth. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We compare these results against three existing approaches for estimating ecological interactions, standard NODEs, ODE models and convergent cross‐mapping (CCM). We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate and rotifer population, in the hare and lynx system, and the Maizuru Bay community featuring 11 species. Our BNGM approach allows us to reduce the fitting time of NODE systems to only a few seconds and provides accurate estimates of ecological interactions in the artificial system, as true ecological interactions are recovered with minimal error. Our benchmark analysis reveals that our approach is both faster and more accurate than standard NODEs and parametric ODEs, while CCM was found to be faster but less accurate. The analysis of the replicated time series reveals that only the strongest interactions are consistent across replicates, while the analysis of the Maizuru community shows the strong negative impact of the chameleon goby on most species of the community, and a potential indirect negative effect of temperature by favouring goby population growth. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting to larger systems, cross‐validation and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models

Neural ordinary differential equations for ecological and evolutionary time‐series analysis

Inferring the functional shape of ecological and evolutionary processes from time-series data can be challenging because processes are often not describable with simple equations. The dynamical coupling between variables in time series further complicates the identification of equations through model selection as the inference of a given process is contingent on the accurate depiction of all other processes. We present a novel method, neural ordinary differential equations (NODEs), for learning ecological and evolutionary processes from time-series data by modelling dynamical systems as ordinary differential equations and dynamical functions with artificial neural networks (ANNs). Upon successful training, the ANNs converge to functional shapes that best describe the biological processes underlying the dynamics observed, in a way that is robust to mathematical misspecifications of the dynamical model. We demonstrate NODEs in a population dynamic context and show how they can be used to infer ecological interactions, dynamical causation and equilibrium points. We tested NODEs by analysing well-understood hare and lynx time-series data, which revealed that prey–predator oscillations were mainly driven by the interspecific interaction, as well as intraspecific densitydependence, and characterised by a single equilibrium point at the centre of the oscillation. Our approach is applicable to any system that can be modelled with differential equations, and particularly suitable for linking ecological, evolutionary and environmental dynamics where parametric approaches are too challenging to implement, opening new avenues for theoretical and empirical investigations

CamilleLeclerc/FoodWebsLakeStream: First release of FoodWebsLakeStream

Data and R code to explore the individual and interactive effects of enrichment and temperature on food-web structure in lakes and streams - Bonnaffé et al. (in prep