Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice.

The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity

2022 Update of the consensus on the rational use of antithrombotics and thrombolytics in Veterinary Critical Care (CURATIVE) Domain 6: Defining rational use of thrombolytics

Objectives To systematically review available evidence and establish guidelines related to the use of thrombolytics for the management of small animals with suspected or confirmed thrombosis. Design PICO (Population, Intervention, Control, and Outcome) questions were formulated, and worksheets completed as part of a standardized and systematic literature evaluation. The population of interest included dogs and cats (considered separately) and arterial and venous thrombosis. The interventions assessed were the use of thrombolytics, compared to no thrombolytics, with or without anticoagulants or antiplatelet agents. Specific protocols for recombinant tissue plasminogen activator were also evaluated. Outcomes assessed included efficacy and safety. Relevant articles were categorized according to level of evidence, quality, and as to whether they supported, were neutral to, or opposed the PICO questions. Conclusions from the PICO worksheets were used to draft guidelines, which were subsequently refined via Delphi surveys undertaken by the Consensus on the Rational Use of Antithrombotics and Thrombolytics in Veterinary Critical Care (CURATIVE) working group. Results Fourteen PICO questions were developed, generating 14 guidelines. The majority of the literature addressing the PICO questions in dogs is experimental studies (level of evidence 3), thus providing insufficient evidence to determine if thrombolysis improves patient-centered outcomes. In cats, literature was more limited and often neutral to the PICO questions, precluding strong evidence-based recommendations for thrombolytic use. Rather, for both species, suggestions are made regarding considerations for when thrombolytic drugs may be considered, the combination of thrombolytics with anticoagulant or antiplatelet drugs, and the choice of thrombolytic agent. Conclusions Substantial additional research is needed to address the role of thrombolytics for the treatment of arterial and venous thrombosis in dogs and cats. Clinical trials with patient-centered outcomes will be most valuable for addressing knowledge gaps in the field

The debiased Whittle likelihood

The Whittle likelihood is a widely used and computationally efficient pseudolikelihood. However, it is known to produce biased parameter estimates with finite sample sizes for large classes of models. We propose a method for debiasing Whittle estimates for second-order stationary stochastic processes. The debiased Whittle likelihood can be computed in the same O(n log n) operations as the standard Whittle approach. We demonstrate the superior performance of our method in simulation studies and in application to a large-scale oceanographic dataset, where in both cases the debiased approach reduces bias by up to two orders of magnitude, achieving estimates that are close to those of the exact maximum likelihood, at a fraction of the computational cost. We prove that the method yields estimates that are consistent at an optimal convergence rate of n(-1/2) for Gaussian processes and for certain classes of non-Gaussian or nonlinear processes. This is established under weaker assumptions than in the standard theory, and in particular the power spectral density is not required to be continuous in frequency. We describe how the method can be readily combined with standard methods of bias reduction, such as tapering and differencing, to further reduce bias in parameter estimates

What’s the Point: Semantic Segmentation with Point Supervision

The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain, image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of 12.9% mIOU over image-level supervision. Further, we demonstrate that models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget.Comment: ECCV (2016) submissio

Somnotate: a probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"—a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics

Genetic signatures of variation in population size in a native fungal pathogen after the recent intensive plantation of its host tree

Historical fluctuations in forests’ distribution driven by past climate changes and anthropogenic activities can have large impacts on the demographic history of pathogens that have a long co-evolution history with these host trees. Using a population genetic approach, we investigated that hypothesis by reconstructing the demographic history of Armillaria ostoyae, one of the major pathogens of the maritime pine (Pinus pinaster), in the largest monospecific pine planted forest in Europe (south-western France). Genetic structure analyses and approximate Bayesian computation approaches revealed that a single pathogen population underwent a severe reduction in effective size (12 times lower) 1080–2080 generations ago, followed by an expansion (4 times higher) during the last 4 generations. These results are consistent with the history of the maritime pine forest in the region characterized by a strong recession during the last glaciation (~19 000 years ago) and massive plantations during the second half of the nineteenth century. Results suggest that recent and intensive plantations of a host tree population have offered the opportunity for a rapid spread and adaptation of their pathogens

Corrosion behavior of friction stir welded lap joints of AA6061-T6 aluminum alloy

In this work, the corrosion behaviors of friction-stir lap welding of 6061-T6 Al-alloy are studied. The friction-stir lap welding was performed under different welding conditions (rotation speed and welding speed). The corrosion behavior of the parent alloy, the weld nugget zone (WNZ), and the heat affected zone (HAZ) of each welded sample working as an electrode, were investigated by the Tafel polarization test in 3.5 wt. (%) NaCl at ambient temperature. The morphology of the corroded surface of each region was analyzed by scanning electron microscopy together with energy dispersive spectroscopy (SEM-EDS). The results showed that the corrosion resistance of the parent alloy was better than the WNZ and the HAZ in both welding conditions. Localized pit dissolution and intergranular corrosion were the dominant corrosion types observed in the parent alloy, WNZ, and HAZ. The parent alloy, WNZ, and HAZ exhibited similar corrosion potentials (Ecorr) after T6 heat treatment. This treatment had a better effect on the corrosion resistance of the welded regions than the parent alloy

Sparse Kernel Learning for Image Annotation

In this paper we introduce a sparse kernel learning frame-work for the Continuous Relevance Model (CRM). State-of-the-art image annotation models linearly combine evidence from several different feature types to improve image anno-tation accuracy. While previous authors have focused on learning the linear combination weights for these features, there has been no work examining the optimal combination of kernels. We address this gap by formulating a sparse kernel learning framework for the CRM, dubbed the SKL-CRM, that greedily selects an optimal combination of ker-nels. Our kernel learning framework rapidly converges to an annotation accuracy that substantially outperforms a host of state-of-the-art annotation models. We make two surprising conclusions: firstly, if the kernels are chosen correctly, only a very small number of features are required so to achieve superior performance over models that utilise a full suite of feature types; and secondly, the standard default selection of kernels commonly used in the literature is sub-optimal, and it is much better to adapt the kernel choice based on the feature type and image dataset

Elementos de historia de la ciencia

Astronomía y física en Platón / Pablo Melogno -- Ciencia y método en Aristóteles / Elena Diez de la Cortina Montemayor -- Los elementos de Euclides y el desarrollo de la matemática griega / Pablo Melogno -- La teoría planetaria de Claudio Ptolomeo / Christián C. Carmen -- Ciencia y filosofía en la Edad Media : la disputa entre razón y fe / Margarita Santana de la Cruz -- De la alquimia a la química / Soledad Esteban Santos -- Leonardo Da Vinci : un estudio de la unidad de su pensamiento y su lugar en la historia de la ciencia / Adriana Assandri -- Conceptos fundamentales de la teoría copernicana / Marina Camejo -- Galileo Galilei : evidencia experimental matemáticamente analizada en la filosofía natural de principios del siglo XVII / Godfrey Guillaumin -- J. Kepler (1571-1630) : la creatividad y el rigor en la búsqueda de la armonía del mundo / Inmaculada Perdomo Reyes -- Antonie-Laurent Lavoisier (1743?-1794) y la química del siglo XVIII / Inés Pellón González -- Darwin y el evolucionismo / José María Adrover -- El problema del V postulado y el surgimiento de las geometrías no euclidianas / Pablo Rodríguez -- Einstein y la reinvención de la física / Leonardo Moledo, Nicolás Olszevicki, Esteban Magnani -- Los inicios de la física cuántica y el problema de su interpretación / Pablo Rodríguez -- La teoría del big bang en la red del conocimiento / Hernán Miguel