Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?

Several theories of early sensory processing suggest that it whitens sensory stimuli. Here, we test three key predictions of the whitening theory using recordings from 152 ganglion cells in salamander retina responding to natural movies. We confirm the previous finding that firing rates of ganglion cells are less correlated compared to natural scenes, although significant correlations remain. We show that while the power spectrum of ganglion cells decays less steeply than that of natural scenes, it is not completely flattened. Finally, we find evidence that only the top principal components of the visual stimulus are transmitted.Comment: 2016 Asilomar Conference on Signals, Systems and Computer

Definitions, methods, and applications in interpretable machine learning.

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods

Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology.

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches

Review: Pharmacological effects of Capparis spinosa L.

Medicinal plants have been known as one of the most important therapeutic agents since ancient times. During the last two decades, much attention has been paid to the health-promoting effects of edible medicinal plants, because of multiple beneficial effects and negligible adverse effects. Capparis spinosa L. is one of the most common medicinal plants, used widely in different parts of the world to treat numerous human diseases. This paper aims to critically review the available scientific literature regarding the health-promoting effects of C. spinosa, its traditional uses, cultivation protocols and phytochemical constituents. Recently, a wide range of evidence has shown that this plant possesses different biological effects, including antioxidant, anticancer and antibacterial effects. Phytochemical analysis shows that C. spinosa has high quantities of bioactive constituents, including polyphenolic compounds, which are responsible for its health-promoting effects, although many of these substances are present in low concentrations and significant changes in their content occur during processing. In addition, there is negligible scientific evidence regarding any adverse effects. Different health promotion activities, as well as tremendous diversity of active constituents, make C. spinosa a good candidate for discovering new drugs. However these findings are still in its infancy and future experimental and clinical studies are needed

Free-Standing Hierarchically Sandwich-Type Tungsten Disulfide Nanotubes/Graphene Anode for Lithium-Ion Batteries

Transition metal dichalcogenides (TMD), analogue of graphene, could form various dimensionalities. Similar to carbon, one dimensional (1D) nanotube of TMD materials has wide application in hydrogen storage，Li-ion batteries and supercapacitors due to their unique structure and properties. Here we demonstrate the feasibility of tungsten disulfide nanotubes (WS2-NTs)/graphene (GS) sandwich-type architecture as anode for lithium-ion batteries for the first time. The graphene based hierarchical architecture plays vital roles in achieving fast electron/ion transfer, thus leading to good electrochemical performance. When evaluated as anode, WS2-NTs /GS hybrid could maintain a capacity of 318.6 mA/g over 500 cycles at a current density of 1A/g. Besides, the hybrid anode does not require any additional polymetric binder, conductive additives or a separate metal current-collector. The relatively high density of this hybrid is beneficial for high capacity per unit volume. Those characteristics make it a potential anode material for light and high performance lithium-ion batteries

Interpretable machine learning: definitions, methods, and applications

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.Comment: 11 page

A new vicious cycle involving glutamate excitotoxicity, oxidative stress and mitochondrial dynamics

Glutamate excitotoxicity leads to fragmented mitochondria in neurodegenerative diseases, mediated by nitric oxide and S-nitrosylation of dynamin-related protein 1, a mitochondrial outer membrane fission protein. Optic atrophy gene 1 (OPA1) is an inner membrane protein important for mitochondrial fusion. Autosomal dominant optic atrophy (ADOA), caused by mutations in OPA1, is a neurodegenerative disease affecting mainly retinal ganglion cells (RGCs). Here, we showed that OPA1 deficiency in an ADOA model influences N-methyl-D-aspartate (NMDA) receptor expression, which is involved in glutamate excitotoxicity and oxidative stress. Opa1enu/+ mice show a slow progressive loss of RGCs, activation of astroglia and microglia, and pronounced mitochondrial fission in optic nerve heads as found by electron tomography. Expression of NMDA receptors (NR1, 2A, and 2B) in the retina of Opa1enu/+ mice was significantly increased as determined by western blot and immunohistochemistry. Superoxide dismutase 2 (SOD2) expression was significantly decreased, the apoptotic pathway was activated as Bax was increased, and phosphorylated Bad and BcL-xL were decreased. Our results conclusively demonstrate that not only glutamate excitotoxicity and/or oxidative stress alters mitochondrial fission/fusion, but that an imbalance in mitochondrial fission/fusion in turn leads to NMDA receptor upregulation and oxidative stress. Therefore, we propose a new vicious cycle involved in neurodegeneration that includes glutamate excitotoxicity, oxidative stress, and mitochondrial dynamics

Early prediction of cardiac resynchronization therapy response by non-invasive electrocardiogram markers

[EN] Cardiac resynchronization therapy (CRT) is an effective treatment for those patients with severe heart failure. Regrettably, there are about one third of CRT "non-responders", i.e. patients who have undergone this form of device therapy but do not respond to it, which adversely affects the utility and cost-effectiveness of CRT. In this paper, we assess the ability of a novel surface ECG marker to predict CRT response. We performed a retrospective exploratory study of the ECG previous to CRT implantation in 43 consecutive patients with ischemic (17) or non-ischemic (26) cardiomyopathy. We extracted the QRST complexes (consisting of the QRS complex, the S-T segment, and the T wave) and obtained a measure of their energy by means of spectral analysis. 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