Biology subjects

Lewis, M.

Publications and source records attributed to Lewis, M..

6 recordsLinked to original sources

bioRxiv2018

A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

neuroscience↗

bioRxiv2018

Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells

The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement and predict sensory stimuli after movement. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the objects reference frame. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement.

neuroscience↗

bioRxiv2018

Determinants of accelerated metabolomic and epigenetic ageing in a UK cohort

Markers of biological ageing have potential utility in primary care and public health. We developed an elastic net regression model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum (almost 100,000 features assayed), within a large sample (N=2,239) from the UK occupational Airwave cohort. We investigated the determinants of accelerated ageing, including genetic, lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (r=0.85 in independent test set). Increased metabolomic age acceleration (mAA) was associated (p<0.0025) with overweight/obesity and depression and nominally associated (p<0.05) with high alcohol use and low income. DNA methylation age acceleration (N=1,102) was nominally associated (p<0.05) with high alcohol use, anxiety and post-traumatic stress disorder, but not correlated with mAA. Biological age acceleration may present an important mechanism linking psycho-social stress to age-related disease.

epidemiology↗

bioRxiv2018

M3C: A Monte Carlo reference-based consensus clustering algorithm

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.

bioinformatics↗

bioRxiv2018

GEMC1 and CCNO are required for efferent duct development and male fertility

GEMC1 is a Geminin family protein that triggers the E2F4/5-mediated transcriptional activation of target genes, including many required for multiciliogenesis, such as Mcidas, FoxJ1 and Ccno. Male mice lacking either Gemc1 or Ccno were found to be infertile, but the origin of this defect has remained unclear. Here we show that mice lacking either Gemc1 or Ccno showed a nearly empty seminiferous tubule phenotype. While both genes were expressed in the testes, much higher levels were observed in the efferent ducts that mobilize sperm to the epididymis. We found that both GEMC1 and CCNO were required for the generation of multiciliated cells in the efferent ducts but that they differentially affect activation of FoxJ1 and Tp73. These data indicate that defective efferent duct development, rather than defects in testes development, are likely the primary origin of male infertility observed in the absence of GEMC1 and CCNO and this could extend to Reduced Generation of Multiple Motile Cilia patients with MCIDAS and CCNO mutations.\n\nSummary statementWe demonstrate that male mice lacking GEMC1 or CCNO are infertile due to defects in the formation of the efferent ducts.

developmental biology↗

bioRxiv2017

Reversal of cardiac and skeletal manifestations of Duchenne muscular dystrophy by cardiosphere-derived cells and their exosomes in mdx dystrophic mice and in human Duchenne cardiomyocytes

Genetic deficiency of dystrophin leads to disability and premature death in Duchenne muscular dystrophy, affecting the heart as well as skeletal muscle. Here we report that cardiosphere-derived cells (CDCs), which are being tested clinically for the treatment of Duchenne cardiomyopathy, improve cardiac and skeletal myopathy in the mdx mouse model of DMD and in human Duchenne cardiomyocytes. Injection of CDCs into the hearts of mdx mice augments cardiac function, ambulatory capacity and survival. Exosomes secreted by human CDCs reproduce the benefits of CDCs in mdx mice and in human Duchenne cardiomyocytes. The findings further motivate the testing of CDCs in Duchenne patients, while identifying exosomes as next-generation therapeutic candidates.

physiology↗