Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal

Medial prefrontal cortex compresses concept representations through learning

Prefrontal cortex (PFC) is thought to support the ability to focus on goal-relevant information by filtering out irrelevant information, a process akin to dimensionality reduction. Here, we find direct evidence of goal-directed data compression within medial PFC during learning, such that the degree of neural compression predicts an individual's ability to selectively attend to concept-specific information. These findings suggest a domaingeneral mechanism of learning through compression in mPFC

Building concepts one episode at a time: The hippocampus and concept formation

Concepts organize our experiences and allow for meaningful inferences in novel situations. Acquiring new concepts requires extracting regularities across multiple learning experiences, a process formalized in mathematical models of learning. These models posit a computational framework that has increasingly aligned with the expanding repertoire of functions associated with the hippocampus. Here, we propose the Episodes-to-Concepts (EpCon) theoretical model of hippocampal function in concept learning and review evidence for the hippocampal computations that support concept formation including memory integration, attentional biasing, and memory-based prediction error. We focus on recent studies that have directly assessed the hippocampal role in concept learning with an innovative approach that combines computational modeling and sophisticated neuroimaging measures. Collectively, this work suggests that the hippocampus does much more than encode individual episodes; rather, it adaptively transforms initially-encoded episodic memories into organized conceptual knowledge that drives novel behavior

CoQ10 Deficient Endothelial Cell Culture Model for the Investigation of CoQ10 Blood–Brain Barrier Transport

Primary coenzyme Q10 (CoQ10) deficiency is unique among mitochondrial respiratory chain disorders in that it is potentially treatable if high-dose CoQ10 supplements are given in the early stages of the disease.While supplements improve peripheral abnormalities, neurological symptoms are only partially or temporarily ameliorated. The reasons for this refractory response to CoQ10 supplementation are unclear, however, a contributory factor may be the poor transfer of CoQ10 across the blood–brain barrier (BBB). The aim of this study was to investigate mechanisms of CoQ10 transport across the BBB, using normal and pathophysiological (CoQ10 deficient) cell culture models. The study identifies lipoprotein-associated CoQ10 transcytosis in both directions across the in vitro BBB. Uptake via SR-B1 (Scavenger Receptor) and RAGE (Receptor for Advanced Glycation Endproducts), is matched by efflux via LDLR (Low Density Lipoprotein Receptor) transporters, resulting in no “net” transport across the BBB. In the CoQ10 deficient model, BBB tight junctions were disrupted and CoQ10 “net” transport to the brain side increased. The addition of anti-oxidants did not improve CoQ10 uptake to the brain side. This study is the first to generate in vitro BBB endothelial cell models of CoQ10 deficiency, and the first to identify lipoprotein-associated uptake and efflux mechanisms regulating CoQ10 distribution across the BBB. The results imply that the uptake of exogenous CoQ10 into the brain might be improved by the administration of LDLR inhibitors, or by interventions to stimulate luminal activity of SR-B1 transporters

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand or accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The reduced dataset for t-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on 'unseen' meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 seconds

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding ((Formula presented.) -SNE) algorithm. The reduced dataset for (Formula presented.) -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on “unseen” meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s

Cirsium species show disparity in patterns of genetic variation at their range-edge, despite similar patterns of reproduction and isolation

Genetic variation was assessed across the UK geographical range of Cirsium acaule and Cirsium heterophyllum. A decline in genetic diversity and increase in population divergence approaching the range edge of these species was predicted based on parallel declines in population density and seed production reported seperately. Patterns were compared with UK populations of the widespread Cirsium arvense.Populations were sampled along a latitudinal transect in the UK and genetic variation assessed using microsatellite markers. Cirsium acaule shows strong isolation by distance, a significant decline in diversity and an increase in divergence among range-edge populations. Geographical structure is also evident in C. arvense, whereas no such patterns are seen in C.heterophyllum. There is a major disparity between patterns of genetic variation in C. acaule and C. heterophyllum despite very similar patterns in seed production and population isolation in these species. This suggests it may be misleading to make assumptions about the geographical structure of genetic variation within species based solely on the present-day reproduction and distribution of populations

Charcoal does not change the decomposition rate of mixed litters in a mineral cambisol: a controlled conditions study

It has been recently shown that the presence of charcoal might promote humus decomposition in the soil. We investigated the decomposition rate of charcoal and litters of different biochemical quality mixed together in a soil incubation under controlled conditions. Despite the large range of organic substrate quality used in this study, we did not find any difference in the decomposition between the average of two individual substrates decomposing separately and the same substrates mixed together. We concluded that charcoal does not always promote other organic matter decomposition and that its particular effect might depend on various factors, for example, soil properties

Is TEA an inhibitor for human Aquaporin-1?

Excessive water uptake through aquaporins can be life threatening, and disregulation of water permeability causes many diseases. Therefore, reversible aquaporin inhibitors are highly desired. In this paper, we identified the binding site for tetraethylammonium (TEA) of the membrane water channel aquaporin-1 by a combined molecular docking and molecular dynamics simulation approach. The binding site identified from docking studies was independently confirmed with an unbiased molecular dynamics simulation of an aquaporin tetramer embedded in a lipid membrane, surrounded by a 100-mM tetraethylammonium solution in water. A third independent assessment of the binding site was obtained by umbrella sampling simulations. These simulations, in addition, revealed a binding affinity of more than 17 kJ/mol, corresponding to an IC50 value of << 3 mM. Finally, we observed in our simulations a 50% reduction of the water flux in the presence of TEA, in agreement with water permeability measurements on aquaporin expressed in oocytes. These results confirm TEA as a putative lead for an aquaporin-1 inhibitor

Effects of beta-alanine supplementation on brain homocarnosine/carnosine signal and cognitive function: an exploratory study

Objectives: Two independent studies were conducted to examine the effects of 28 d of beta-alanine supplementation at 6.4 g d-1 on brain homocarnosine/carnosine signal in omnivores and vegetarians (Study 1) and on cognitive function before and after exercise in trained cyclists (Study 2). Methods: In Study 1, seven healthy vegetarians (3 women and 4 men) and seven age- and sex-matched omnivores undertook a brain 1H-MRS exam at baseline and after beta-alanine supplementation. In study 2, nineteen trained male cyclists completed four 20-Km cycling time trials (two pre supplementation and two post supplementation), with a battery of cognitive function tests (Stroop test, Sternberg paradigm, Rapid Visual Information Processing task) being performed before and after exercise on each occasion. Results: In Study 1, there were no within-group effects of beta-alanine supplementation on brain homocarnosine/carnosine signal in either vegetarians (p = 0.99) or omnivores (p = 0.27); nor was there any effect when data from both groups were pooled (p = 0.19). Similarly, there was no group by time interaction for brain homocarnosine/carnosine signal (p = 0.27). In study 2, exercise improved cognitive function across all tests (P0.05) of beta-alanine supplementation on response times or accuracy for the Stroop test, Sternberg paradigm or RVIP task at rest or after exercise. Conclusion: 28 d of beta-alanine supplementation at 6.4g d-1 appeared not to influence brain homocarnosine/ carnosine signal in either omnivores or vegetarians; nor did it influence cognitive function before or after exercise in trained cyclists