Inhibition of Neutral Amino Acid Transport Across the Human Blood-Brain Barrier by Phenylalanine

The delivery of large neutral amino acids (LNAAs) to brain across the blood-brain barrier (BBB) is mediated by the L-type neutral amino acid transporter present in the membranes of the brain capillary endothelial cell. In experimental animals, the L-system transporter is saturated under normal conditions, and therefore an elevation in the plasma concentration of one LNAA will reduce brain uptake of others. In this study, we used positron emission tomography (PET) to determine the effect of elevated plasma phenylalanine concentrations on the uptake of an artificial neutral amino acid, [ 11 C]-aminocyclohexanecarboxylate ([ 11 C]ACHC), in human brain. PET scans were performed on six normal male subjects after an overnight fast and again 60 min after oral administration of 100 mg/kg of phenylalanine. The plasma phenylalanine concentration increased by an average of 11-fold between the first and second scans. This increase produced a reduction in [ 11 C]ACHC uptake in all brain regions but not in scalp. The mean ± SD influx rate constant for whole brain decreased after phenylalanine ingestion from 0.036 ± 0.002 to 0.019 ± 0.004 ml/g/min. Kinetic analysis of the effect of plasma phenylalanine concentration on the rate of [ 11 C]ACHC uptake is compatible with a model of competitive inhibition so that large increases in the concentration of one LNAA in plasma will reduce the brain uptake of other LNAAs across the human BBB.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65880/1/j.1471-4159.1995.64031252.x.pd

Dynamical and quasistatic structural relaxation paths in Pd_(40)Ni_(40)P_(20) glass

By sequential heat treatment of a Pd_(40)Ni_(40)P_(20) metallic glass at temperatures and durations for which α-relaxation is not possible, dynamic, and quasistatic relaxation paths below the glass transition are identified via ex situ ultrasonic measurements following each heat treatment. The dynamic relaxation paths are associated with hopping between nonequilibrium potential energy states of the glass, while the quasistatic relaxation path is associated with reversible β-relaxation events toward quasiequilibrium states. These quasiequilibrium states are identified with secondary potential energy minima that exist within the inherent energy minimum of the glass, thereby supporting the concept of the sub-basin/metabasin organization of the potential-energy landscape

Covariance of Kinetic Parameter Estimators Based on Time Activity Curve Reconstructions: Preliminary Study on 1D Dynamic Imaging

We provide approximate expressions for the covariance matrix of kinetic parameter estimators based on time activity curve (TAC) reconstructions when TACs are modeled as a linear combination of temporal basis functions such as B-splines. The approximations are useful tools for assessing and optimizing the basis functions for TACs and the temporal bins for data in terms of computation and efficiency. In this paper we analyze a 1D temporal problem for simplicity, and we consider a scenario where TACs are reconstructed by penalized-likelihood (PL) estimation incorporating temporal regularization, and kinetic parameters are obtained by maximum likelihood (ML) estimation. We derive approximate formulas for the covariance of the kinetic parameter estimators using 1) the mean and variance approximations for PL estimators in (Fessler, 1996) and 2) Cramer-Rao bounds. The approximations apply to list-mode data as well as bin-mode data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85981/1/Fessler193.pd

Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models

(Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials

INTERMEDIATE SUMS ON POLYHEDRA II:BIDEGREE AND POISSON FORMULA

Abstract. We continue our study of intermediate sums over polyhedra,interpolating between integrals and discrete sums, whichwere introduced by A. Barvinok [Computing the Ehrhart quasi-polynomial of a rational simplex, Math. Comp. 75 (2006), 1449–1466]. By well-known decompositions, it is sufficient to considerthe case of affine cones s+c, where s is an arbitrary real vertex andc is a rational polyhedral cone. For a given rational subspace L,we integrate a given polynomial function h over all lattice slicesof the affine cone s + c parallel to the subspace L and sum up theintegrals. We study these intermediate sums by means of the intermediategenerating functions SL(s+c)(ξ), and expose the bidegreestructure in parameters s and ξ, which was implicitly used in thealgorithms in our papers [Computation of the highest coefficients ofweighted Ehrhart quasi-polynomials of rational polyhedra, Found.Comput. Math. 12 (2012), 435–469] and [Intermediate sums onpolyhedra: Computation and real Ehrhart theory, Mathematika 59(2013), 1–22]. The bidegree structure is key to a new proof for theBaldoni–Berline–Vergne approximation theorem for discrete generatingfunctions [Local Euler–Maclaurin expansion of Barvinokvaluations and Ehrhart coefficients of rational polytopes, Contemp.Math. 452 (2008), 15–33], using the Fourier analysis with respectto the parameter s and a continuity argument. Our study alsoenables a forthcoming paper, in which we study intermediate sumsover multi-parameter families of polytopes

Imaging butyrylcholinesterase activity in Alzheimer's disease

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55890/1/21023_ftp.pd

Age Differences in Behavior and PET Activation Reveal Differences in Interference Resolution in Verbal Working Memory

Older adults were tested on a verbal working memory task that used the item-recognition paradigm. On some trials of this task, response-conflict was created by presenting test-items that were familiar but were not members of a current set of items stored in memory. These items required a negative response, but their familiarity biased subjects toward a positive response. Younger subjects show an interference effect on such trials, and this interference is accompanied by activation of a region of left lateral prefrontal cortex. However, there has been no evidence that the activation in this region is causally related to the interference that the subjects exhibit. In the present study, we demonstrate that older adults show more behavioral interference than younger subjects on this task, and they also show no reliable activation at the same lateral prefrontal site. This leads to the conclusion that this prefrontal site is functionally involved in mediating resolution among conflicting responses or among conflicting representations in working memory

Assessment of the biodistribution of an [ 18 F]FDG‐loaded perfluorocarbon double emulsion using dynamic micro‐PET in rats

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97497/1/cmmi1532.pd

Age Differences in the Frontal Lateralization of Verbal and Spatial Working Memory Revealed by PET

Age-related decline in working memory figures prominently in theories of cognitive aging. However, the effects of aging on the neural substrate of working memory are largely unknown. Positron emission tomography (PET) was used to investigate verbal and spatial short-term storage (3 sec) in older and younger adults. Previous investigations with younger subjects performing these same tasks have revealed asymmetries in the lateral organization of verbal and spatial working memory. Using volume of interest (VOI) analyses that specifically compared activation at sites identified with working memory to their homologous twin in the opposite hemisphere, we show pronounced age differences in this organization, particularly in the frontal lobes: In younger adults, activation is predominantly left lateralized for verbal working memory, and right lateralized for spatial working memory, whereas older adults show a global pattern of anterior bilateral activation for both types of memory. Analyses of frontal subregions indicate that several underlying patterns contribute to global bilaterality in older adults: most notably, bilateral activation in areas associated with rehearsal, and paradoxical laterality in dorsolateral prefrontal sites (DLPFC; greater left activation for spatial and greater right activation for verbal). We consider several mechanisms that could account for these age differences including the possibility that bilateral activation reflects recruitment to compensate for neural decline

Microscopic Model for Granular Stratification and Segregation

We study segregation and stratification of mixtures of grains differing in size, shape and material properties poured in two-dimensional silos using a microscopic lattice model for surface flows of grains. The model incorporates the dissipation of energy in collisions between rolling and static grains and an energy barrier describing the geometrical asperities of the grains. We study the phase diagram of the different morphologies predicted by the model as a function of the two parameters. We find regions of segregation and stratification, in agreement with experimental finding, as well as a region of total mixing.Comment: 4 pages, 7 figures, http://polymer.bu.edu/~hmakse/Home.htm