Estimating exploitable stock biomass for the Maine green sea urchin (Strongylocentrotus droebachiensis) fishery using a spatial statistics approach

The objective of this study was to investigate the spatial patterns in green sea urchin (Strongylocentrotus droebachiensis) density off the coast of Maine, using data from a fishery-independent survey program, to estimate the exploitable biomass of this species. The dependence of sea urchin variables on the environment, the lack of stationarity, and the presence of discontinuities in the study area made intrinsic geostatistics inappropriate for the study; therefore, we used triangulated irregular networks (TINs) to characterize the large-scale patterns in sea urchin density. The resulting density surfaces were modified to include only areas of the appropriate substrate type and depth zone, and were used to calculate total biomass. Exploitable biomass was estimated by using two different sea urchin density threshold values, which made different assumptions about the fishing industry. We observed considerable spatial variability on both small and large scales, including large-scale patterns in sea urchin density related to depth and fishing pressure. We conclude that the TIN method provides a reasonable spatial approach for generating biomass estimates for a fishery unsuited to geostatistics, but we suggest further studies into uncertainty estimation and the selection of threshold density values

Damage to Association Fiber Tracts Impairs Recognition of the Facial Expression of Emotion

An array of cortical and subcortical structures have been implicated in the recognition of emotion from facial expressions. It remains unknown how these regions communicate as parts of a system to achieve recognition, but white matter tracts are likely critical to this process. We hypothesized that (1) damage to white matter tracts would be associated with recognition impairment and (2) the degree of disconnection of association fiber tracts [inferior longitudinal fasciculus (ILF) and/or inferior fronto-occipital fasciculus (IFOF)] connecting the visual cortex with emotion-related regions would negatively correlate with recognition performance. One hundred three patients with focal, stable brain lesions mapped onto a reference brain were tested on their recognition of six basic emotional facial expressions. Association fiber tracts from a probabilistic atlas were coregistered to the reference brain. Parameters estimating disconnection were entered in a general linear model to predict emotion recognition impairments, accounting for lesion size and cortical damage. Damage associated with the right IFOF significantly predicted an overall facial emotion recognition impairment and specific impairments for sadness, anger, and fear. One subject had a pure white matter lesion in the location of the right IFOF and ILF. He presented specific, unequivocal emotion recognition impairments. Additional analysis suggested that impairment in fear recognition can result from damage to the IFOF and not the amygdala. Our findings demonstrate the key role of white matter association tracts in the recognition of the facial expression of emotion and identify specific tracts that may be most critical

On computing homology gradients over finite fields

Recently the so-called Atiyah conjecture about l^2-Betti numbers has been disproved. The counterexamples were found using a specific method of computing the spectral measure of a matrix over a complex group ring. We show that in many situations the same method allows to compute homology gradients, i.e. generalizations of l^2-Betti numbers to fields of arbitrary characteristic. As an application we point out that (i) the homology gradient over any field of characteristic different than 2 can be an irrational number, and (ii) there exists a finite CW-complex with the property that the homology gradients of its universal cover taken over different fields have infinitely many different values.Comment: 30 pages, 6 figures, v3: changes in exposition, fixed proof of Theorem 4.1. To appear in Cambridge Math. Pro

Empirical comparison of diffusion kurtosis imaging and diffusion basis spectrum imaging using the same acquisition in healthy young adults

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals

Cyclic Sieving of Increasing Tableaux and small Schr\"oder Paths

An increasing tableau is a semistandard tableau with strictly increasing rows and columns. It is well known that the Catalan numbers enumerate both rectangular standard Young tableaux of two rows and also Dyck paths. We generalize this to a bijection between rectangular 2-row increasing tableaux and small Schr\"oder paths. We demonstrate relations between the jeu de taquin for increasing tableaux developed by H. Thomas and A. Yong and the combinatorics of tropical frieze patterns. We then use this jeu de taquin to present new instances of the cyclic sieving phenomenon of V. Reiner, D. Stanton, and D. White, generalizing results of D. White and of J. Stembridge.Comment: 20 page

Early diagnosis, biomarkers and intervention in Alzheimer’s disease

New Trends in Dementia Diagnosis and CareDementia in Primary Care: Prevalence to Partnerships Thomas J. Grabowski MD Director, UW Memory and Brain Wellness Center Director, Alzheimer Disease Research Center University of Washingto

Satellite data validation: a parametrization of the natural variability of atmospheric mixing ratios

High-resolution model data are used to estimate the statistically typical mixing ratio variabilities of trace species as a function of distance and time separation. These estimates can be used to explain the fact that some of the differences between observations made with different observing systems are due to the less-than-perfect co-location of the measurements. The variability function is approximated by a two-parameter regression function, and lookup tables of the natural variability values as a function of distance separation and time separation are provided. In addition, a reparametrization of the variability values as a function of latitudinal gradients is proposed, and the seasonal independence of the linear approximation of such a function is demonstrated

Breaking New Ground in Computational Psychiatry:Model-Based Characterization of Forgetting in Healthy Aging and Mild Cognitive Impairment

Computational models of memory used in adaptive learning settings trace a learner’s memory capacities. However, less work has been done on the implementation of these models in the clinical realm. Current assessment tools lack the reliable, convenient, and repeatable qualities needed to capture the individualized and evolving nature of memory decline. The goal of this project was to predict and track memory decline in subjectively- or mildly cognitively impaired (MCI) individuals by using a model-based, adaptive fact-learning system. Here we present data demonstrating that these tools can diagnose mild memory impairment with over 80% accuracy after a single 8-minute learning session. These findings provide new insights into the nature and progression of memory decline and may have implications for the early detection and management of Alzheimer’s disease and other forms of dementia