The Mosaic of Extracellular Matrix in the Central Nervous System as a Determinant of Glial Heterogeneity

Accumulating evidence points to a primary role for non-myelinating glia as principal mediators of homeostasis in the central nervous system (CNS). However, the origins of the basis for glial heterogeneity are not well understood. Our recent studies contribute to an emerging view that the extracellular matrix (ECM) provides clues to glia underling their specialized functions and, more importantly, the nature of how glia change in relation to neuropathology. In this review, we discuss how the dynamic mosaic of CNS ECM impacting CNS health and disease. Specifically, we focus on the roles of select extracellular matrix proteins, namely fibronectin (Fn), vitronectin (Vn), laminin (Ln) and tenascin-c (TnC), as prototypes for how ECM can modulate glial functions. We discuss the differences in expression patterns in the developing and adult CNS and relate these ECM molecules to specific changes in glial functions in neurological diseases. We also discuss how experiments have revealed the role of ECM molecules’ influence on CNS development and the response of glia to injury and inflammation. We provide a new model to explain the nature of glial diversity as an adaptive response to the extracellular milieu, and provide a different approach to understand the complex nature of glia heterogeneity

Stem Cells of the Aging Brain.

The adult central nervous system (CNS) contains resident stem cells within specific niches that maintain a self-renewal and proliferative capacity to generate new neurons, astrocytes, and oligodendrocytes throughout adulthood. Physiological aging is associated with a progressive loss of function and a decline in the self-renewal and regenerative capacities of CNS stem cells. Also, the biggest risk factor for neurodegenerative diseases is age, and current in vivo and in vitro models of neurodegenerative diseases rarely consider this. Therefore, combining both aging research and appropriate interrogation of animal disease models towards the understanding of the disease and age-related stem cell failure is imperative to the discovery of new therapies. This review article will highlight the main intrinsic and extrinsic regulators of neural stem cell (NSC) aging and discuss how these factors impact normal homeostatic functions within the adult brain. We will consider established in vivo animal and in vitro human disease model systems, and then discuss the current and future trajectories of novel senotherapeutics that target aging NSCs to ameliorate brain disease

Archeological 3D Mapping: The Structure from Motion Revolution

Mapping is a critical aspect of systematic documentation no matter where archaeologists work. From hand-drawn maps of excavation units to maps created with Total Data Stations or LiDAR scanning, today’s archaeologists have a suite of mapping techniques and technologies to choose from when documenting a site. Typically, spectacular sites often receive high resolution mapping, whereas everyday sites rarely do. Recently, however, a revolutionary technology and technique has been created that can produce highly accurate and precise three-dimensional maps and orthophotos of archaeological sites, features, and profiles at a fraction of the cost and time of LiDAR and intensive TDS mapping: Structure from Motion (SfM). SfM is a new digital photography processing technique for capturing highly detailed, three-dimensional (3D) data from almost any surface using digital cameras. This article introduces the various platforms SfM photographs can be collected from (UAV, kites, balloons, poles, and groundbased) and provides examples of different types of data SfM can provide. The Structure from Motion Revolution is unfolding across the globe at a rapid pace, and we encourage archaeologists to take advantage of this new recording method

Flight range, fuel load and the impact of climate change on the journeys of migrant birds

Collection of biometric data was supported by the Natural Environment Research Council (NE/I028068/1) to JAT and the USUK Fulbright Commission and the Oxford Clarendon Fund to C.S. This research was funded by a Durham University Seedcorn grant to SGW. Production of the underlying SDMs was funded by a National Environment Research Council training grant (NE/J500215/1).Climate change is predicted to increase migration distances for many migratory species, but the physiological and temporal implications of longer migratory journeys have not been explored. Here, we combine information about species' flight range potential and migratory refuelling requirements to simulate the number of stopovers required and the duration of current migratory journeys for 77 bird species breeding in Europe. Using tracking data, we show that our estimates accord with recorded journey times and stopovers for most species. We then combine projections of altered migratory distances under climate change with models of avian flight to predict future migratory journeys. We find that 37% of migratory journeys undertaken by long-distance migrants will necessitate an additional stopover in future. These greater distances and the increased number of stops will substantially increase overall journey durations of many long-distance migratory species, a factor not currently considered in climate impact studies.Publisher PDFPeer reviewe

Assessing the effect of a treatment when subjects are growing at different rates

The analysis of covariance is often used in the context of premeasure/postmeasure designs to compare treatment and control groups in both randomized [1] and nonrandomized [2] studies. The intent is to adjust the difference between the changes in the 2 groups for any difference which might exist at baseline, i.e., for any difference between the premeasures in the 2 groups. An important assumption underlying the use of the analysis of covariance is that the slopes of the lines for the regression of the postmeasure on the premeasure in the 2 groups are equal. In this paper we describe a program which can be used to test the hypothesis of equal slopes; and performs an alternative analysis which does not depend on this assumption. This is done in the context of comparing treatment and control groups with respect to a measurement subject to natural maturation as in [3]. Equal slopes in this context means equal growth rates; unequal slopes implies that the 2 groups are growing at different rates. The method, known as the Johnson-Neyman procedure [4] is, however, more general than this, and can be used in any two-sample comparison where an alternative to the usual analysis of covariance is deemed appropriate. The procedure identifies a `region of significance' which is especially useful in practice. This region consists of a set of values of the premeasure for which the treatment and the control groups are significantly different with respect to the postmeasure.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31282/1/0000188.pd

ANCOVA for nonparallel slopes: the Johnson-Neyman technique

The Johnson-Neyman (JN) procedure, as originally formulated (Stat Res Mem, 1 (1936) 57-93), applies to a situation in which measurements on 1 dependent (response) variable, X, and 2 independent (predictor) variables, Z1 and Z2, are available for the members of 2 groups. The expected value of X is assumed to be a linear function of Z1 and Z2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the Z variables for which one would reject, at a specified level of significance [alpha] (e.g., [alpha] = 0.05), the hypothesis that the 2 groups have the same expected X values. This set of values, or `region of significance,' may then be plotted to obtain a convenient description of those values of Z1 and Z2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of X on the covariates, Z1 and Z2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31210/1/0000112.pd

A PC program for classification into one of several groups on the basis of longitudinal data

A stand-alone, menu-driven PC program, ZCLASS, written in GAUSS386i, for classifying subjects into one of several distinct, existing groups on the basis of longitudinal data is described, illustrated, and made available to interested readers. The program accepts data from studies where common times of measurement are planned, but missing data are accommodated in that one or more measurement sequences may be incomplete.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31477/1/0000399.pd

Regression imputation of missing values in longitudinal data sets

A stand-alone, menu-driven PC program, written in GAUSS, which can be used to estimate missing observations in longitudinal data sets is described and made available to interested readers. The program is limited to the situation in which we have complete data on N cases at each of the planned times of measurement t1, t2,..., tT; and we wish to use this information, together with the non-missing values for n additional cases, to estimate the missing values for those cases. The augmented data matrix may be saved in an ASCII file and subsequently imported into programs requiring complete data. The use of the program is illustrated. Ten percent of the observations in a data set consisting of mandibular ramus height measurements for N = 12 young male rhesus monkeys measured at T = 5 time points are randomly discarded. The augmented data matrix is used to determine the lowest degree polynomial adequate to fit the average growth curve (AGC); the regression coefficients are estimated and confidence intervals for them are determined; and confidence bands for the AGC are constructed. The results are compared with those obtained when the original complete data set is used.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30933/1/0000603.pd

Clustering on the basis of longitudinal data

A menu-drive PC program, ZDIST, for computing the distances between the estimated polynomial growth curves of subjects who have been followed longitudinally is described, illustrated, and made available to interested readers. These distances can be computed on the basis of the individual growth curves themselves and/or from estimates of individuals' growth velocity and acceleration curves. The resulting distance matrices can be saved in ASCII format and subsequently imported into any clustering program which accepts this type of input, e.g. SYSTAT.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30607/1/0000244.pd

PC program implementing an alternative to the paired t-test which adjusts for regression to the mean

In many biomedical research contexts, treatment effects are estimated from studies based on subjects who have been recruited because of high (low) measurements of a response variable, e.g., high blood pressure or low scores on a stress test. In this situation, simple change scores will overestimate the treatment effect; and the use of the paired t-test may find significant change due not to the treatment per se but, rather, due to regression towards the mean. A PC program implementing a procedure for adjusting the observed change for the regression effect in simple pre-test-post-test experiments is described, illustrated, and made available to interested readers. The method is due to Mee and Chua (Am Stat, 45 (1991) 39-42), and may be considered as an alternative to the paired t-test which separates the effect of the treatment from the so-called regression effect.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31205/1/0000107.pd