Impact of maternal mild hyperglycemia on maternal care and offspring development and behavior of Wistar rats

The aim of the present study was to evaluate the effect of maternal mild hyperglycemia on maternal behavior, as well as the development, behavior, reproductive function, and glucose tolerance of the offspring. At birth, litters were assigned either to Control (subcutaneous (sc)-citrate buffer) or STZ groups (streptozotocin (STZ)-100 mg/kg-sc.). On PND 90 both STZ-treated and Control female rats were mated. Glucose tolerance tests (GTT) and insulin tolerance tests (ITT) were performed during pregnancy. Pregnancy duration, litter size and sex ratio were assessed. Newborns were classified according to birth weight as small (SPA), adequate (APA), or large for pregnancy age (LPA). Maternal behavior was analyzed on PND 5 and 10. Offspring body weight, length, and anogenital distance were measured and general activity was assessed in the open field. Sexual behavior was tested in both male and female offspring. Levels of reproductive hormones and estrous cycle duration were evaluated in female offspring. Female offspring were mated and both a GTT and ITT performed during pregnancy. Neonatal STZ administration caused mild hyperglycemia during pregnancy and changed some aspects of maternal care. The hyperglycemic intrauterine milieu impaired physical development and increased immobility in the open field in the offspring although the latter effect appeared at different ages for males (adulthood) and females (infancy). There was no impairment in the sexual behavior of either male or female offspring. As adults, female offspring of STZ-treated mothers did not show glucose intolerance during pregnancy. Thus, offspring of female rats that show mild hyperglycemia in pregnancy have fewer behavioral and developmental impairments than previously reported in the offspring of severely diabetic dams suggesting that the degree of impairment is directly related to the mother glycemic intensity. (C) 2012 Elsevier Inc. All rights reserved.

Time-variations of equivalent water heights'from Grace Mission and in-situ river stages in the Amazon basin

Gravity Recovery and Climate Experiment (GRACE) mission is dedicated to measuring temporal variations of the Earth's gravity field. In this study, the Stokes coefficients made available by Groupe de Recherche en Géodésie Spatiale (GRGS) at a 10-day interval were converted into equivalent water height (EWH) for a ~4-year period in the Amazon basin (from July-2002 to May-2006). The seasonal amplitudes of EWH signal are the largest on the surface of Earth and reach ~ 1250mm at that basin's center. Error budget represents ~130 mm of EWH, including formal errors on Stokes coefficient, leakage errors (12 ~ 21 mm) and spectrum truncation (10 ~ 15 mm). Comparison between in situ river level time series measured at 233 ground-based hydrometric stations (HS) in the Amazon basin and vertically-integrated EWH derived from GRACE is carried out in this paper. Although EWH and HS measure different water bodies, in most of the cases a high correlation (up to ~80%) is detected between the HS series and EWH series at the same site. This correlation allows adjusting linear relationships between in situ and GRACE-based series for the major tributaries of the Amazon river. The regression coefficients decrease from up to down stream along the rivers reaching the theoretical value 1 at the Amazon's mouth in the Atlantic Ocean. The variation of the regression coefficients versus the distance from estuary is analysed for the largest rivers in the basin. In a second step, a classification of the proportionality between in situ and GRACE time-series is proposed.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.