Relationships between secchi disk visibility, water temperature and dissolved oxygen in freshwater fishpond

A study was conducted to determine the relationships between secchi disk variability, water temperature and dissolved oxygen in fish ponds. Multiple regression correlation analysis was done to evaluate the relationships between the variables. Results indicated that the ranges of secchi disk visibility, water temperature and dissolved oxygen in the study ponds were just within the ranges of the variables for tilapia culture. Multiple regression correlation showed no (or insignificant) relationships with dissolved oxygen and water temperature, dissolved oxygen with secchi disk visibility and water temperature with secchi disk visibility.

Age-related effects of bilateral frontal eye fields lesions on rapid eye movements during REM sleep in rhesus monkeys

Rapid eye movement (REM) is one of the most characteristic features of REM sleep, but the mechanisms underlying its regulation remain unclear. The present study aims to investigate whether the frontal eye field (FEF) is involved in the regulation of the r

Neuronal activity in dorsomedial frontal cortex and prefrontal cortex reflecting irrelevant stimulus dimensions

Previous studies of the dorsomedial frontal cortex (DMF) and the prefrontal cortex (PF) have shown that, when monkeys respond to nonspatial features of a discriminative stimulus (e.g., color) and the stimulus appears at a place unrelated to the movement t

Frontal and cingulate gray matter volume reduction in heroin dependence: Optimized voxel-based morphometry

Aims: Repeated exposure to heroin, a typical opiate, causes neuronal adaptation and may result in anatomical changes in specific brain regions, particularly the frontal and limbic cortices. The volume changes of gray matter (GM) of these brain regions, ho

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases. © 2009 IEEE.