Just as Good

Fiction by Frederick Robinso

The University of Queensland, St. Lucia, Brisbane.

No abstract availabl

Letter regarding National Recovery Act Parade

Letter from the President of CCNY regarding the National Recovery Act Parade in which Brooklyn College participate

Grouping and Classifying Electrophysiologically-Defined Classes of Neocortical Neurons by Single Cell, Whole-Genome Expression Profiling

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts

Reverberation Chamber Immunity Testing : A novel methodology to avoid accidental DUT damage

Abstract—This paper shows a novel method of measuring the immunity of electronic devices inside reverberation chambers. Rather than using mode stirring or mode tuning with a constant power input into the chamber, we will present a method based on variable power that protects the DUT against accidental damage and also gives more information about the hardness of the DUT than the traditional methods

CASTLEGUARD : anonymised data streams with guaranteed differential privacy

Data streams are commonly used by data controllers to outsource the processing of real-time data to third-party data processors. Data protection legislation and best practice in data management support the view that data controllers are responsible for providing a guarantee of privacy for user data contained within published data streams. Continuously Anonymising STreaming data via adaptive cLustEring (CASTLE) is an established method for anonymising data streams with a guarantee of k-anonymity. However, k-anonymity has been shown to be a weak privacy guarantee that has vulnerabilities in practical applications. In this paper we propose Continuously Anonymising STreaming data via adaptive cLustEring with GUAR-anteed Differential privacy (CASTLEGUARD), a data stream anonymisation algorithm that provides a reliable guarantee of k-anonymity, l-diversity and differential privacy to data subjects. We analyse CASTLEGUARD to show that, through safe k-anonymisation and β-sampling, the proposed approach satisfies differentially private k-anonymity. Further, we demonstrate the efficacy of the approach in the context of machine learning, presenting experimental analysis to demonstrate that it can be used to protect the individual privacy of users whilst maintaining the utility of a data stream

Summer 1975

Getting Back to Basics--Turfgrass Fertilization (page 3) Carefree Herbaceous Perennials For Gardens and Borders (9) Back and Beyond (10) Turf News--New Lifting Technique accepted (14) Vandalism on Golf Courses (15) UMass Turfgrass Research Fund (20

Distribution of understory species in relation to maximum and minimum tree influence in the montane forest of the Central Oregon Cascades

Twenty sites of uniform topography and soil were select ed in the montane forest found on the East flank of the Central Oregon Cascades. These sites were located along a vegetational gradient composed of five plant communities: Abies/Pachistima, Pinus/Ceanothus, Pinus Arctostaphylos-Purshia, Pinus/Purshia/Festuca, and Juniperus Festuca. An attempt was made to relate the distributional pattern of understory species to six aspects of tree influence (overhead cover, amount of shade, daily differences in shading, seasonal differences in shading, amount of litter deposit, and species of tree depositing the litter) understory species' competition, and plot location. Cole's index and hierarchical classification analysis were statistical methods used to correlate the understory species' pattern to tree influence, understory species' competition, and plot location. From frequency data of the 63 species analyzed by Cole's index, only two showed an inconsistent distributional pattern in relation to tree influence. Similarly, three distributional patterns were noted. (1) Species were distributed at random in the area of maximum tree influence regardless where the species occurred along the gradient. (2) Species were distributed at random in the area of minimum tree influence and were absent in the area of maximum tree influence regardless where the species occurred along the gradient. (3) Species at a point along the gradient were distributed at random in areas of maximum and minimum tree influence; but on more xeric plots the species were distributed similar to pattern 1, and on more mesic plots species were distributed similar to pattern 2. In pattern 3, the point along the gradient where the species were distributed at random to areas of maximum and minimum tree influence may suggest an optimum point along the gradient where the effect from tree layer influence is minimal. This point provides a basis for comparing the environmental tolerances of the species and ordinating the stands. When the species' density data were analyzed by hierarchial classification to determine what factors of maximum and minimum tree influence effected the density distribution of the species, the following patterns were noted. Normally, species with highest densities in areas of maximum insolation or sparse litter were prominent on the xeric end of the gradient, and those species with maximum densities in areas of low insolation or deep litter were prominent on the mesic end of the gradient. Chamaephyte species sampled had highest densities in areas underneath the trees and usually near the me sic end of the gradient. Therophyte species sampled had highest densities in open areas usually near the xeric end of the gradient. Thus, a theoretical model was constructed using data obtained from this structural analysis of internal distributional patterns of understory species. The distribution of the species is much wider according to the theoretical model than was actually found by constancy data, but the differences when statistically analyzed are not great enough to reject the model at the 1% significance level. The model suggests predictable patterns of species' distribution within the five community types and may reflect the relative stability of these species within the community types