Nonimmersions of RP^n implied by tmf, revisited

In a 2002 paper, the authors and Bruner used the new spectrum tmf to obtain some new nonimmersions of real projective spaces. In this note, we complete/correct two oversights in that paper. The first is to note that in that paper a general nonimmersion result was stated which yielded new nonimmersions for RP^n with n as small as 48, and yet it was stated there that the first new result occurred when n=1536. Here we give a simple proof of those overlooked results. Secondly, we fill in a gap in the proof of the 2002 paper. There it was claimed that an axial map f must satisfy f^*(X)=X_1+X_2. We realized recently that this is not clear. However, here we show that it is true up multiplication by a unit in the appropriate ring, and so we retrieve all the nonimmersion results claimed in the original paper. Finally, we present a complete determination of tmf^{8*}(RP^\infty\times RP^\infty) and tmf^*(CP^\infty\times CP^\infty) in positive dimensions.Comment: 35 pages; author produced PDF available at http://www.lehigh.edu/~dmd1/overlook4.pd

Intercellular Migration of Centrioles in the Germarium of \u3cem\u3eDrosophila melanogaster\u3c/em\u3e. An Electron Microscopic Study

A cluster of centrioles has been found in the early Drosophila oocyte. Since the oocyte is connected to 15 nurse cells by a system of intercellular bridges or ring canals, the possibility that the cluster of centrioles arose in the germarium from an intercellular migration of centrioles from the nurse cells to the oocyte was analyzed in serial sections for the electron microscope. Initially, all of the 16 cells of the future egg chambers possess centrioles, which are located in a juxtanuclear position. At the time the 16 cell cluster becomes arranged in a lens-shaped layer laterally across the germarium, the centrioles lose their juxtanuclear position and move towards the oocyte. By the time the 16 cell cluster of cells is surrounded by follicle cells (Stage 1), between 14 and 17 centrioles are found in the oocyte. Later, these centrioles become located between the oocyte nucleus and the follicle cell border and become aggregated into a cluster less than 1.5 µ in its largest dimension. The fate of these centrioles in the oocyte is not known. The fine structure of the germarium and the early oocyte is also described

Silicon retina with adaptive photoreceptors

The central problem faced by the retina is to encode reliably small local differences in image intensity over a several-decade range of background illumination. The distal layers of the retina adjust the transducing elements to make this encoding possible. Several generations of silicon retinae that integrate phototransducers and CMOS processing elements in the focal plane are modeled after the distal layers of the vertebrate retina. A silicon retina with an adaptive photoreceptor that responds with high gain to small spatial and temporal variations in light intensity is described. Comparison with a spatial and temporal average of receptor response extends the dynamic range of the receptor. Continuous, slow adaptation centers the operating point of the photoreceptor around its time-average intensity and compensates for static transistor mismatch

Neuromorphic analogue VLSI

Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

Does a Theoretical Estimation of the Dust Size Distribution at Emission Suggest More Bioavailable Iron Deposition?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95443/1/grl28895.pd