Separation and probability of correct classification among two or more distributions

A function on theK-fold product of a set in normed vector space will be called a separation measurement if, for any collection ofK points, the function is bounded below and above, respectively, by maximum and total distance between pairs of points in the collection. Separation measurements are relavent toK-sample hypothesis testing and also to discrimination amongK classes, and several examples are given. In particular, ordinaryL ₁ distance between integrable functions can be generalized to a non-pairwise separation measurement for densitiesf ₁,f ₂,...,f _K inL ₁[μ]; and this separation is a linear transform of the optimal discriminant's probability of correct classification. This research was supported by grant A8044 from the National Research Council of Canada.     

Helium emission spectra—I. intermediate pressure studies

The apparent cross sections of the n¹P(n = 3,4,5,6) and n^1,3D(n = 4,5,6,7) levels have been measured as a function of pressure between 5 and 200 mTorr at 1·5KeV electron impact excitation energy. The 4¹S apparent cross section was measured under the same conditions to observe secondary electron effects. A detailed analysis is made on the mechanisms of excitation transfer out of the n¹P levels in terms of radiative and collision processes. Collisional n¹P?nF transfer cross sections are calculated for the n = 4 and n = 5 levels and the results are in agreement with the principle of detailed balance. An analysis of the 6¹P apparent cross section indicates that collision processes other than n¹P?nF become important at n = 6 level in determining collisional transfer out of n¹P. Evaluation of n^1,3D apparent cross sections show that direct collisional gain and loss become more important as n increases.     

Protein type effects on steady-state crossflow membrane ultrafiltration fluxes and protein transmission

The permeate fluxes and percent protein transmission were evaluated for steady-state crossflow ultrafiltration of two proteins of different composition: bovine serum albumin (BSA), containing fatty acid, and “fatty-acid-poor” BSA, from which most of the fatty acids had been removed (BSA/FAP). The influences of protein concentration up to 6.5 percent w/v, transmembrane pressure, ionic environment and membrane type (i.e. nominal molecular weight cut-off) were investigated. For both BSA and BSA/FAP, the fluxes and the protein transmission were dependent on the amount of salt present. The higher fatty acid content in the BSA apparently enhanced protein-protein interaction, resulting in a more cohesive and resistant fouling layer; permeate fluxes were lower with BSA/FAP than with BSA at otherwise corresponding operating conditions. A hysteresis behaviour of the flux (J)-transmembrane pressure (TMP) relationship was observed whenever the ultrafiltration unit was operated at a TMP less than some higher value to which the membrane previously had been exposed.     

<Emphasis Type="Italic">N</Emphasis>-arachidonoyl glycine,an abundant endogenous lipid,potently drives directed cellular migration through GPR18, the putative abnormal cannabidiol receptor

Background  

Microglia provide continuous immune surveillance of the CNS and upon activation rapidly change phenotype to express receptors that respond to chemoattractants during CNS damage or infection. These activated microglia undergo directed migration towards affected tissue. Importantly, the molecular species of chemoattractant encountered determines if microglia respond with pro- or anti-inflammatory behaviour, yet the signaling molecules that trigger migration remain poorly understood. The endogenous cannabinoid system regulates microglial migration via CB₂ receptors and an as yet unidentified GPCR termed the 'abnormal cannabidiol' (Abn-CBD) receptor. Abn-CBD is a synthetic isomer of the phytocannabinoid cannabidiol (CBD) and is inactive at CB₁ or CB₂ receptors, but functions as a selective agonist at this G_i/o-coupled GPCR. N-arachidonoyl glycine (NAGly) is an endogenous metabolite of the endocannabinoid anandamide and acts as an efficacious agonist at GPR18. Here, we investigate the relationship between NAGly, Abn-CBD, the unidentified 'Abn-CBD' receptor, GPR18, and BV-2 microglial migration.     

Approximating Data in \mathfrak{R}^{n} by a Quadratic Underestimator with Specified Hessian Minimum and Maximum Eigenvalues

The problem of approximating m data points (x _i , y _i ) in , with a quadratic function q(x, p) with s parameters, m ≥ s, is considered. The parameter vector is to be determined so as to satisfy three conditions: (1) q(x, p) must underestimate all m data points, i.e. q(x _i , p) ≤ y _i , i=1,...,m. (2) The error of the approximation is to be minimized in the L1 norm. (3) The eigenvalues of H are to satisfy specified lower and upper bounds, where H is the Hessian of q(x, p) with respect to x. This is called the Quadratic Underestimator with Bounds on Eigenvalues (QUBE) problem. An algorithm for its solution (QUBE algorithm) is given and justified, and computational results presented. The QUBE algorithm has application to finding the global minimum of a basin (or funnel) shaped function with a large number of local minima. Such problems arise in computational biology where it is desired to find the global minimum of an energy surface, in order to predict native protein-ligand docking geometry (drug design) or protein structure. Computational results for a simulated docking energy surface, with n=15, are presented. It is shown that specifying a small condition number for H improves the ability of the underestimator to correctly predict the global minimum point.     

A terabit capacity passive polymer optical backplane based on a novel meshed waveguide architecture

An optical backplane based on a meshed polymer waveguide architecture enabling high-speed board-to-board optical interconnection is presented. This planar array of multimode polymer waveguides can provide passive strictly non-blocking links between server line cards fitted with optical transmitter and receiver arrays. This architecture offers a scalable and low-cost solution to the bandwidth limitations faced by electrical backplanes and is suitable for PCB integration. The reported backplane demonstrator uses a matrix of 100 waveguides each capable of 10 Gb/s operation to interconnect 10 cards for a total capacity of a terabit per second aggregate data rate in multicast mode. Characterisation of the backplane demonstrator reveals low link losses of 2 to 8 dB for a multimode fibre input and crosstalk values below −35 dB. Error free data transmission at 10 Gb/s is achieved with a power penalty of only 0.2 dB at a bit-error-rate of 10⁻⁹. Additionally, lossless operation of a Gigabit Ethernet link over the backplane is achieved even when using the worst-case highest loss links.     

Distributed feedback gain-coupled lasers based on InGaAs quantum-wire arrays

Fabrication and characterization of a current-injected InGaAs-GaAs quantum-wire gain-coupled distributed feedback (DFB) laser operating at 77 K at a wavelength of 923 nm are presented. Threshold current densities in broad area lasers were measured to be as low as 160 A/cm/sup 2/. The side-mode suppression ratio at twice threshold is 35 dB. A 4-/spl mu/m rib waveguide device has a threshold of 14 mA. The patterning process for the second-order DFB grating fabricated with deep UV holography and wet-chemical etching is described.     

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.