Assessment of synchrony in multiple neural spike trains using loglinear point process models

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS429 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

First Record of \u3ci\u3eOchlerotatus Japonicus\u3c/i\u3e (Diptera: Culicidae) in St. Joseph County, Indiana

A single female specimen of Ochlerotatus japonicus (Theobald)(formerly Aedes japonicus), the Asian bush mosquito, was captured in St. Joseph County, IN on 29 July 2004. This is the first report of that species in northern Indiana. Additional specimens were subsequently collected, indicating probable establishment throughout the county

Referencing Sources of Molecular Spectroscopic Data in the Era of Data Science: Application to the HITRAN and AMBDAS Databases

The application described has been designed to create bibliographic entries in large databases with diverse sources automatically, which reduces both the frequency of mistakes and the workload for the administrators. This new system uniquely identifies each reference from its digital object identifier (DOI) and retrieves the corresponding bibliographic information from any of several online services, including the SAO/NASA Astrophysics Data Systems (ADS) and CrossRef APIs. Once parsed into a relational database, the software is able to produce bibliographies in any of several formats, including HTML and BibTeX, for use on websites or printed articles. The application is provided free-of-charge for general use by any scientific database. The power of this application is demonstrated when used to populate reference data for the HITRAN and AMBDAS databases as test cases. HITRAN contains data that is provided by researchers and collaborators throughout the spectroscopic community. These contributors are accredited for their contributions through the bibliography produced alongside the data returned by an online search in HITRAN. Prior to the work presented here, HITRAN and AMBDAS created these bibliographies manually, which is a tedious, time-consuming and error-prone process. The complete code for the new referencing system can be found at \url{https://github.com/hitranonline/refs}.Comment: 11 pages, 5 figures, already published online at https://doi.org/10.3390/atoms802001

False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper

Teaching and Learning Los Angeles through Engagement with UCLA Library Special Collections

This article presents a case study of how library services and special collections, in particular, can be integrated into undergraduate education by engaging strategically with a high-impact area of the curriculum and concentrating on courses related thematically to collection strengths. The goals of such engagement include enhancing student academic success and increasing the visibility and use of library services and collections. During the academic year 2012-2013, the UCLA Library\u27s Teaching and Learning Services and Library Special Collections partnered with the Division of Undergraduate Education\u27s Freshman Cluster Program to experiment with embedding librarians into instructional teams in order to improve students\u27 research skills. In “Los Angeles: The Cluster,” a year-long, interdisciplinary course focused on the history, architecture, and culture of Los Angeles, librarians collaborated with faculty and graduate student teaching assistants to incorporate primary sources, especially rare and unique cultural heritage materials, into the undergraduate curriculum. In this article, Kelly Miller provides an overview of the library’s partnership with the Freshman Cluster Program, and Robert Montoya describes his experience as an embedded librarian in the LA Cluster

Stabilization of thermocapillary convection by means of nonplanar flow oscillations

Nonplanar flow oscillations have been shown to be effective in stabilizing buoyancy-induced Rayleigh-Benard convection. The present study was initiated to see if thermocapillary convection of the Marangoni type might also be stabilized by the same means. When surface deflection can be ignored, significant stabilization occurs. However, when the operating parameters are such that surface deflection is nonnegligible, destabilization can occur, in contrast to Rayleigh-Benard convection. Mechanisms for both stabilization and destabilization are discussed

Studies in Thermocapillary Convection of the Marangoni-Benard Type

The effects of imposed nonlinear oscillatory shear upon the onset of Marangoni-Bernard convection, as predicted by linear theory, in a layer of liquid with a deformable free surface were reported upon by Or and Kelly for small amplitude oscillations. Depending on the operating conditions, either stabilization or destabilization might occur. The aim of the current paper is to report the results for finite amplitude imposed oscillations so that the actual amount of stabilization or destabilization can be determined for prescribed operating conditions