A Technique for Characterizing the Development of Rhythms in Bird Song

The developmental trajectory of nervous system dynamics shows hierarchical structure on time scales spanning ten orders of magnitude from milliseconds to years. Analyzing and characterizing this structure poses significant signal processing challenges. In the context of birdsong development, we have previously proposed that an effective way to do this is to use the dynamic spectrum or spectrogram, a classical signal processing tool, computed at multiple time scales in a nested fashion. Temporal structure on the millisecond timescale is normally captured using a short time Fourier analysis, and structure on the second timescale using song spectrograms. Here we use the dynamic spectrum on time series of song features to study the development of rhythm in juvenile zebra finch. The method is able to detect rhythmic structure in juvenile song in contrast to previous characterizations of such song as unstructured. We show that the method can be used to examine song development, the accuracy with which rhythm is imitated, and the variability of rhythms across different renditions of a song. We hope that this technique will provide a standard, automated method for measuring and characterizing song rhythm

An Early Assessment of Medium Range Monsoon Precipitation Forecasts from the Latest High-Resolution NCEP-GFS (T1534) Model over South Asia

Reliable prediction of the South Asian monsoon rainfall and its variability is crucial for various hydrological applications and early warning systems. The National Centers for Environmental Prediction – Global Forecast System (NCEP–GFS) is one of the popular global deterministic numerical weather prediction models, which is recently upgraded from T574 to T1534. In this paper, medium range monsoon precipitation forecasts from both the T1534 and T574 models are critically evaluated over the South Asia for the peak monsoon months (July and August) of 2015. Although both the versions of GFS model show similar large-scale monsoon rainfall patterns, the dry bias over the northwest India and equatorial Indian Ocean is noticeably improved in day-1 through day-5 forecasts in the new high-resolution T1534 model. The error decomposition analysis shows similar error characteristics in the monsoon rainfall prediction from both the versions of GFS model, in general. However, forecast improvement factor shows 10-30% improvement in precipitation forecast from the latest T1534 model over most parts of the South Asia. These preliminary analyses suggest that a suitable bias-correction to the GFS model precipitation forecasts will be useful for any specific application

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Apolipoprotein A-I Attenuates Palmitate-Mediated NF-κB Activation by Reducing Toll-Like Receptor-4 Recruitment into Lipid Rafts

While high-density lipoprotein (HDL) is known to protect against a wide range of inflammatory stimuli, its anti-inflammatory mechanisms are not well understood. Furthermore, HDL's protective effects against saturated dietary fats have not been previously described. In this study, we used endothelial cells to demonstrate that while palmitic acid activates NF-κB signaling, apolipoprotein A–I, (apoA-I), the major protein component of HDL, attenuates palmitate-induced NF-κB activation. Further, vascular NF-κB signaling (IL-6, MCP-1, TNF-α) and macrophage markers (CD68, CD11c) induced by 24 weeks of a diabetogenic diet containing cholesterol (DDC) is reduced in human apoA-I overexpressing transgenic C57BL/6 mice compared to age-matched WT controls. Moreover, WT mice on DDC compared to a chow diet display increased gene expression of lipid raft markers such as Caveolin-1 and Flotillin-1, and inflammatory Toll-like receptors (TLRs) (TLR2, TLR4) in the vasculature. However apoA-I transgenic mice on DDC show markedly reduced expression of these genes. Finally, we show that in endothelial cells TLR4 is recruited into lipid rafts in response to palmitate, and that apoA-I prevents palmitate-induced TLR4 trafficking into lipid rafts, thereby blocking NF-κB activation. Thus, apoA-I overexpression might be a useful therapeutic tool against vascular inflammation

An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006

Annual meeting abstracts published by scientific societies often contain rich arrays of information that can be computationally mined and distilled to elucidate the state and dynamics of the subject field. We extracted and processed abstract data from the Society for Neuroscience (SFN) annual meeting abstracts during the period 2001–2006 in order to gain an objective view of contemporary neuroscience. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. Using natural language processing, text mining, and other data analysis techniques, we then examined the demographics and structure of the scientific collaboration network, the dynamics of the field over time, major research trends, and the structure of the sources of research funding. Some interesting findings include a high geographical concentration of neuroscience research in the north eastern United States, a surprisingly large transient population (66% of the authors appear in only one out of the six studied years), the central role played by the study of neurodegenerative disorders in the neuroscience community, and an apparent growth of behavioral/systems neuroscience with a corresponding shrinkage of cellular/molecular neuroscience over the six year period. The results from this work will prove useful for scientists, policy makers, and funding agencies seeking to gain a complete and unbiased picture of the community structure and body of knowledge encapsulated by a specific scientific domain