16,816 research outputs found
Difference in response reliability predicted by STRFs in the cochlear nuclei of barn owls
The brainstem auditory pathway is obligatory for all aural information. Brainstem auditory neurons must encode the level and timing of sounds, as well as their time-dependent spectral properties, the fine structure and envelope, which are essential for sound discrimination. This study focused on envelope coding in the two cochlear nuclei of the barn owl, nucleus angularis (NA) and nucleus magnocellularis (NM). NA and NM receive input from bifurcating auditory nerve fibers and initiate processing pathways specialized in encoding interaural time (ITD) and level (ILD) differences, respectively. We found that NA neurons, though unable to accurately encode stimulus phase, lock more strongly to the stimulus envelope than NM units. The spectrotemporal receptive fields (STRFs) of NA neurons exhibit a pre-excitatory suppressive field. Using multilinear regression analysis and computational modeling, we show that this feature of STRFs can account for enhanced across-trial response reliability, by locking spikes to the stimulus envelope. Our findings indicate a dichotomy in envelope coding between the time and intensity processing pathways as early as the level of the cochlear nuclei. This allows the ILD processing pathway to encode envelope information with greater fidelity than the ITD processing pathway. Furthermore, we demonstrate that the properties of the neurons’ STRFs can be quantitatively related to spike timing reliability
A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times
The paradigm for compartment models in epidemiology assumes exponentially
distributed incubation and removal times, which is not realistic in actual
populations. Commonly used variations with multiple exponentially distributed
variables are more flexible, yet do not allow for arbitrary distributions. We
present a new formulation, focussing on the SEIR concept that allows to include
general distributions of incubation and removal times. We compare the solution
to two types of agent-based model simulations, a spatially homogeneous one
where infection occurs by proximity, and a model on a scale-free network with
varying clustering properties, where the infection between any two agents
occurs via their link if it exists. We find good agreement in both cases.
Furthermore a family of asymptotic solutions of the equations is found in terms
of a logistic curve, which after a non-universal time shift, fits extremely
well all the microdynamical simulations. The formulation allows for a simple
numerical approach; software in Julia and Python is provided.Comment: 21 pages, 11 figures. v2 matches published version: improved
presentation (including title, abstract and references), results and
conclusions unchange
Controlled generation of large volumes of atmospheric clouds in a ground-based environmental chamber
Atmospheric clouds were generated in a 23,000 cubic meter environmental chamber as the first step in a two part study on the effects of contaminants on cloud formation. The generation procedure was modeled on the terrestrial generation mechanism so that naturally occurring microphysics mechanisms were operative in the cloud generation process. Temperature, altitude, liquid water content, and convective updraft velocity could be selected independently over the range of terrestrially realizable clouds. To provide cloud stability, a cotton muslin cylinder 29.3 meters in diameter and 24.2 meters high was erected within the chamber and continuously wetted with water at precisely the same temperature as the cloud. The improved instrumentation which permitted fast, precise, and continual measurements of cloud temperature and liquid water content is described
Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease
The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice
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