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

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

The Composition Of Normative Groups And Diagnostic Decision Making: Shooting Ourselves In The Foot

Purpose: The normative group of a norm-referenced test is intended to provide a basis for interpreting test scores. However, the composition of the normative group may facilitate or impede different types of diagnostic interpretations. This article considers who should be included in a normative sample and how-this decision must be made relative to the purpose for which a test is intended. Method: The way in which the composition of the normative sample affects classification accuracy is demonstrated through a test review followed by a simulation study. The test review examined the descriptions of the normative group in a sample of 32 child language tests. The mean performance reported in the test manual for the sample of language impaired children was compared with the sample's norms, which either included or excluded children with language impairment. For the simulation, 2 contrasting normative procedures were modeled. The first procedure included a mixed group of representative cases (language impaired and normal cases). The second procedure excluded the language impaired cases from the norm. Results: Both the data obtained from test manuals and the data simulation based on population characteristics supported our claim that use of mixed normative groups decreases the ability to accurately identify language impairment. Tests that used mixed norms had smaller differences between the normative and language impaired groups in comparison with tests that excluded children with impairment within the normative sample. The simulation demonstrated mixed norms that lowered the group mean and increased the standard deviation, resulting in decreased classification accuracy. Conclusions: When the purpose of testing is to identify children with impaired language skills, including children with language impairment in the normative sample can reduce identification accuracy.Communication Sciences and Disorder