Frequency-Invariant Representation of Interaural Time Differences in Mammals

Interaural time differences (ITDs) are the major cue for localizing low-frequency sounds. The activity of neuronal populations in the brainstem encodes ITDs with an exquisite temporal acuity of about . The response of single neurons, however, also changes with other stimulus properties like the spectral composition of sound. The influence of stimulus frequency is very different across neurons and thus it is unclear how ITDs are encoded independently of stimulus frequency by populations of neurons. Here we fitted a statistical model to single-cell rate responses of the dorsal nucleus of the lateral lemniscus. The model was used to evaluate the impact of single-cell response characteristics on the frequency-invariant mutual information between rate response and ITD. We found a rough correspondence between the measured cell characteristics and those predicted by computing mutual information. Furthermore, we studied two readout mechanisms, a linear classifier and a two-channel rate difference decoder. The latter turned out to be better suited to decode the population patterns obtained from the fitted model

Computational Identification and Analysis of the Key Biosorbent Characteristics for the Biosorption Process of Reactive Black 5 onto Fungal Biomass

The performances of nine biosorbents derived from dead fungal biomass were investigated for their ability to remove Reactive Black 5 from aqueous solution. The biosorption data for removal of Reactive Black 5 were readily modeled using the Langmuir adsorption isotherm. Kinetic analysis based on both pseudo-second-order and Weber-Morris models indicated intraparticle diffusion was the rate limiting step for biosorption of Reactive Black 5 on to the biosorbents. Sorption capacities of the biosorbents were not correlated with the initial biosorption rates. Sensitivity analysis of the factors affecting biosorption examined by an artificial neural network model showed that pH was the most important parameter, explaining 22%, followed by nitrogen content of biosorbents (16%), initial dye concentration (15%) and carbon content of biosorbents (10%). The biosorption capacities were not proportional to surface areas of the sorbents, but were instead influenced by their chemical element composition. The main functional groups contributing to dye sorption were amine, carboxylic, and alcohol moieties. The data further suggest that differences in carbon and nitrogen contents of biosorbents may be used as a selection index for identifying effective biosorbents from dead fungal biomass

Precision Newborn Screening for Lysosomal Disorders

Purpose: The implementation of newborn screening for lysosomal disorders has uncovered overall poor specificity, psychosocial harm experienced by caregivers, and costly follow-up testing of false-positive cases. We report an informatics solution proven to minimize these issues. Methods: The Kentucky Department for Public Health outsourced testing for mucopolysaccharidosis type I (MPS I) and Pompe disease, conditions recently added to the recommended uniform screening panel, plus Krabbe disease, which was added by legislative mandate. A total of 55,161 specimens were collected from infants born over 1 year starting from February 2016. Testing by tandem mass spectrometry was integrated with multivariate pattern recognition software (Collaborative Laboratory Integrated Reports), which is freely available to newborn screening programs for selection of cases for which a biochemical second-tier test is needed. Results: Of five presumptive positive cases, one was affected with infantile Krabbe disease, two with Pompe disease, and one with MPS I. The remaining case was a heterozygote for the latter condition. The false-positive rate was 0.0018% and the positive predictive value was 80%. Conclusion: Postanalytical interpretive tools can drastically reduce false-positive outcomes, with preliminary evidence of no greater risk of false-negative events, still to be verified by long-term surveillance

Serum levels of cytokines and C-reactive protein in acute ischemic stroke patients, and their relationship to stroke lateralization, type, and infarct volume

There is increasing evidence that inflammation plays an important role in the progression of acute ischemic stroke (AIS). The primary aims of this study were to examine the serum levels of 13 cytokines, C-reactive protein (CRP), glucose, and hemoglobin in AIS patients, and their relationship to stroke lateralization, type, and infarct volume. Forty-five patients with AIS were evaluated. Blood samples were taken within 72 h, and volumetric analyses performed within 1–7 days after AIS onset. Cytokines were measured in serum from all patients and from 40 control subjects using Luminex Bio-Plex XMap technology. The levels of interleukin (IL)-1ra (p < 0.001), IL-6 (p < 0.001), IL-8 (p < 0.001), IL-9 (p = 0.038), IL-10 (p = 0.001), IL-12 (p = 0.001), IL-18 (p < 0.001), and GRO-α (CXCL1) (p = 0.017) were significantly higher in the AIS patients than in the controls. The IL-8 level was significantly correlated with age in the patient group (r = 0.52, p < 0.001). None of the variables were found to be associated with stroke lateralization. Infarct volume was significantly positively correlated with CRP level (r = 0.47, p = 0.005). Patients with radiologically confirmed infarctions had significantly elevated serum levels of GRO-α (p = 0.023). The cytokine profile of the AIS patients supports not only earlier findings of a proinflammatory response but also early activation of endogenous immunosuppressive mechanisms. Novel findings of this study are elevated serum levels of IL-9 and GRO-α. Elevated GRO-α in AIS patients with radiologically confirmed infarctions suggests that GRO-α is specific for stroke of known etiology. Our results indicate that CRP plays an important role in the progression of cerebral tissue injury

Identification of neutral biochemical network models from time series data

<p>Abstract</p> <p>Background</p> <p>The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, <it>i.e</it>., if it is constructed according to strict guidelines.</p> <p>Results</p> <p>In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity.</p> <p>Conclusion</p> <p>The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium <it>Lactococcus lactis </it>and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.</p

Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions

The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m-2 (Y1), spikelets per fertile tillers (Y2), florets per spikelet- (Y3), seed numbers per spikelet (Y4) and seed weight (Y5) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y1 was the most important seed yield component describing the Z and Y2 was the least. The total direct effects of the Y1, Y3 and Y5 to the Z were positive while Y4 and Y2 were weakly negative. The total effects (directs plus indirects) of the components were positively contributed to the Z by path analyses. The seed yield components Y1, Y2, Y4 and Y5 were significantly (P<0.001) correlated with the Z for 4 years totally, while in the individual years, Y2 were not significant correlated with Y3, Y4 and Y5 by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y1, Y2 and Y3 would be effective for breeding programs in grasses. Furthermore, it is the most important that, via ridge regression, a steady algorithm model between Z and the five yield components was founded, which can be closely estimated the seed yield via the components