Project description:Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies--such as constitutive expression or graded response--for regulating protein levels in response to environmental inputs. We propose a general framework-here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient-to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.
Project description:During critical periods, neural circuits develop to form receptive fields that adapt to the sensory environment and enable optimal performance of relevant tasks. We hypothesized that early exposure to background noise can improve signal-in-noise processing, and the resulting receptive field plasticity in the primary auditory cortex can reveal functional principles guiding that important task. We raised rat pups in different spectro-temporal noise statistics during their auditory critical period. As adults, they showed enhanced behavioral performance in detecting vocalizations in noise. Concomitantly, encoding of vocalizations in noise in the primary auditory cortex improves with noise-rearing. Significantly, spectro-temporal modulation plasticity shifts cortical preferences away from the exposed noise statistics, thus reducing noise interference with the foreground sound representation. Auditory cortical plasticity shapes receptive field preferences to optimally extract foreground information in noisy environments during noise-rearing. Early noise exposure induces cortical circuits to implement efficient coding in the joint spectral and temporal modulation domain.
Project description:People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.
Project description:There is enduring debate over the question of which early-life effects are adaptive and which ones are not. Mathematical modelling shows that early-life effects can be adaptive in environments that have particular statistical properties, such as reliable cues to current conditions and high autocorrelation of environmental states. However, few empirical studies have measured these properties, leading to an impasse. Progress, therefore, depends on research that quantifies cue reliability and autocorrelation of environmental parameters in real environments. These statistics may be different for social and non-social aspects of the environment. In this paper, we summarize evolutionary models of early-life effects. Then, we discuss empirical data on environmental statistics from a range of disciplines. We highlight cases where data on environmental statistics have been used to test competing explanations of early-life effects. We conclude by providing guidelines for new data collection and reflections on future directions. This article is part of the theme issue 'Developing differences: early-life effects and evolutionary medicine'.
Project description:Initiating an eye movement is slowed if the saccade is directed to a location that has been fixated in the recent past. We show that this inhibitory effect is modulated by the temporal statistics of the environment: If a return location is likely to become behaviorally relevant, inhibition of return is absent. By fitting an accumulator model of saccadic decision-making, we show that the inhibitory effect and the sensitivity to local statistics can be dissociated in their effects on the rate of accumulation of evidence, and the threshold controlling the amount of evidence needed to generate a saccade.