scholarly journals A mathematical model describes Drosophila sleep behavior in w1118 controls and in a hyposomnolent insomniac line

2015 ◽  
Author(s):  
Joshua M Diamond
Keyword(s):  
Mathematical Model ◽  
Goodness Of Fit ◽  
Model Organism ◽  
Fruit Fly ◽  
Model Fit ◽  
Sleep Architecture ◽  
Coefficient Of Determination ◽  
Sleep Behavior ◽  
Nighttime Sleep

The conserved nature of sleep in Drosophila has allowed the fruit fly to emerge in the last decade as a powerful model organism in which to study sleep. Recent sleep studies in Drosophila have focused on the discovery and characterization of hyposomnolent mutants. One common feature of these animals is a change in sleep architecture: sleep bout count tends to be greater, and sleep bout length lower, in hyposomnolent mutants. I propose a mathematical model, produced by least-squares nonlinear regression to fit the form Y = aX^b, which can explain sleep behavior in the healthy animal as well as previously-reported changes in total sleep and sleep architecture in hyposomnolent mutants. This model, fit to sleep data, yields coefficient of determination R squared, which describes goodness of fit. R squared is lower in hyposomnolent mutant insomniac as compared to control, indicating a poorer fit of the model to the data in insomniac. R squared also tends to be lower in daytime sleep as compared to nighttime sleep. My findings raise the possibility that low R squared is a feature of all hyposomnolent mutants, not just insomniac. If this were the case, R squared could emerge as a novel means by which sleep researchers might assess sleep dysfunction.

scholarly journals A mathematical model describes Drosophila sleep behavior in w1118 controls and in a hyposomnolent insomniac line

2015 ◽  
Author(s):  
Joshua M Diamond
Keyword(s):  
Mathematical Model ◽  
Goodness Of Fit ◽  
Model Organism ◽  
Fruit Fly ◽  
Model Fit ◽  
Sleep Architecture ◽  
Coefficient Of Determination ◽  
Sleep Behavior ◽  
Nighttime Sleep

The conserved nature of sleep in Drosophila has allowed the fruit fly to emerge in the last decade as a powerful model organism in which to study sleep. Recent sleep studies in Drosophila have focused on the discovery and characterization of hyposomnolent mutants. One common feature of these animals is a change in sleep architecture: sleep bout count tends to be greater, and sleep bout length lower, in hyposomnolent mutants. I propose a mathematical model, produced by least-squares nonlinear regression to fit the form Y = aX^b, which can explain sleep behavior in the healthy animal as well as previously-reported changes in total sleep and sleep architecture in hyposomnolent mutants. This model, fit to sleep data, yields coefficient of determination R squared, which describes goodness of fit. R squared is lower in hyposomnolent mutant insomniac as compared to control, indicating a poorer fit of the model to the data in insomniac. R squared also tends to be lower in daytime sleep as compared to nighttime sleep. My findings raise the possibility that low R squared is a feature of all hyposomnolent mutants, not just insomniac. If this were the case, R squared could emerge as a novel means by which sleep researchers might assess sleep dysfunction.

scholarly journals Goodness of fit to a mathematical model for Drosophila sleep behavior is reduced in hyposomnolent mutants

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1533 ◽  
Author(s):  
Joshua M. Diamond
Keyword(s):  
Mathematical Model ◽  
Goodness Of Fit ◽  
Model Organism ◽  
Fruit Fly ◽  
Model Fit ◽  
Sleep Architecture ◽  
Coefficient Of Determination ◽  
Sleep Behavior ◽  
Sleep Studies

The conserved nature of sleep in Drosophila has allowed the fruit fly to emerge in the last decade as a powerful model organism in which to study sleep. Recent sleep studies in Drosophila have focused on the discovery and characterization of hyposomnolent mutants. One common feature of these animals is a change in sleep architecture: sleep bout count tends to be greater, and sleep bout length lower, in hyposomnolent mutants. I propose a mathematical model, produced by least-squares nonlinear regression to fit the formY=aX∧b, which can explain sleep behavior in the healthy animal as well as previously-reported changes in total sleep and sleep architecture in hyposomnolent mutants. This model, fit to sleep data, yields coefficient of determinationRsquared, which describes goodness of fit.Rsquared is lower, as compared to control, in hyposomnolent mutantsinsomniacandfumin. My findings raise the possibility that lowRsquared is a feature of all hyposomnolent mutants, not justinsomniacandfumin. If this were the case,Rsquared could emerge as a novel means by which sleep researchers might assess sleep dysfunction.

scholarly journals Characterization of the genome of the mealybug Planococcus lilacinus, a model organism for studying whole-chromosome imprinting and inactivation

2002 ◽  
Vol 79 (2) ◽  
pp. 111-118 ◽  
Author(s):  
K. NAGA MOHAN ◽  
PARAMITA RAY ◽  
H. SHARAT CHANDRA
Keyword(s):  
Drosophila Melanogaster ◽  
Repetitive Sequences ◽  
Gc Content ◽  
Model Organism ◽  
Fruit Fly ◽  
Single Copy ◽  
Coding Sequences ◽  
Repeat Sequences ◽  
Chromosome Imprinting

The co-occurrence of three chromosome-wide phenomena – imprinting, facultative heterochromatization and diffuse centromere – in the mealybug Planococcus lilacinus makes investigation of the genomics of this species an attractive prospect. In order to estimate the complexity of the genome of this species, 300 random stretches of its DNA, constituting ∼0·1% of the genome, were sequenced. Coding sequences appear to constitute ∼53·5%, repeat sequences ∼44·5% and non-coding single-copy sequences ∼2% of the genome. The proportion of repetitive sequences in the mealybug is higher than that in the fruit fly Drosophila melanogaster (∼30%). The mealybug genome (∼220 Mb) is about 1·3 times the size of the fly genome (∼165 Mb) and its GC content (∼35%) less than that of the fly genome (∼40%). The relative abundance of various dinucleotides, as analysed by the method of Gentles and Karlin, shows that the dinucleotide signatures of the two species are moderately similar and that in the mealybug there is neither over-representation nor under-representation of any dinucleotide.

scholarly journals How well does your model fit the data?

2001 ◽  
Vol 3 (1) ◽  
pp. 49-55 ◽  
Author(s):  
M. J. Hall
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Computer Modelling ◽  
Synthetic Data ◽  
Model Fit ◽  
Coefficient Of Determination ◽  
Input Output

Despite almost five decades of activity on the computer modelling of input–output relationships, little general agreement has emerged on appropriate indices for the goodness-of-fit of a model to a set of observations of the pertinent variables. The coefficient of efficiency, which is closely allied in form to the coefficient of determination, has been widely adopted in many data mining and modelling exercises. Values of this coefficient close to unity are taken as evidence of good matching between observed and computed flows. However, studies using synthetic data have demonstrated that negative values of the coefficient of efficiency can occur both in the presence of bias in computed outputs, and when the computed volume of flow greatly exceeds the observed volume of flow. In contrast, the coefficient of efficiency lacks discrimination for cases close to perfect reproduction. In the latter case, a coefficient based upon the first differences of the data proves to be more helpful.

scholarly journals From R-squared to coefficient of model accuracy for assessing "goodness-of-fits"

2020 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
Goodness Of Fit ◽  
Classical Model ◽  
Model Performance ◽  
Model Fit ◽  
Coefficient Of Determination ◽  
Hydrological Models ◽  
Objective Functions ◽  
Model Accuracy ◽  
Sample Correlation ◽  
Model Residuals

Abstract. Modelers tend to focus more on advancing methods of statistical and mathematical modeling than developing novel techniques for comparing modeled results with observations or establishing metrics for model performance assessment. Perhaps solely the most extensively applied "goodness-of-fit" measure especially for assessing performance of regression models is the coefficient of determination R2. Normally, high R2 tends to be associated with an efficient model. Nevertheless, R2 has been cited to have no importance in the classical model of regression. Even in its use in descriptive statistics, R2 is known to have questionable justification. R2 is inadequate in assessing model performance because it does not give any information on the model residuals. Furthermore, R-squared can be low for an effective model. Contrastingly, a very poor model fit can yield high R2. Regressing X on Y yields R2 which is the same as that if Y is regressed on X thereby invalidating its use as a coefficient of determination. Taking into account the drawbacks of using R2, this paper introduces coefficient of model accuracy (CMA) the derivation of which comprises an analogy to the R2. However, instead of simply squaring an ordinary Pearson's product-moment correlation coefficient to obtain R2, CMA comprises the product of nonparametric sample correlation and model bias. Acceptability of the introduced method can be found demonstrated through comparison of results from simulations by hydrological models calibrated using CMA and other existing objective functions.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Quantitative investigation reveals distinct phases in Drosophila sleep

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xiaochan Xu ◽  
Wei Yang ◽  
Binghui Tian ◽  
Xiuwen Sui ◽  
Weilai Chi ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
General Pattern ◽  
Model Organism ◽  
Infrared Camera ◽  
Fruit Fly ◽  
Genetic Dissection ◽  
Power Law Distribution ◽  
Quantitative Investigation ◽  
Waking Up ◽  
Set Up

AbstractThe fruit fly, Drosophila melanogaster, has been used as a model organism for the molecular and genetic dissection of sleeping behaviors. However, most previous studies were based on qualitative or semi-quantitative characterizations. Here we quantified sleep in flies. We set up an assay to continuously track the activity of flies using infrared camera, which monitored the movement of tens of flies simultaneously with high spatial and temporal resolution. We obtained accurate statistics regarding the rest and sleep patterns of single flies. Analysis of our data has revealed a general pattern of rest and sleep: the rest statistics obeyed a power law distribution and the sleep statistics obeyed an exponential distribution. Thus, a resting fly would start to move again with a probability that decreased with the time it has rested, whereas a sleeping fly would wake up with a probability independent of how long it had slept. Resting transits to sleeping at time scales of minutes. Our method allows quantitative investigations of resting and sleeping behaviors and our results provide insights for mechanisms of falling into and waking up from sleep.

scholarly journals Direct Estimate of the Mutation Rate and the Distribution of Fitness Effects in the Yeast Saccharomyces cerevisiae

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 441-452
Author(s):  
Dominika M Wloch ◽  
Krzysztof Szafraniec ◽  
Rhona H Borts ◽  
Ryszard Korona
Keyword(s):  
Spontaneous Mutation ◽  
Fruit Fly ◽  
Diploid Cell ◽  
Induced Mutations ◽  
Direct Estimate ◽  
Yeast Saccharomyces Cerevisiae ◽  
Related Trait ◽  
First Time ◽  
Defective Mismatch Repair

Abstract Estimates of the rate and frequency distribution of deleterious effects were obtained for the first time by direct scoring and characterization of individual mutations. This was achieved by applying tetrad analysis to a large number of yeast clones. The genomic rate of spontaneous mutation deleterious to a basic fitness-related trait, that of growth rate, was U = 1.1 × 10−3 per diploid cell division. Extrapolated to the fruit fly and humans, the per generation rate would be 0.074 and 0.92, respectively. This is likely to be an underestimate because single mutations with selection coefficients s < 0.01 could not be detected. The distribution of s ≥ 0.01 was studied both for spontaneous and induced mutations. The latter were induced by ethyl methanesulfonate (EMS) or resulted from defective mismatch repair. Lethal changes accounted for ~30–40% of the scored mutations. The mean s of nonlethal mutations was fairly high, but most frequently its value was between 0.01 and 0.05. Although the rate and distribution of very small effects could not be determined, the joint share of such mutations in decreasing average fitness was probably no larger than ~1%.

scholarly journals Modelling Degradation and Replication Kinetics of the Zika Virus In Vitro Infection

Viruses ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 547
Author(s):  
Veronika Bernhauerová ◽  
Veronica V. Rezelj ◽  
Marco Vignuzzi
Keyword(s):  
Mathematical Model ◽  
Viral Infection ◽  
Host Cell ◽  
Decay Time ◽  
Zika Virus ◽  
Infectious Virus ◽  
Numerical Characterization ◽  
The Mathematical Model

Mathematical models of in vitro viral kinetics help us understand and quantify the main determinants underlying the virus–host cell interactions. We aimed to provide a numerical characterization of the Zika virus (ZIKV) in vitro infection kinetics, an arthropod-borne emerging virus that has gained public recognition due to its association with microcephaly in newborns. The mathematical model of in vitro viral infection typically assumes that degradation of extracellular infectious virus proceeds in an exponential manner, that is, each viral particle has the same probability of losing infectivity at any given time. We incubated ZIKV stock in the cell culture media and sampled with high frequency for quantification over the course of 96 h. The data showed a delay in the virus degradation in the first 24 h followed by a decline, which could not be captured by the model with exponentially distributed decay time of infectious virus. Thus, we proposed a model, in which inactivation of infectious ZIKV is gamma distributed and fit the model to the temporal measurements of infectious virus remaining in the media. The model was able to reproduce the data well and yielded the decay time of infectious ZIKV to be 40 h. We studied the in vitro ZIKV infection kinetics by conducting cell infection at two distinct multiplicity of infection and measuring viral loads over time. We fit the mathematical model of in vitro viral infection with gamma distributed degradation time of infectious virus to the viral growth data and identified the timespans and rates involved within the ZIKV-host cell interplay. Our mathematical analysis combined with the data provides a well-described example of non-exponential viral decay dynamics and presents numerical characterization of in vitro infection with ZIKV.

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