scholarly journals Additive and multiplicative probabilistic models of infant looking times

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11771
Author(s):  
Matuš Šimkovic ◽  
Birgit Träuble
Keyword(s):  
Gamma Distribution ◽  
Probabilistic Models ◽  
Additive Model ◽  
Predictive Performance ◽  
Absolute Error ◽  
Habituation Rate ◽  
Age Cohorts ◽  
Orientation Contrast ◽  
Multiplicative Models ◽  
The Impact

Additive and multiplicative regression models of habituation were compared regarding the fit to looking times from a habituation experiment with infants aged between 3 and 11 months. In contrast to earlier studies, the current study considered multiple probability distributions, namely Weibull, gamma, lognormal and normal distribution. In the habituation experiment the type of contrast between the habituation and the test trial was varied (luminance, color or orientation contrast), crossed with the number of habituation trials (1, 3, 5, or 7 habituation trials) and crossed with three age cohorts (4, 7, 10 months). The initial mean LT to dark stimuli (around 3.7 s) was considerably shorter than the mean LT to green and gray stimuli (around 5 s). Infants showed the strongest dishabituation to changes from dark to bright (luminance contrast) and weak-to-no dishabituation to a 90-degrees rotation of the gray stimuli (orientation contrast). The dishabituation was stronger after five and seven habituation trials, but the result was not statistically robust. The gamma distribution showed the best fit in terms of log-likelihood and mean absolute error and the best predictive performance. Furthermore, the gamma distribution showed small correlations between parameters relative to other models. The normal additive model showed an inferior fit and medium correlations between the parameters. In particular, the positive correlation between the initial looking time (LT) and the habituation rate was likely responsible for a different interpretation relative to the multiplicative models of the main effect of age on the habituation rate. Otherwise, the additive and multiplicative models provided similar statistical conclusions. The performance of the model versions without pooling and with partial pooling across participants (also called random-effects, multi-level or hierarchical models) were compared. The latter type of models showed worse data fit but more precise predictions and reduced correlations between the parameters. The performance of model variants with auto-regressive time structures were explored but showed considerably worse fit. The performance of quadratic models that allowed non-monotonic changes in LTs were investigated as well. However, when fitted with LT data, these models did not produce non-monotonic change in LTs. The study underscores the utility of partial-pooling models in terms of providing more accurate predictions. Further, it agrees with previous research in that a multiplicative LT model is preferable. Nevertheless, the current results suggest that the impact of the choice of an additive model on the statistical inference is less dramatic then previously assumed.

scholarly journals Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size

2021 ◽  
Vol 25 ◽  
pp. 233121652110661
Author(s):  
Elaheh Shafieibavani ◽  
Benjamin Goudey ◽  
Isabell Kiral ◽  
Peter Zhong ◽  
Antonio Jimeno-Yepes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cochlear Implant ◽  
Sample Size ◽  
Clinical Decision Making ◽  
Linear Models ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Absolute Error ◽  
Learning Approaches ◽  
The Impact

While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual’s hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance. We conduct a thorough examination of machine learning approaches to predict word recognition scores (WRS) measured approximately 12 months after implantation in adults with post-lingual hearing loss. This is the largest retrospective study of cochlear implant outcomes to date, evaluating 2,489 cochlear implant recipients from three clinics. We demonstrate that while machine learning models significantly outperform linear models in prediction of WRS, their overall accuracy remains limited (mean absolute error: 17.9-21.8). The models are robust across clinical cohorts, with predictive error increasing by at most 16% when evaluated on a clinic excluded from the training set. We show that predictive improvement is unlikely to be improved by increasing sample size alone, with doubling of sample size estimated to only increasing performance by 3% on the combined dataset. Finally, we demonstrate how the current models could support clinical decision making, highlighting that subsets of individuals can be identified that have a 94% chance of improving WRS by at least 10% points after implantation, which is likely to be clinically meaningful. We discuss several implications of this analysis, focusing on the need to improve and standardize data collection.

scholarly journals Window operation behaviour and indoor air quality during lockdown: A monitoring-based simulation-assisted study in London

2021 ◽  
pp. 014362442110177
Author(s):  
Farhang Tahmasebi ◽  
Yan Wang ◽  
Elizabeth Cooper ◽  
Daniel Godoy Shimizu ◽  
Samuel Stamp ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Energy Demand ◽  
Natural Ventilation ◽  
Probabilistic Models ◽  
Performance Model ◽  
Outdoor Environment ◽  
Ventilation Strategies ◽  
The Impact

The Covid-19 outbreak has resulted in new patterns of home occupancy, the implications of which for indoor air quality (IAQ) and energy use are not well-known. In this context, the present study investigates 8 flats in London to uncover if during a lockdown, (a) IAQ in the monitored flats deteriorated, (b) the patterns of window operation by occupants changed, and (c) more effective ventilation patterns could enhance IAQ without significant increases in heating energy demand. To this end, one-year’s worth of monitored data on indoor and outdoor environment along with occupant use of windows has been used to analyse the impact of lockdown on IAQ and infer probabilistic models of window operation behaviour. Moreover, using on-site CO2 data, monitored occupancy and operation of windows, the team has calibrated a thermal performance model of one of the flats to investigate the implications of alternative ventilation strategies. The results suggest that despite the extended occupancy during lockdown, occupants relied less on natural ventilation, which led to an increase of median CO2 concentration by up to 300 ppm. However, simple natural ventilation patterns or use of mechanical ventilation with heat recovery proves to be very effective to maintain acceptable IAQ. Practical application: This study provides evidence on the deterioration of indoor air quality resulting from homeworking during imposed lockdowns. It also tests and recommends specific ventilation strategies to maintain acceptable indoor air quality at home despite the extended occupancy hours.

scholarly journals Impact of rs2414096 polymorphism of CYP19 gene on susceptibility of polycystic ovary syndrome and hyperandrogenism in Kashmiri women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sairish Ashraf ◽  
Shayaq Ul Abeer Rasool ◽  
Mudasar Nabi ◽  
Mohd Ashraf Ganie ◽  
Shariq R. Masoodi ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Clinical Manifestations ◽  
Additive Model ◽  
Genotypic Frequency ◽  
Ovary Syndrome ◽  
Significant Difference ◽  
Cyp19 Gene ◽  
Reproductive Endocrine ◽  
The Impact

AbstractPolycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder in pre-menopausal women having complex pathophysiology. Several candidate genes have been shown to have association with PCOS. CYP19 gene encodes a key steroidogenic enzyme involved in conversion of androgens into estrogens. Previous studies have reported contradictory results with regard to association of SNP rs2414096 in CYP19 gene with PCOS and hyperandrogenism in different ethnic populations. Present study was aimed to investigate the impact of SNP rs2414096 polymorphism of CYP19 gene on susceptibility of PCOS and hyperandrogenism in Kashmiri women. Further we also studied the genotypic-phenotypic association for various clinical and biochemical parameters of this polymorphism. Case control study. 394 PCOS cases diagnosed on the basis of Rotterdam criteria and age matched 306 healthy women. We found a significant differences in genotypic frequency (χ2 = 18.91, p < 0.05) as well as allele frequency (OR 0.63, CI 0.51–0.78, χ2 = 17.66, p < 0.05) between PCOS women and controls. The genotype–phenotype correlation analysis showed a significant difference in FG score (p = 0.047) and alopecia (p = 0.045) between the three genotypes. Also, the androgen excess markers like DHEAS (p < 0.001), Androstenedione (p < 0.001), Testosterone (p < 0.001) and FAI (p = 0.005) were significantly elevated in GG genotype and showed a significant difference in additive model in PCOS women. rs2414096 polymorphism of CYP19 gene is associated with the risk of PCOS as well as with clinical and biochemical markers of hyperandrogenism, hence suggesting its role in clinical manifestations of PCOS in Kashmiri women.

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals Predictive performance of international COVID-19 mortality forecasting models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph Friedman ◽  
Patrick Liu ◽  
Christopher E. Troeger ◽  
Austin Carter ◽  
Robert C. Reiner ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Absolute Error ◽  
Decision Makers ◽  
Government Interventions ◽  
World Region ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Pandemic Response ◽  
Alternative Scenarios ◽  
Absolute Percent Error

AbstractForecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase (https://github.com/pyliu47/covidcompare) can be used to compare predictions and evaluate predictive performance going forward.

Eviction and Voter Turnout: The Political Consequences of Housing Instability

2021 ◽  
pp. 003232922110507
Author(s):  
Gillian Slee ◽  
Matthew Desmond
Keyword(s):  
Voter Turnout ◽  
Causal Effect ◽  
Additive Model ◽  
The United States ◽  
Data Set ◽  
Endogeneity Bias ◽  
Political Consequences ◽  
Housing Costs ◽  
Election Results ◽  
The Impact

In recent years, housing costs have outpaced incomes in the United States, resulting in millions of eviction filings each year. Yet no study has examined the link between eviction and voting. Drawing on a novel data set that combines tens of millions of eviction and voting records, this article finds that residential eviction rates negatively impacted voter turnout during the 2016 presidential election. Results from a generalized additive model show eviction’s effect on voter turnout to be strongest in neighborhoods with relatively low rates of displacement. To address endogeneity bias and estimate the causal effect of eviction on voting, the analysis treats commercial evictions as an instrument for residential evictions, finding that increases in neighborhood eviction rates led to substantial declines in voter turnout. This study demonstrates that the impact of eviction reverberates far beyond housing loss, affecting democratic participation.

scholarly journals The labor potential reproduction in depopulation period of 2019–2035 and the compensating role of migration

2019 ◽  
Vol 16 (5) ◽  
pp. 70-84
Author(s):  
A. V. Topilin ◽  
O. D. Vorob’eva ◽  
A. S. Maksimova
Keyword(s):  
Young People ◽  
Labor Force ◽  
Forecast Period ◽  
Age Cohorts ◽  
Time Migration ◽  
Working Age ◽  
Working Age Population ◽  
The Impact ◽  
Labor Potential

Purpose of the research. To examine the dynamics of reproduction of labor potential and labor supply in Russia for the period up to 2035, depending on the impact factors of its reproduction: generation change (changes in the proportion of cohort, entering and leaving at the age composition of the labor potential), fertility and mortality rates, migration balance in the individual age cohorts.  Materials and methods. The concept of “replacement of generations” is introduced. The coefficient of replacement of generations is developed and its value for labor potential of Russia for the period up to 2035 is calculated. The influence of factors of natural population movement on the dynamics of labor potential is analyzed. The compensating role of the migration factor in the conditions of labor potential reduction is calculated. Russian regions were grouped according to the following criteria: the direction and intensity of changes in the working-age population in 2020–2035 and the proportion of young people aged 0–15 years.  Results.  – There will be the reduction and aging of labor potential during the second stage of depopulation due to demographic factors.  – The decline in the working-age population in the second wave of depopulation is expected to be smaller than in the first wave.  – In Russia there will be a decrease in the replacement of generations in the contingent of people of the working age.  – The growth of Total Fertility Rate (TFR) in the forecast period should not be expected, because until 2030 a gradual decrease in the number of women of reproductive age is expected.  – The deepest failure in the population of the working age will be in 2020–2025 accounting for 1.7 million people according to the average variant of the forecast.  – In the forecast period, the labor force in the most productive age of 25–39 years will decrease by 10.5 million people, and the employment rate will decrease from 65.5% to 63.5%.  – Regional features of the formation of demand and supply of labor force in Russia cause the allocation of six homogeneous groups of regions.  – In order to compensate for the losses, it is necessary to increase the migration gain in the average version of ROSSTAT forecast by 2–2.5 times.  Conclusion. To meet the needs of the economy in the labor force in the forecast period, it is necessary to solve two interrelated tasks: compensation for the reduction of labor potential and ensuring the quality of labor potential necessary for the introduction of new technologies and digitalization of the economy. The unfavorable situation with the formation of labor resources is exacerbated by regional imbalances in the distribution of labor potential and differences in its quality across the country. In the future, migration is once again the only source of replenishment of labor potential and replacement of generations, despite the risks of quality losses due to the emigration of highly qualified persons and young people. It is necessary to take measures to increase the compensatory role of migration in the next five – six years. At the same time, migration policy measures should be considered in close conjunction with other measures to stimulate fertility and reduce mortality, ensuring a positive impact on the components of the population growth.  

scholarly journals An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction

2020 ◽  
Author(s):  
Fanny Mollandin ◽  
Andrea Rau ◽  
Pascal Croiseau
Keyword(s):  
Linkage Disequilibrium ◽  
Qtl Mapping ◽  
Effect Size ◽  
Genomic Prediction ◽  
Simulated Data ◽  
Predictive Performance ◽  
Real Data ◽  
Sliding Windows ◽  
Size Classes ◽  
The Impact

ABSTRACTTechnological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures, phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium. We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak linkage disequilibrium with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.

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