Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project.

An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, e.g. Dev. Cogn. Neur. 32:43-54, 2018; PLoS Med. 12(3):e1001779, 2015; Elam and Van Essen, Enc. Comp. Neur., 2013, as well as in psychiatric cohorts, e.g. Trans. Psych. 10(1):100, 2020; Mol. Psych. 19:659-667, 2014; Mol. Aut. 8:24, 2017; Eur. Child and Adol. Psych. 24(3):265-281, 2015. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researchers aim to integrate relationships across multiple measures. Here we aim to evaluate different imputation strategies to fill in missing values in clinical data from a large (total N = 764) and deeply phenotyped (i.e. range of clinical and cognitive instruments administered) sample of N = 453 autistic individuals and N = 311 control individuals recruited as part of the EU-AIMS Longitudinal European Autism Project (LEAP) consortium. In particular, we consider a total of 160 clinical measures divided in 15 overlapping subsets of participants. We use two simple but common univariate strategies-mean and median imputation-as well as a Round Robin regression approach involving four independent multivariate regression models including Bayesian Ridge regression, as well as several non-linear models: Decision Trees (Extra Trees., and Nearest Neighbours regression. We evaluate the models using the traditional mean square error towards removed available data, and also consider the Kullback-Leibler divergence between the observed and the imputed distributions. We show that all of the multivariate approaches tested provide a substantial improvement compared to typical univariate approaches. Further, our analyses reveal that across all 15 data-subsets tested, an Extra Trees regression approach provided the best global results. This not only allows the selection of a unique model to impute missing data for the LEAP project and delivers a fixed set of imputed clinical data to be used by researchers working with the LEAP dataset in the future, but provides more general guidelines for data imputation in large scale epidemiological studies.

Effect of adsorbents and chemical treatments on the removal of strontium from aqueous solutions.

In the present investigation, three different solid wastes namely almond green hull, eggplant hull, and moss were initially treated and used as adsorbents for the adsorption of strontium ion from aqueous solutions. Adsorbent types and chemical treatments are proved to have effective roles on the adsorption of Sr(II) ion. Among the three adsorbents, almond green hull demonstrated strong affinity toward strontium ion in different solutions. The effectiveness of this new adsorbent was studied in batch adsorption mode under a variety of experimental conditions such as: different chemical treatments, various amounts of adsorbent, and initial metal-ion concentration. The optimum doses of adsorbent for the maximum Sr(II) adsorption were found to be 0.2 and 0.3 g for 45 and 102 mg L(-1) solutions, respectively. High Sr(II) adsorption efficiencies were achieved only in the first 3 min of adsorbent's contact time. The kinetics of Sr(II) adsorption on almond green hull was also examined and it was observed that it follows the pseudo second-order behavior. Both Langmuir and Freundlich models well predicted the experimental adsorption isotherm data. The maximum adsorption capacity on almond green hull was found to be 116.3 mg g(-1). The present study also confirmed that these low cost agriculture byproducts could be used as efficient adsorbents for the removal of strontium from wastewater streams.

Studies on adsorption of mercury from aqueous solution on activated carbons prepared from walnut shell.

The adsorption ability of a powdered activated carbons (PAC) derived from walnut shell was investigated in an attempt to produce more economic and effective sorbents for the control of Hg(II) ion from industrial liquid streams. Carbonaceous sorbents derived from local walnut shell, were prepared by chemical activation methods using ZnCl(2) as activating reagents. Adsorption of Hg(II) from aqueous solutions was carried out under different experimental conditions by varying treatment time, metal ion concentration, pH and solution temperature. It was shown that Hg(II) uptake decreases with increasing pH of the solution. The proper choice of preparation conditions were resulted in microporous activated carbons with different BET surface areas of 780 (Carbon A, 1:0.5 ZnCl(2)) and 803 (Carbon B, 1:1 ZnCl(2))m(2)/g BET surface area. The monolayer adsorption capacity of these particular adsorbents were obtained as 151.5 and 100.9 mg/g for carbons A and B, respectively. It was determined that Hg(II) adsorption follows both Langmuir and Freundlich isotherms as well as pseudo-second-order kinetics.

Removal of mercury from water by carbonaceous sorbents derived from walnut shell.

The adsorption ability of a powdered activated carbon (PAC) derived from walnut shell was investigated in an attempt to produce more economic and effective sorbent for the control of Hg(II) ion from industrial liquid streams. Carbonaceous sorbents derived from Iranian walnut shell (WS) were prepared by chemical activation method using ZnCl(2) as an activating reagent. To the best of our knowledge, this adsorbent was not used before for removing mercury from water. Adsorption of Hg(II) from aqueous solutions was carried out under different experimental conditions by varying treatment time, metal ion concentration, adsorbent amount, pH and solution temperature. It was determined that Hg(II) adsorption follows both Langmuir and Freundlich isotherms as well as pseudo-second-order kinetics. It was also shown that Hg(II) uptake decreases with increasing pH of the solution. The proper choice of preparation conditions resulted in a microporous activated carbon with 0.45 g/cm(3) density, 737 mg/g iodine number and 780 m(2)/g BET surface area. The monolayer sorption capacity of this optimum adsorbent was obtained as 151.5 mg/g.