Nanocrystals of MgO and CaO have been prepared by a modified aerogel/hypercritical drying/dehydration method. For nanocrystalline MgO (AP-MgO) surface areas ranged from 250 to 500 m 2 /g, whereas for AP-CaO 100-160 m 2 /g. These materials have been compared with more conventional (CP) microcrystalline samples of lower surface area with regard to (1) morphology (AP-samples (autoclave preparation) are tiny polyhedral crystallites, while CP-samples (conventional preparation) are larger, hexagonal platelets and cubes);(2) residual surface OH (AP-samples have less acidic OH, which are more isolated from each other; (3) acid gas adsorption (AP-samples adsorb more SO 2 and CO 2 at low pressures and room temperature and prefer monodentate rather than bidentate adsorption modes, but at higher pressures CP-samples adsorb more SO 2 and HCl apparently due to the formation of more well ordered multilayers); (4) destructive adsorption of organophosphorus compounds and chlorocarbons (AP-samples are superior due to higher surface areas and higher surface reactivities), and (5) very thin layers of transition metal oxides on the MgO and CaO nanocrystals that significantly enhance destructive adsorption capacities to the point where [M x O y ]AP-MgO and [M x O y ]-AP-CaO become stoichiometric in reaction with CCl 4 . The data are conclusive that the nanocrystals are more reactive than the microcrystals, and this is mainly attributed to morphological differences, including defects. However, intrinsic electronic effects due purely to "smallness" cannot be ruled out.

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The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified; in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem; in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart; and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions, and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series.

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Highlights► Our study provides new insights into the pre-regressional development of RTT. ► The pre-regression period should not be considered asymptomatic. ► Peculiarities in speech-language development are potential red flags for RTT.