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The acylation of unsymmetrical N-benzylbispidinols in aromatic solvents without an external base led to the formation of supramolecular gels, which possess different thicknesses and degrees of stability depending on the substituents in para-positions of the benzylic group as well as on the nature of the acylating agent and of the solvent used. Structural features of the native gels as well as of their dried forms were studied by complementary techniques including Fourier-transform infrared (FTIR) and attenuated total reflection (ATR) spectroscopy, atomic force microscopy (AFM), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and small-angle X-ray scattering and diffraction (SAXS). Structures of the key crystalline compounds were established by X-ray diffraction. An analysis of the obtained data allowed speculation on the crucial structural and condition factors that governed the gel formation. The most important factors were as follows: (i) absence of base, either external or internal; (ii) presence of HCl; (iii) presence of carbonyl and hydroxyl groups to allow hydrogen bonding; and (iv) presence of two (hetero)aromatic rings at both sides of the molecule. The hydrogen bonding involving amide carbonyl, hydroxyl at position 9, and, very probably, ammonium N-H+ and Cl− anion appears to be responsible for the formation of infinite molecular chains required for the first step of gel formation. Subsequent lateral cooperation of molecular chains into fibers occurred, presumably, due to the aromatic π−π-stacking interactions. Supercritical carbon dioxide drying of the organogels gave rise to aerogels with morphologies different from that of air-dried samples.

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Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a number of large-scale empirical studies on the transfer performance of such models have been conducted, there is not yet an agreed-upon set of control baselines, evaluation practices, and metrics to report, which often hinders a nuanced and calibrated understanding of the real efficacy of the methods. We share an evaluation standard that aims to quantify and communicate transfer learning performance in an informative and accessible setup. This is done by baking a number of simple yet critical control baselines in the evaluation method, particularly the 'blind-guess' (quantifying the dataset bias), 'scratchmodel' (quantifying the architectural contribution), and 'maximal-supervision' (quantifying the upper-bound). To demonstrate how the evaluation standard can be employed, we provide an example empirical study investigating a few basic questions about self-supervised learning. For example, using this standard, the study shows the effectiveness of existing self-supervised pre-training methods is skewed towards image classification tasks versus dense pixel-wise predictions. In general, we encourage using/reporting the suggested control baselines in evaluating transfer learning in order to gain a more meaningful and informative understanding.

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The O-polysaccharide was isolated by mild acid degradation of the lipopolysaccharide of Enterobacter cloacae G3421 and studied by sugar analysis along with 1D and 2D (1)H and (13)C NMR spectroscopy. In addition, partial solvolysis with anhydrous trifluoroacetic acid was applied, which cleaved selectively the α-l-rhamnopyranosidic linkages. The following structure of the branched hexasaccharide repeating unit was established. The O-polysaccharide studied shares the β-l-Rhap-(1→4)-α-l-Rhap-(1→2)-α-l-Rhap trisaccharide fragment with the O-polysaccharide of Shigella boydii type 18. The O-antigen gene cluster of E. cloacae G3421 was sequenced. Functions of genes in the cluster, including those for glycosyltransferases, were tentatively assigned by a comparison with sequences in the available databases and found to be consistent with the O-polysaccharide structure.