Studies on the solid-state structure of two polymorphs of 4-methyl-2-nitroacetanilide (MNA) were conducted using magic-angle spinning (13)C, (15)N and (1)H NMR spectroscopy, together with first-principles computations of NMR shielding (including use of a program that takes explicit account of the translational symmetry inherent in crystalline structures). The effects on (13)C chemical shifts of side-chain rotations have been explored. Information derived from these studies was then incorporated within a systematic space-search methodology for elucidation of trial crystallographic structures from powder XRD.

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and ameliorate any risks to health or the environment posed by the presence of ENMs. Datadriven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity, and interpretation of the models they generate can be problematic. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large data sets, are not particularly good at feature selection, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets.We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with additional advantages that it works with small datasets, automatically selects descriptors, and provides improved interpretability of models.

While the risk management of engineered nanomaterials (ENMS) receives significant attention, there is still a limited understanding of how to select optimal risk management measures for reducing the risks of ENMs.

Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at different stages: (1) a novel James–Stein Decision Tree (JSDT) is proposed to generate better knowledge representations for DNNs, especially when the input data are in low-frequency or low-quality; (2) the DNNs output high-performing prediction result from the knowledge embedding inputs and behave as a teacher model for the following tree model; and (3) a novel distillable Gradient Boosted Decision Tree (dGBDT) is proposed to learn interpretable trees from the soft labels and make a comparable prediction as DNNs do. Extensive experiments on various machine learning tasks demonstrated the effectiveness of the proposed method.

Ritonavir has been reported in seven crystal forms notably form I, II, IIIb and IV, as well as a hydrate, an L-tyrosine co-crystal and a formamide solvate. A new form...