Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. Multicomponent persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for chemical and biological problems. Extensive numerical experiments involving more than 4,000 protein-ligand complexes from the PDBBind database and near 100,000 ligands and decoys in the DUD database are performed to test respectively the scoring power and the virtual screening power of the proposed topological approaches. It is demonstrated that the present approaches outperform the modern machine learning based methods in protein-ligand binding affinity predictions and ligand-decoy discrimination

How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

Psychosocial outcomes as motivations for urban gardening: A cross-cultural comparison of Swiss and Chilean gardeners

The range of benefits provided by urban greenspaces is reasonably well understood and a broad consensus has been reached that they provide habitats along with social ecosystem services, such as restoration, health and food. Domestic gardens, which are a primary node of contact between city residents and nature, typically represent a significant portion of the urban greenspaces, so the gardeners who manage them play an important role in maintaining the greenspaces within a city. In this way, gardeners voluntarily provide a public service so should be encouraged, but the motivations of individual gardeners have not been sufficiently studied. In this study, we address this research gap by using a 14 item ‘motivations for gardening’ scale to evaluate the motivations for gardening held by gardeners in different cultural contexts. We used questionnaires to collect data in three Swiss cities (Lausanne, Bern and Zürich; N = 409) and one Chilean city (Temuco; N = 167) and analysed the responses at both item and scale levels. Although significant differences between Swiss and Chilean responses were found for all individual scale items, a principal component analysis revealed nearly identical component structures for both the Swiss and Chilean samples. Three clear components were identified; restoration as the motivational component receiving the strongest agreement, followed by socialization, and then food production. Nearly identical component structures were found, with the same scale items loading against the same components, when the sample was divided according to age, gender, education and income. These results suggest that motivations for gardening are not context dependant but rather represent an inherent human condition that frames how gardeners manage and interact with their gardens. Acknowledgement of these human needs: especially regarding the restoration benefits that people gain from these spaces, in public policies related to management and regulation of green urban areas has the potential to contribute to the survival of urban gardens