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To integrate nanophotonics into light-based technologies, it is critical to elicit a desired optical response from its fundamental component, a nanoresonator. Because the optical resonance of a nanoresonator depends strongly on its base material and structural features, machine learning has been contemplated to enhance the design and optimization processes. However, its accuracy in searching the vast parameter space of nanophotonics still poses unresolved questions. Here, we show how the choice of objective functions, in combination with trained neural networks, can drastically change the optimization process-even for a simple nanophotonic structure. To assess how different objective functions select the correct structural parameters that generate a desired optical Mie response, we use a simple core-shell, all-dielectric nanostructure as the benchmark. By controlling the proportion of training data, which represents the "experience" level, we also quantify how the various objective functions perform in finding the ground-truth parameters. Our findings demonstrate that certain objective functions exhibit improved accuracy when used with highly "experienced" neural networks. Surprisingly, we also find other objective functions that perform better when paired with less "experienced" neural networks. Taken together, our results emphasize that it is critical to understand how neural networks are coupled to optimization schemes, as is evident even when a simple core-shell nanostructure is used.

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BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping).RESULTSAmong 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68-0.71), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).CONCLUSIONSStructural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.TRIAL REGISTRATIONClinicalTrials.gov NCT00608764.FUNDINGThis study was supported by NIH grants K23 HL133438 and R21EB027891 and an American Thoracic Foundation 2018 Unrestricted Research Grant. The COPDGene study is supported by NIH grants NHLBI U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprising AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion.

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A systems perspective on diverse phenotypes, mechanisms of infection, and responses to environmental stresses can lead to considerable advances in agriculture and medicine. A significant promise of systems biology within plants is the development of disease-resistant crop varieties, which would maximize yield output for food, clothing, building materials, and biofuel production. A systems or "-omics" perspective frames the next frontier in the search for enhanced knowledge of plant network biology. The functional understanding of network structure and dynamics is vital to expanding our knowledge of how the intercellular communication processes are executed. This review article will systematically discuss various levels of organization of systems biology beginning with the building blocks termed "-omes" and ending with complex transcriptional and protein-protein interaction networks. We will also highlight the prevailing computational modeling approaches of biological regulatory network dynamics. The latest developments in the "-omics" approach will be reviewed and discussed to underline and highlight novel technologies and research directions in plant network biology.

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The highly collaborative research sponsored by the NSF-funded Assembling the Porifera Tree of Life (PorToL) project is providing insights into some of the most difficult questions in metazoan systematics. Our understanding of phylogenetic relationships within the phylum Porifera has changed considerably with increased taxon sampling and data from additional molecular markers. PorToL researchers have falsified earlier phylogenetic hypotheses, discovered novel phylogenetic alliances, found phylogenetic homes for enigmatic taxa, and provided a more precise understanding of the evolution of skeletal features, secondary metabolites, body organization, and symbioses. Some of these exciting new discoveries are shared in the papers that form this issue of Integrative and Comparative Biology. Our analyses of over 300 nearly complete 28S ribosomal subunit gene sequences provide specific case studies that illustrate how our dataset confirms new hypotheses of sponge evolution. We recovered monophyletic clades for all 4 classes of sponges, as well as the 4 major clades of Demospongiae (Keratosa, Myxospongiae, Haploscleromorpha, and Heteroscleromorpha), but our phylogeny differs in several aspects from traditional classifications. In most major clades of sponges, families within orders appear to be paraphyletic. Although additional sampling of genes and taxa are needed to establish whether this pattern results from a lack of phylogenetic resolution or from a paraphyletic classification system, many of our results are congruent with those obtained from 18S ribosomal subunit gene sequences and complete mitochondrial genomes. These data provide further support for a revision of the traditional classification of sponges.

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