Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%–94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species

Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers

Marmosets, with their highly social nature and complex vocal communication system, are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalisations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalisations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalisations of up to 18 marmosets. We optimised the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex, and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21% – 94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalisations but also for analysing vocalisations and tracking vocal learning trajectories of other species

Small Molecule Inhibitors of the BfrB-Bfd Interaction Decrease Pseudomonas aeruginosa Fitness and Potentiate Fluoroquinolone Activity

© 2019 American Chemical Society. All rights reserved. The iron storage protein bacterioferritin (BfrB) is central to bacterial iron homeostasis. The mobilization of iron from BfrB, which requires binding by a cognate ferredoxin (Bfd), is essential to the regulation of cytosolic iron levels in P. aeruginosa. This paper describes the structure-guided development of small molecule inhibitors of the BfrB-Bfd protein-protein interaction. The process was initiated by screening a fragment library and followed by obtaining the structure of a fragment hit bound to BfrB. The structural insights were used to develop a series of 4-(benzylamino)- A nd 4-((3-phenylpropyl)amino)-isoindoline-1,3-dione analogs that selectively bind BfrB at the Bfd binding site. Challenging P. aeruginosa cells with the 4-substituted isoindoline analogs revealed a dose-dependent growth phenotype. Further investigation determined that the analogs elicit a pyoverdin hyperproduction phenotype that is consistent with blockade of the BfrB-Bfd interaction and ensuing irreversible accumulation of iron in BfrB, with concomitant depletion of iron in the cytosol. The irreversible accumulation of iron in BfrB prompted by the 4-substituted isoindoline analogs was confirmed by visualization of BfrB-iron in P. aeruginosa cell lysates separated on native PAGE gels and stained for iron with Ferene S. Challenging P. aeruginosa cultures with a combination of commercial fluoroquinolone and our isoindoline analogs results in significantly lower cell survival relative to treatment with either antibiotic or analog alone. Collectively, these findings furnish proof of concept for the usefulness of small molecule probes designed to dysregulate bacterial iron homeostasis by targeting a protein-protein interaction pivotal for iron storage in the bacterial cell

Relevance of ROT control for hot rolled low carbon steels

The need for precision in controlling coiling temperature and cooling profiles on the run out table for hot rolled low carbon steel strips has been investigated. It is claimed in literature that a high degree of automation and control of the run out table is required to control the yield strength variation to within 20 MPa. However, calculations based on models available in literature predict that the Variation in strip temperature encountered in a typical run out table does not affect the mechanical property significantly. This has been shown to be true even from experimental data available in literature. Hence even a coarse control over the run out table is adequate to achieve the desired yield strength. A detailed report concentrating on the influence of coiling temperature on the mechanical properties of HSLA steels is under preparation

Grain-size distribution effects in plastic flow and failure

There has been considerable success over the past five decades in developing a phenomenological and micromechanism-based understanding of plastic flow, creep and superplasticity. Although it is widely known that grain sizes have a distribution in polycrystals and nanocrystals, this factor is usually not included in most analysis of deformation and failure. Experimental observations relating to the influence of grain size distributions are discussed briefly, and an analysis is developed to consider the influence of this factor on the transition from grain boundary strengthening to grain boundary weakening in nanocrystalline materials. The transition from grain boundary strengthening to weakening becomes broader with an increase in the standard deviation of the grain size distribution. It is demonstrated that the observed standard deviations for grain size distributions and nominal errors in grain size measurements can lead to substantially different experimental observations under nominally identical conditions

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%-94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species.ISSN:1742-5689ISSN:1742-566

Monkman-Grant relation - analysis of first order kinetics of creep in AISI 304 stainless steel

The transient creep behaviour in AISI 304 Stainless Steel at 873 and 973 K for different test conditions obeyed first order kinetics and it was shown that the validity of Monkman-Grant relation (MGR) is a consequence of first order kinetics. The analysis is extended to tertiary creep regime and the results are found to obey first order kinetics for all test conditions. A new relationship between steady state and tertiary creep is proposed as epsilon over dot ((sic)(s))'.t(t)/epsilon(t) = constant (where t(t) and epsilon(t) are time spent in tertiary and the limiting tertiary creep strain respectively); alpha' is found to be unity for all test conditions implying the validity of first order kinetics. Further, this relation (for alpha' = 1) is found to be identical to modified Monkman - Grant relation (MMGR; m' = 1 where m' is the exponent for epsilon over dot (s) in MMGR) for the conditions satisfying f = 1/lambda and it is postulated that validity of MMGR is also a consequence of first order kinetics. When first order kinetics is applicable, a generalised relationship between steady state creep rate and rupture life is formulated and is valid for the results at all test conditions

Analysis of first order kinetics for tertiary creep in aisi 304 stainless steel

The results of constant load creep tests on AISI 304 stainless steel at 873 and 973 K for different test conditions were found to obey the first order kinetics for tertiary creep. Distinct master creep curves were obtained at 873 and 973 K with a separate set of constant values of K', epsilon(t), beta' and C-MG. A relationship between steady-state creep rate epsilon over dot(s) , time spent in tertiary creep t(t) and limiting tertiary creep strain epsilon(t) is formulated as epsilon over dot(s) . t(t)/epsilon(t) = constant and is found to be valid for all test conditions. Further, this relation is identical to the modified Monkman-Grant relation (MMGR) for the conditions satisfying f = 1/lambda; it is postulated that the validity of MMGR is a consequence of first order kinetics. Another important outcome of this study is a generalised relation of the form epsilon over dot(s) . t(r) = (epsilon(23).epsilon(f))(1/2); and this relation is compared with the relation proposed by Radhakrishnan, i.e. epsilon over dot(s) . t(r) = (epsilon(23)(2) .epsilon(f))(1/3). It is suggested that cavities act as vacancy sinks and accelerate dislocation climb controlled recovery process leading to tertiary creep. Copyright (C) 1996 Acta Metallurgica Inc