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Chipper: Open-source software for semi-automated segmentation and analysis of birdsong and other natural soundsRunning head: Chipper: software for analysis of natural sounds Tweeting Abstract: Chipper: open-source software to semi-automate the segmentation and analysis of acoustic signals, particularly birdsong Abstract 1. Audio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high-throughput methods of analysis for researchers. Specifically, there is a need in the community for open-source methods that are tailored to recordings of varying qualities and from multiple species collected in nature.2. We developed Chipper, a Python-based software to semi-automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper's accuracy, repeatability, and reproducibility.3. Chipper provides an effective way to quickly generate reproducible estimates of birdsong features. The cross-platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analyzing field recordings.4. Chipper streamlines the processing of audio recordings with multiple user-friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen-science data and increases the feasibility of large-scale multi-species birdsong studies.

Audio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high‐throughput methods of analysis for researchers. Specifically, there is a need in the community for open‐source methods that are tailored to recordings of varying qualities and from multiple species collected in nature. We developed Chipper, a Python‐based software to semi‐automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining song syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper's accuracy, repeatability and reproducibility. Chipper provides an effective way to quickly generate quantitative, reproducible measures of birdsong. The cross‐platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analysing field recordings. Chipper streamlines the processing of audio recordings with multiple user‐friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen‐science data and increases the feasibility of large‐scale multi‐species birdsong studies.

Have you ever raised your voice because someone could not hear you? Imagine talking to a friend in a peaceful park. Now imagine trying to talk on a busy street or near a highway. The traffic noise makes it difficult to communicate, and you may speak up so your friend can hear you. Other animals have this issue, too. Songbirds can live in various environments, such as forests and grasslands, and they use their songs to communicate with each other. As cities grow and invade their habitats, birds may find it harder to hear one another. To be heard, some birds might change their songs. For example, some birds in cities sing louder, longer, or at a higher pitch than rural birds. Researchers are studying this problem: how does human-made noise affect birdsong? Answering this question is important so we can protect the birds around us and their habitats.

Introduction The goal of this study is to establish a predictive model of cytotoxic therapy that incorporates in vitro drug pharmacokinetics and cell-scale therapy response data, on a cell-line specific basis. We report on a series of time-resolved fluorescence microscopy experiments to characterize the uptake of doxorubicin and its effect on the population dynamics of MDA-MB-231 cells, a model of triple negative breast cancer. Experimental Design We leveraged the intrinsic fluorescence of doxorubicin to measure its uptake by MDA-MB-231 cells. Cells, labeled with a fluorescent nuclear marker, were seeded in microtiter plates and incubated with doxorubicin concentrations ranging from 10 nM to 10 μM for 6, 12, or 24 hours. These plates were imaged daily via bright field and fluorescent microscopy after addition of doxorubicin. Nuclei were segmented and automatically counted to quantify cell population size. Counts were normalized to population size at time of treatment and converted to population doublings. On a separate channel, extracellular, cytoplasmic, and nuclear doxorubicin fluorescence were quantified. A compartment model describing the movement of doxorubicin from the extracellular space into cells was fit to these data. We then constructed a cell treatment response model and fit it, coupled with the compartment model, to the population data using MATLAB. Results MDA-MB-231 cellular response to doxorubicin was tightly linked to both drug concentration and exposure time. Higher doses (> 1 μM) invariably induced rapid cell death. Smaller doses (< 1 μM) induced a concentration-dependent nonlinear response defined by an initial increase in population size that, depending on exposure time, was followed by a protracted decrease in cell number. For example, when treated with 156 nM for 6, 12, and 24 hours, we observed, respectively, an average of 2.6, 2.1, and 0.67 population doublings over the first 150 hours after treatment (p < 0.05 among groups). These populations then either held stable or receded out to 400 hours, when we observed net population doublings of 2.6, 1.7, and 0.037, respectively (p < 0.05). Untreated cells followed a logistic growth pattern, with an average total of 4.4 population doublings. Conclusion These time-resolved treatment protocols replicate clinically observed pharmacokinetics of cytotoxic therapies more closely than the constant concentrations in previous dose-response assays. By explicitly considering both drug and population dynamics, our mathematical model enables exploration, in silico, of treatment protocols intractable experimentally. Predictions from model simulations can then be tested experimentally, hopefully allowing for computationally-optimized and experimentally validated treatment regimens that maximize cytotoxic effects of doxorubicin. Citation Format: Matthew T. McKenna, Stephanie L. Barnes, Abigail Searfoss, Darren R. Tyson, Erin Rericha, Vito Quaranta, Thomas E. Yankeelov. Multiscale treatment response model for triple-negative breast cancer linking drug pharmacokinetics to tumor cell population dynamics. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 776.