LGBTQIA+ Individuals' Experiences with Intimate Partner Violence and Perceptions of Victim Services.

This study explored lived experiences of LGBTQIA+ survivors of intimate partner violence (IPV). Seven participants completed a one-on-one, in-depth interview to share their experiences of IPV and any internal or external factors that influenced whether they sought support services or reported victimization. Thematic analysis revealed four overarching themes: (a) health complications, (b) coping mechanisms, (c) barriers to seeking professional help, and (d) suggestions for professionals. Results contribute insight into internal and external barriers LGBTQIA+ IPV survivors face when accessing support services and outline practical approaches for professionals providing support to LGBTQIA+ IPV survivors.

Deep learning for robust and flexible tracking in behavioral studies for C. elegans.

Robust and accurate behavioral tracking is essential for ethological studies. Common methods for tracking and extracting behavior rely on user adjusted heuristics that can significantly vary across different individuals, environments, and experimental conditions. As a result, they are difficult to implement in large-scale behavioral studies with complex, heterogenous environmental conditions. Recently developed deep-learning methods for object recognition such as Faster R-CNN have advantages in their speed, accuracy, and robustness. Here, we show that Faster R-CNN can be employed for identification and detection of Caenorhabditis elegans in a variety of life stages in complex environments. We applied the algorithm to track animal speeds during development, fecundity rates and spatial distribution in reproductive adults, and behavioral decline in aging populations. By doing so, we demonstrate the flexibility, speed, and scalability of Faster R-CNN across a variety of experimental conditions, illustrating its generalized use for future large-scale behavioral studies.

Topological Data Analysis of C. elegans Locomotion and Behavior.

We apply topological data analysis to the behavior of C. elegans, a widely studied model organism in biology. In particular, we use topology to produce a quantitative summary of complex behavior which may be applied to high-throughput data. Our methods allow us to distinguish and classify videos from various environmental conditions and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors.

Fast, versatile and quantitative annotation of complex images.

We report a generic smartphone app for quantitative annotation of complex images. The app is simple enough to be used by children, and annotation tasks are distributed across app users, contributing to efficient annotation. We demonstrate its flexibility and speed by annotating >30,000 images, including features of rice root growth and structure, stem cell aggregate morphology, and complex worm (Caenorhabditis elegans) postures, for which we show that the speed of annotation is >130-fold faster than state-of-the-art techniques with similar accuracy.

Optics-Integrated Microfluidic Platforms for Biomolecular Analyses.

Compared with conventional optical methods, optics implemented on microfluidic chips provide small, and often much cheaper ways to interrogate biological systems from the level of single molecules up to small model organisms. The optical probing of single molecules has been used to investigate the mechanical properties of individual biological molecules; however, multiplexing of these measurements through microfluidics and nanofluidics confers many analytical advantages. Optics-integrated microfluidic systems can significantly simplify sample processing and allow a more user-friendly experience; alignments of on-chip optical components are predetermined during fabrication and many purely optical techniques are passively controlled. Furthermore, sample loss from complicated preparation and fluid transfer steps can be virtually eliminated, a particularly important attribute for biological molecules at very low concentrations. Excellent fluid handling and high surface area/volume ratios also contribute to faster detection times for low abundance molecules in small sample volumes. Although integration of optical systems with classical microfluidic analysis techniques has been limited, microfluidics offers a ready platform for interrogation of biophysical properties. By exploiting the ease with which fluids and particles can be precisely and dynamically controlled in microfluidic devices, optical sensors capable of unique imaging modes, single molecule manipulation, and detection of minute changes in concentration of an analyte are possible.