More Like this

1. Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies

   https://doi.org/10.1242/jeb.245784  

   Ijspeert, Auke J.; Daley, Monica A. (August 2023, Journal of Experimental Biology)

   ABSTRACT Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) – systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers – and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers. 

   more » « less
2. Understanding how variable thermal environments affect the molecular mechanisms underlying temperature-sensitive phenotypes: lessons from sex determination

   https://doi.org/10.1242/jeb.242373  

   Paitz, Ryan T.; Breitenbach, Anthony T.; Marroquín-Flores, Rosario A.; Bowden, Rachel M. (June 2022, Journal of Experimental Biology)

   ABSTRACT The thermal environment that organisms experience can affect many aspects of their phenotype. As global temperatures become more unpredictable, it is imperative that we understand the molecular mechanisms by which organisms respond to variable, and often transient, thermal environments. Beyond deciphering the mechanisms through which organisms respond to temperature, we must also appreciate the underlying variation in temperature-dependent processes, as this variation is essential for understanding the potential to adapt to changing climates. In this Commentary, we use temperature-dependent sex determination as an example to explore the mechanistic processes underlying the development of temperature-sensitive phenotypes. We synthesize the current literature on how variable thermal conditions affect these processes and address factors that may limit or allow organisms to respond to variable environments. From these examples, we posit a framework for how the field might move forward in a more systematic way to address three key questions: (1) which genes directly respond to temperature-sensitive changes in protein function and which genes are downstream, indirect responders?; (2) how long does it take different proteins and genes to respond to temperature?; and (3) are the experimental temperature manipulations relevant to the climate the organism experiences or to predicted climate change scenarios? This approach combines mechanistic questions (questions 1 and 2) with ecologically relevant conditions (question 3), allowing us to explore how organisms respond to transient thermal environments and, thus, cope with climate change. 

   more » « less
3. Interface-Dominated Plasticity and Kink Bands in Metallic Nanolaminates

   https://doi.org/10.3390/cryst13050828  

   Arora, Abhishek; Arora, Rajat; Acharya, Amit (May 2023, Crystals)

   The theoretical and computational framework of finite deformation mesoscale field dislocation mechanics (MFDM) is used to understand the salient aspects of kink-band formation in Cu-Nb nano-metallic laminates (NMLs). A conceptually minimal, plane-strain idealization of the three-dimensional geometry, including crystalline orientation, of additively manufactured NML is used to model NMLs. Importantly, the natural jump/interface condition of MFDM imposing continuity of (certain components) of plastic strain rates across interfaces allows theory-driven ‘communication’ of plastic flow across the laminate boundaries in our finite element implementation. Kink bands under layer parallel compression of NMLs in accord with experimental observations arise in our numerical simulations. The possible mechanisms for the formation and orientation of kink bands are discussed, within the scope of our idealized framework. We also report results corresponding to various parametric studies that provide preliminary insights and clear questions for future work on understanding the intricate underlying mechanisms for the formation of kink bands. 

   more » « less
4. SLEAP: A deep learning system for multi-animal pose tracking

   https://doi.org/10.1038/s41592-022-01426-1  

   Pereira, Talmo D.; Tabris, Nathaniel; Matsliah, Arie; Turner, David M.; Li, Junyu; Ravindranath, Shruthi; Papadoyannis, Eleni S.; Normand, Edna; Deutsch, David S.; Wang, Z. Yan; et al (April 2022, Nature Methods)

   Abstract The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal. 

   more » « less
5. BoomBox: An Automated Behavioural Response (ABR) camera trap module for wildlife playback experiments

   https://doi.org/10.1111/2041-210X.13789  

   Palmer, Meredith S.; Wang, Chris; Plucinski, Jacinta; Pringle, Robert M. (January 2022, Methods in Ecology and Evolution)

   Abstract Camera traps (CTs) are a valuable tool in ecological research, amassing large quantities of information on the behaviour of diverse wildlife communities. CTs are predominantly used as passive data loggers to gather observational data for correlational analyses. Integrating CTs into experimental studies, however, can enable rigorous testing of key hypotheses in animal behaviour and conservation biology that are otherwise difficult or impossible to evaluate.We developed the 'BoomBox', an open‐source Arduino‐compatible board that attaches to commercially available CTs to form an Automated Behavioural Response (ABR) system. The modular unit connects directly to the CT’s passive infrared (PIR) motion sensor, playing audio files over external speakers when the sensor is triggered. This creates a remote playback system that captures animal responses to specific cues, combining the benefits of camera trapping (e.g. continuous monitoring in remote locations, lack of human observers, large data volume) with the power of experimental manipulations (e.g. controlled perturbations for strong mechanistic inference).Our system builds on previous ABR designs to provide a cheap (~100USD) and customizable field tool. We provide a practical guide detailing how to build and operate the BoomBox ABR system with suggestions for potential experimental designs that address a variety of questions in wildlife ecology. As proof‐of‐concept, we successfully field tested the BoomBox in two distinct field settings to study species interactions (predator–prey and predator–predator) and wildlife responses to conservation interventions.This new tool allows researchers to conduct a unique suite of manipulative experiments on free‐living species in complex environments, enhancing the ability to identify mechanistic drivers of species' behaviours and interactions in natural systems. 

   more » « less