Search

Knowledge of behaviours, including sex-biased dispersal and kin-association, provides important insight into the costs and benefits of group-living. Such behaviours can be difficult to observe directly, but have quantifiable genetic signatures that allow us to determine their occurrence within and among groups. The mourning cuttlefish Sepia plangon Gray, 1849 is both solitary and found in groups at different locations inside Sydney Harbour. Here we describe the genetic relatedness of individuals from four sampling locations, up to 7 km apart, within Sydney Harbour. We also describe the relatedness of juveniles swimming together in small groups of up to seven individuals and the relatedness of 32 adults found in 16 mating pairs, to evaluate the risk of inbreeding. We estimate relatedness with data from 52 amplified fragment length polymorphism (AFLP) loci. We found no evidence that groups of juveniles consisted of related individuals and therefore suggest that kin do not preferentially associate. Additionally, we found that relatedness between adult pairs did not differ from random. Taken together, these results suggest that the risk of inbreeding in this species is low. No genetic structure was detected among groups of juvenile S. plangon across our sampling locations, implying regular dispersal and gene flow among locations separated by distances of up to 7 km. We therefore suggest that group formation in S. plangon does not incur the risk of inbreeding or tend indirectly to benefit related individuals sharing the same group. [ABSTRACT FROM AUTHOR]

Background: National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects., Objective: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth-the percentage change in total cumulative cases-across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms., Methods: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non-time series machine learning models in predicting confirmed infection growth. We used three validation methods-in-distribution, out-of-distribution, and country-based cross-validation-for the evaluation, each of which was applicable to a different use case of the models., Results: Our results demonstrate high R 2 values between the labels and predictions for the in-distribution method (0.959) and moderate R 2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case., Conclusions: This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections., (©Arnold YS Yeung, Francois Roewer-Despres, Laura Rosella, Frank Rudzicz. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.04.2021.)

Objective: Musculoskeletal models play an important role in surgical planning and clinical assessment of gait and movement. Faster and more accurate simulation of muscle paths in such models can result in better predictions of forces and facilitate real-time clinical applications, such as rehabilitation with real-time feedback. We propose a novel and efficient method for computing wrapping paths across arbitrary surfaces, such as those defined by bone geometry., Methods: A muscle path is modeled as a massless, frictionless elastic strand that uses artificial forces, applied independently of the dynamic simulation, to wrap tightly around intervening obstacles. Contact with arbitrary surfaces is computed quickly using a distance grid, which is interpolated quadratically to provide smoother results., Results: Evaluation of the method demonstrates good accuracy, with mean relative errors of 0.002 or better when compared against simple cases with exact solutions. The method is also fast, with strand update times of around 0.5 msec for a variety of bone shaped obstacles., Conclusion: Our method has been implemented in the open source simulation system ArtiSynth (www.artisynth.org) and helps solve the problem of muscle wrapping around bones and other structures., Significance: Muscle wrapping on arbitrary surfaces opens up new possibilities for patient-specific musculoskeletal models where muscle paths can directly conform to shapes extracted from medical image data.

Keven & Akins suggest that innate stereotypies like TP/R may participate in the acquisition of tongue control. This commentary examines this claim in the context of speech motor learning and biomechanics, proposing that stereotypies could provide a basis for both swallowing and speech movements, and provides biomechanical simulation results to supplement neurological evidence for similarities between the two behaviors.