Machine Learning-Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation.

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 R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 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.

Muscle Path Wrapping on Arbitrary Surfaces.

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.

Quantal biomechanical effects in speech postures of the lips.

The unique biomechanical and functional constraints on human speech make it a promising area for research investigating modular control of movement. The present article illustrates how a modular control approach to speech can provide insights relevant to understanding both motor control and observed variation across languages. We specifically explore the robust typological finding that languages produce different degrees of labial constriction using distinct muscle groupings and concomitantly distinct lip postures. Research has suggested that these lip postures exploit biomechanical regions of nonlinearity between neural activation and movement, also known as quantal regions, to allow movement goals to be realized despite variable activation signals. We present two sets of computer simulations showing that these labial postures can be generated under the assumption of modular control and that the corresponding modules are biomechanically robust: first to variation in the activation levels of participating muscles, and second to interference from surrounding muscles. These results provide support for the hypothesis that biomechanical robustness is an important factor in selecting the muscle groupings used for speech movements and provide insight into the neurological control of speech movements and how biomechanical and functional constraints govern the emergence of speech motor modules. We anticipate that future experimental work guided by biomechanical simulation results will provide new insights into the neural organization of speech movements.NEW & NOTEWORTHY This article provides additional evidence that speech motor control is organized in a modular fashion and that biomechanics constrain the kinds of motor modules that may emerge. It also suggests that speech can be a fruitful domain for the study of modularity and that a better understanding of speech motor modules will be useful for speech research. Finally, it suggests that biomechanical modeling can serve as a useful complement to experimental work when studying modularity.

Speaking Tongues Are Actively Braced.

Purpose: Bracing of the tongue against opposing vocal-tract surfaces such as the teeth or palate has long been discussed in the context of biomechanical, somatosensory, and aeroacoustic aspects of tongue movement. However, previous studies have tended to describe bracing only in terms of contact (rather than mechanical support), and only in limited phonetic contexts, supporting a widespread view of bracing as an occasional state, peculiar to specific sounds or sound combinations. Method: The present study tests the pervasiveness and effortfulness of tongue bracing in continuous English speech passages using electropalatography and 3-D biomechanical simulations. Results: The tongue remains in continuous contact with the upper molars during speech, with only rare exceptions. Use of the term bracing (rather than merely contact) is supported here by biomechanical simulations showing that lateral bracing is an active posture requiring dedicated muscle activation; further, loss of lateral contact for onset /l/ allophones is found to be consistently accompanied by contact of the tongue blade against the anterior palate. In the rare instances where direct evidence for contact is lacking (only in a minority of low vowel and postvocalic /l/ tokens), additional biomechanical simulations show that lateral contact is maintained against pharyngeal structures dorsal to the teeth. Conclusion: Taken together, these results indicate that tongue bracing is both pervasive and active in running speech and essential in understanding tongue movement control.

Do innate stereotypies serve as a basis for swallowing and learned speech movements?

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.