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Home advantage in professional sports is a widely accepted phenomenon despite the lack of any controlled experiments at the professional level. The return to play of professional sports during the COVID-19 pandemic presents a unique opportunity to analyze the hypothesized effect of home advantage in neutral settings. While recent work has examined the effect of COVID-19 restrictions on home advantage in European football, comparatively few studies have examined the effect of restrictions in the North American professional sports leagues. In this work, we infer the effect of and changes in home advantage prior to and during COVID-19 in the professional North American leagues for hockey, basketball, baseball, and American football. We propose a Bayesian multi-level regression model that infers the effect of home advantage while accounting for relative team strengths. We also demonstrate that the Negative Binomial distribution is the most appropriate likelihood to use in modelling North American sports leagues as they are prone to overdispersion in their points scored. Our model gives strong evidence that home advantage was negatively impacted in the NHL and NBA during their strongly restricted COVID-19 playoffs, while the MLB and NFL showed little to no change during their weakly restricted COVID-19 seasons.

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for .

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

Understanding the role of anti-gravity behaviour in fine motor control is crucial to achieving a unified theory of motor control. We compare speech from astronauts before and immediately after microgravity exposure to evaluate the role of anti-gravity posture during fine motor skills. Here we show a generalized lowering of vowel space after space travel, which suggests a generalized postural shift of the articulators. Biomechanical modelling of gravitational effects on the vocal tract supports this analysis—the jaw and tongue are pulled down in 1g, but movement trajectories of the tongue are otherwise unaffected. These results demonstrate the role of anti-gravity posture in fine motor behaviour and provide a basis for the unification of motor control models across domains.

Computational modeling has improved our understanding of how muscle forces are coordinated to generate movement in musculoskeletal systems. Muscular-hydrostat systems, such as the human tongue, involve very different biomechanics than musculoskeletal systems, and modeling efforts to date have been limited by the high computational complexity of representing continuum-mechanics. In this study, we developed a computationally efficient tracking-based algorithm for prediction of muscle activations during dynamic 3D finite element simulations. The formulation uses a local quadratic-programming problem at each simulation time-step to find a set of muscle activations that generated target deformations and movements in finite element muscular-hydrostat models. We applied the technique to a 3D finite element tongue model for protrusive and bending movements. Predicted muscle activations were consistent with experimental recordings of tongue strain and electromyography. Upward tongue bending was achieved by recruitment of the superior longitudinal sheath muscle, which is consistent with muscular-hydrostat theory. Lateral tongue bending, however, required recruitment of contralateral transverse and vertical muscles in addition to the ipsilateral margins of the superior longitudinal muscle, which is a new proposition for tongue muscle coordination. Our simulation framework provides a new computational tool for systematic analysis of muscle forces in continuum-mechanics models that is complementary to experimental data and shows promise for eliciting a deeper understanding of human tongue function.

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.

Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy. Core Ideas The capacity of deep neural networks does not match performance in genomic prediction. Deep neural networks are not disadvantaged when they cannot access values of the markers. Deep neural networks likely attend primarily to genetic relatedness, not marker effects.

Some of the jaw tracking methods may be limited in terms of their accuracy or clinical applicability. This article introduces the sphere-based registration method to minimize the fiducial (reference landmark) localization error (FLE) in tracking and coregistration of physical and virtual dental models, to enable an effective clinical analysis of the patient’s masticatory functions. In this method, spheres (registration fiducials) are placed on the corresponding polygonal concavities of the physical and virtual dental models based on the geometrical principle that establishes a unique spatial position for a sphere inside an infinite trihedron. The experiments in this study were implemented using an optical system which tracked active tracking markers connected to the upper and lower dental casts. The accuracy of the tracking workflow was confirmed in vitro, based on comparing virtually calculated interocclusal regions of close proximity against the physical interocclusal impressions. The target registration error of the tracking was estimated based on the leave-one-sphere-out method to be the sum of the error of the sensors, i.e., the FLE was negligible. Moreover, based on a user study, the FLE of the proposed method was confirmed to be 5 and 10 times smaller than the FLE of conventional fiducial selections on the physical and virtual models, respectively. The proposed tracking method is non-invasive and appears to be sufficiently accurate. To conclude, the proposed registration and tracking principles can be extended to track any biomedical and non-biomedical geometries that contain polygonal concavities.

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.