Differences in working memory function are associated with motor imagery-induced changes in spinal motor nerve excitability and subsequent motor skill changes.

Verification of the effectiveness of motor imagery (MI) has mainly focused on the method of implementing MI, and few studies have assessed individual factors. This study examined the individual differences in MI effects from the viewpoint of the multiple components of working memory. Forty-six healthy subjects (mean age 20.8 years) performed the Stroop Test (central executive within working memory) and reverse chanting (phonological loop within working memory). Then, F-waves were measured at rest for 30 s, the Purdue Pegboard was performed with the non-dominant hand to evaluate finger dexterity (Peg score) before MI, F-waves were measured during 30 s of kinesthetic MI, and the Peg score was evaluated after MI. For statistical analysis, the amplitude F/M ratio and Peg score were used as dependent variables, and the subjects were divided into Good and Poor groups according to cognitive function. The results showed an interaction for the amplitude F/M ratio and Peg score when grouped by reverse inverse chanting. In the subsequent simple main effect analysis, the Peg score was significantly improved after MI in both groups. The amplitude F/M ratio was significantly increased during MI compared to the resting state only in the Poor phonological loop group. Conversely, there was no interaction when the groups were divided by Stroop interference. No relationship was found between individual differences in central executive and changes in hand finger dexterity and spinal motor nerve excitability induced by MI. However, there may be a relationship between individual differences in phonological loops and changes in MI-induced finger dexterity and spinal motor nerve excitability.

Lower extremity pain and/or numbness after laparoscopic surgery and robot-assisted surgery in the lithotomy position combined with the Trendelenburg position.

PURPOSE: The purpose of this study was to investigate the incidence and risk factors of lower extremity pain and/or numbness after laparoscopic colorectal surgery and robot-assisted laparoscopic radical prostatectomy in the lithotomy position combined with the Trendelenburg position. The relationship between creatine kinase (CK) levels and lower extremity pain and/or numbness was also investigated. METHODS: We retrospectively reviewed adult patients who underwent laparoscopic colorectal surgery and robot-assisted laparoscopic radical prostatectomy in the lithotomy position combined with the Trendelenburg position between May 2015 and April 2020. Logistic regression analysis was used to identify risk factors of lower extremity pain and/or numbness. Preoperative and postoperative CK levels were compared in patients with and those without lower extremity pain and/or numbness. RESULTS: Among 940 patients, 1.9% experienced lower extremity pain and/or numbness postoperatively. The incidences of lower extremity pain and/or numbness after laparoscopic colorectal surgery and after robot-assisted laparoscopic radical prostatectomy were 1.7% and 2.1%, respectively. Multivariate logistic regression analysis revealed that only duration of surgery > 4 h (odds ratio = 3.144, 95% CI: 1.102-8.969, p = 0.032) was a significant predictor of lower extremity pain and/or numbness. Postoperative median CK level in patients with lower extremity pain and/or numbness was significantly higher than that in patients without lower extremity pain and/or numbness. CONCLUSION: The incidence of lower extremity pain and/or numbness after laparoscopic colorectal surgery was comparable to that after robot-assisted laparoscopic radical prostatectomy. Prolonged duration of surgery contributed to lower extremity pain and/or numbness. Significantly elevated CK levels in patients with lower extremity pain and/or numbness suggest the involvement of muscle injury in these symptoms.

Assessment of the Effect of Meaningful Occupations on Motivation by Orbitofrontal Cortex Activation Using Near-Infrared Spectroscopy.

BACKGROUND: Meaningful occupations are those perceived as important by an individual. Research on meaningful occupations relies on subjective data and requires qualitative inquiries. Therefore, assessing the meaning of occupations using objective methods is challenging. As orbitofrontal cortex (OFC) activation is part of the reward system network involved in motivation, it could aid in assessing the meaning of occupations. OBJECTIVE: We aimed to investigate the effect of meaningful occupations on motivation by measuring OFC activation using near-infrared spectroscopy (NIRS). METHODS: Eight young and healthy participants were enrolled in this study. The occupation was set as "cooking," and its importance was confirmed using the Canadian Occupational Performance Measure (COPM). NIRS was performed using an OEG-16 (Spectareteh Inc.). The target task involved watching a cooking video, while the control task consisted of looking at a "+" sign on a blank sheet of paper. OFC activation was measured based on changes in oxygenated hemoglobin (oxy-Hb) concentration using a block design. Participants with COPM scores of eight or more were classified into the "meaningful occupation performance" group, while those with scores of seven or lower were classified into the "meaningful occupation non-performance" group. Changes in oxy-Hb concentrations between the two groups were compared using the Mann-Whitney U test. RESULTS: Four participants were assigned to the meaningful occupation group (frequency of implementation: various times per week for all participants), and four participants were assigned to the meaningful occupation non-performance group (frequency of implementation: various times per week for one participant, various times per month for one participant, and various times per year for two participants). Statistical analysis revealed significant differences in the changes in the oxy-Hb concentration in the left and bilateral OFC. CONCLUSION: This study suggests that it is important to focus on meaningful occupations that individuals consider important in order to activate the reward system and increase motivation.

Repeated complete atrioventricular block during remifentanil administration in a pediatric patient with brain tumor and acute hydrocephalus: a case report.

BACKGROUND: Remifentanil, an ultra-short-acting µ-opioid receptor agonist, is commonly used for anesthetic management due to excellent adjustability. Remifentanil is known to cause sinus bradycardia, however, because it has a direct negative chronotropic effect on the cardiac conduction system and there is an indirect negative chronotropic effect via the parasympathetic nervous system. CASE PRESENTATION: An 8-year-old Japanese boy was diagnosed with acute hydrocephalus due to a brain tumor in the fourth ventricle and underwent emergency surgery. Imaging examination showed brainstem compression. Endoscopic third ventriculostomy and ventriculoperitoneal shunt surgery were scheduled. Remifentanil was started during induction of general anesthesia, but electrocardiogram showed sinus bradycardia, then Wenckebach-type atrioventricular block, and then complete atrioventricular block. Remifentanil was immediately discontinued, and we administered atropine sulfate. Complete atrioventricular block was restored to sinus rhythm. When remifentanil was restarted, however, the electrocardiogram again showed sinus bradycardia, Wenckebach-type atrioventricular block, and then complete atrioventricular block. Remifentanil was again immediately discontinued, we administered adrenaline, and then complete atrioventricular block was restored to sinus rhythm. Fentanyl was used instead of remifentanil with continuous infusion of dopamine. There has since been no further occurrence of complete atrioventricular block. CONCLUSIONS: This is the first known case of complete atrioventricular block in a pediatric patient with increased intracranial pressure seemingly caused by administration of remifentanil.

Open-Vocabulary Predictive World Models from Sensor Observations.

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots.

Association between muscle quality and nutritional status among community-dwelling older adults: A cross-sectional study.

Background: Decline in muscle quality is associated with poorer health in community-dwelling older adults. Although there is evidence that malnutrition is associated with the decline of muscle quality in older adults, few analyses have considered important factors related to muscle quality. Aim: The purpose of this study was to determine the relationship between muscle quality and nutritional status in community-dwelling older adults, taking into account muscle mass, muscle strength, and amount of physical activity. Methods: This was a cross-sectional study. Data were obtained from a health checkup program in Japan. Participants were older adults aged ≥65 years living in the community who participated in the health checkup program. The data collected were echo intensity, scores on the Global Leadership Initiative on Malnutrition criteria, skeletal muscle mass, grip strength, physical activity level, and demographic data. Multiple regression analysis was used to examine the association between muscle quality and nutritional status. Results: Data were analyzed for 50 participants (mean age 77.62 years, standard deviation 6.16 years, 78.0% women). Multiple regression analysis showed that nutritional status affected muscle quality even when various factors were taken into account (B = 6.95, ß = 0.31, p = 0.04, 95% confidence interval: 0.01-0.62). Conclusion: The observed association suggests that providing nutritional support for older adults living in the community may be a useful strategy to maintain muscle quality.

[A longitudinal study of the association between physical activity and apathy among community-dwelling older adults].

AIM: This study clarified the association between the amount of physical activity and apathy after one year among community-dwelling older adults. METHODS: Two hundred community-dwelling older adults (mean age: 74.3±5.0 years old, female: 52.5%) who participated in the 2018 and 2019 "Kasama Longevity Health Examination" were included. Apathy was assessed using the Apathy Scale (0-42 points; the higher the score, the lower the motivation), physical activity by Physical Activity Scale for the Elderly (PASE), and Geriatric Depression Scale-15 (GDS). The 2018 PASE data were grouped into tertiles. A multiple regression analysis was performed with the 2019 Apathy Scale as the dependent variable and the 2018 PASE as the independent variable, and the sex, age, years of education, economic situation, body mass index, chronic illness, smoking history, alcohol drinking habits, physical function, cognitive function, GDS, and the 2018 Apathy Scale as adjustment variables. The PASE subcategories (leisure-time, household, and work-related activities) were examined using a similar method. RESULTS: The mean Apathy Scale in 2019 was 14.0±6.2 for the low physical activity group, 12.8±6.0 for the medium physical activity group, and 10.1±5.9 for the high physical activity group. The high physical activity group showed a significant negative association with the Apathy Scale (B=-1.56, 95% confidence interval=-2.91 to -0.21, p=0.023). No association was found for any activity of the PASE sub-items. CONCLUSIONS: A high level of physical activity may protect against apathy among community-dwelling older adults.

Collaborative hunting in artificial agents with deep reinforcement learning.

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

From wolves to ants, many animals are known to be able to hunt as a team. This strategy may yield several advantages: going after bigger preys together, for example, can often result in individuals spending less energy and accessing larger food portions than when hunting alone. However, it remains unclear whether this behavior relies on complex cognitive processes, such as the ability for an animal to represent and anticipate the actions of its teammates. It is often thought that 'collaborative hunting' may require such skills, as this form of group hunting involves animals taking on distinct, tightly coordinated roles ­ as opposed to simply engaging in the same actions simultaneously. To better understand whether high-level cognitive skills are required for collaborative hunting, Tsutsui et al. used a type of artificial intelligence known as deep reinforcement learning. This allowed them to develop a computational model in which a small number of 'agents' had the opportunity to 'learn' whether and how to work together to catch a 'prey' under various conditions. To do so, the agents were only equipped with the ability to link distinct stimuli together, such as an event and a reward; this is similar to associative learning, a cognitive process which is widespread amongst animal species. The model showed that the challenge of capturing the prey when hunting alone, and the reward of sharing food after a successful hunt drove the agents to learn how to work together, with previous experiences shaping decisions made during subsequent hunts. Importantly, the predators started to exhibit the ability to take on distinct, complementary roles reminiscent of those observed during collaborative hunting, such as one agent chasing the prey while another ambushes it. Overall, the work by Tsutsui et al. challenges the traditional view that only organisms equipped with high-level cognitive processes can show refined collaborative approaches to hunting, opening the possibility that these behaviors may be more widespread than originally thought ­ including between animals of different species.

Effect of digital game intervention on cognitive functions in older adults: a multiple baseline single case experimental design study.

BACKGROUND: Residents in nursing homes are prone to cognitive decline affecting memory, visuospatial cognition, and executive functions. Cognitive decline can lead to dementia, necessitating prioritized intervention. METHODS: The current study aimed to investigate whether an intervention using a digital game was effective for preserving and improving the cognitive function of residents in nursing homes. An intervention study was conducted using a single-case AB design with multiple baselines. The participants in the study were five older adults aged 65 and over who do not play digital games regularly. The study ran for 15 weeks, including a baseline (phase A) and an intervention phase (phase B). Phase A had five baselines (5 to 9 weeks) with random participant assignment. In phase B, participants engaged in a digital game (Space Invaders) individually. Cognitive function was assessed as the outcome, measured using the Brain Assessment (performed on a tablet through the Internet) at 16 measurement points. Four of five participants (two female and two male) were included in the analysis, using visual inspection and Bayesian statistics with multi-level modeling. RESULTS: Visual inspection of the graphs revealed cognitive function score improvements after the intervention for most layers in terms of memory of numbers, memory of words, mental rotation test (visuospatial ability), and total scores in the Brain Assessment. These effects were also significant in the analysis by multi-level modeling. CONCLUSIONS: The results suggest that the use of digital games may be effective for preserving and improving cognitive function among residents of nursing home. TRIAL REGISTRATION: This study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000048677; public title: Effect of a Digital Game Intervention for Cognitive Functions in Older People; registration date: August 30, 2022).

Associations Between Cognitive Function and Muscle Quality Among Community-Dwelling Older Adults: A Cross-Sectional Study.

OBJECTIVE: To investigate whether muscle quality is related to cognitive function in older adults living in the community. METHODS: The participants were 40 community-dwelling older adults without a diagnosis of dementia (mean age, 78.85 ± 6.40 years; 31 women). The dependent variable was the score on the mild cognitive impairment (MCI) index of the Japanese version of the Neurobehavioral Cognitive Status Examination Five (range: 0-6; higher scores indicate MCI or possible dementia). RESULTS: Multiple regression with Bayesian statistics was performed for analysis of muscle quality, as indicated by echo intensity (EI), with skeletal muscle mass index, gait speed, physical activity level, age, and sex as covariates. EI was significantly associated with the MCI index (expected a posterior = 0.04 [95% Bayesian confidence interval: 0.01, 0.07], t = 2.47, ß = 0.41, p = 0.019). CONCLUSIONS: The results suggest that EI may be related to the cognitive function of older adults living in the community. Future studies should examine whether focusing on EI can help prevent cognitive decline among older adults..

Development and validation of the effective CNR analysis method for evaluating the contrast resolution of CT images.

Contrast resolution is an important index for evaluating the signal detectability of computed tomographic (CT) images. Recently, various noise reduction algorithms, such as iterative reconstruction (IR) and deep learning reconstruction (DLR), have been proposed to reduce the image noise in CT images. However, these algorithms cause changes in the image noise texture and blurred image signals in CT images. Furthermore, the contrast-to-noise ratio (CNR) cannot be accurately evaluated in CT images reconstructed using noise reduction methods. Therefore, in this study, we devised a new method, namely, "effective CNR analysis," for evaluating the contrast resolution of CT images. We verified whether the proposed algorithm could evaluate the effective contrast resolution based on the signal detectability of CT images. The findings showed that the effective CNR values obtained using the proposed method correlated well with the subjective visual impressions of CT images. To investigate whether signal detectability was appropriately evaluated using effective CNR analysis, the conventional CNR analysis method was compared with the proposed method. The CNRs of the IR and DLR images calculated using conventional CNR analysis were 13.2 and 10.7, respectively. By contrast, those calculated using the effective CNR analysis were estimated to be 0.7 and 1.1, respectively. Considering that the signal visibility of DLR images was superior to that of IR images, our proposed effective CNR analysis was shown to be appropriate for evaluating the contrast resolution of CT images.

Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios.

Evaluation of intervention in a multiagent system, for example, when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields. Estimating the individual treatment effect (ITE) using counterfactual long-term prediction is practical to evaluate such interventions. However, most of the conventional frameworks did not consider the time-varying complex structure of multiagent relationships and covariate counterfactual prediction. This may lead to erroneous assessments of ITE and difficulty in interpretation. Here, we propose an interpretable, counterfactual recurrent network in multiagent systems to estimate the effect of the intervention. Our model leverages graph variational recurrent neural networks (GVRNNs) and theory-based computation with domain knowledge for the ITE estimation framework based on long-term prediction of multiagent covariates and outcomes, which can confirm the circumstances under which the intervention is effective. On simulated models of an automated vehicle and biological agents with time-varying confounders, we show that our methods achieved lower estimation errors in counterfactual covariates and the most effective treatment timing than the baselines. Furthermore, using real basketball data, our methods performed realistic counterfactual predictions and evaluated the counterfactual passes in shot scenarios.

Longitudinal association between oral function and underweight onset among community-dwelling older adults: Role of regular self-weighing.

AIMS: Regular self-weighing is effective in weight management and may help to mitigate the risk of underweight among older adults. We examined which factors of oral function are associated with a risk for underweight among community-dwelling older adults and whether regular self-weighing can mitigate that risk. METHODS: This was a cohort study. Self-administered questionnaires were mailed to 7665 older adults in March 2019 (response rate 74.3%) and 7591 in March 2020 (response rate 74.7%). Among those who responded to both surveys, 3594 older adults who were not underweight in March 2019 were included (follow-up rate: 59.2%). We examined the association between dysphagia, poor masticatory function, and dry mouth and the development of underweight, and whether regular self-weighing reduced the risk for underweight. RESULTS: The average age ± standard deviation of participants was 71.1 ± 3.4 years. There was a significant association between swallowing difficulty and the occurrence of underweight (odds ratio = 1.65, p = .012). However, this relationship did not differ significantly with regular self-weighing (p = .477). CONCLUSIONS: These study findings suggest that to prevent underweight among community-dwelling older adults, it is important to focus on those with poor swallowing function.

Relationship between Phase Angle and Physical Activity Intensity among Community-Dwelling Older Adults in Japan: A Cross-Sectional Study.

This cross-sectional study aimed to determine the association between phase angle (PhA) and physical activity intensity in community-dwelling older Japanese adults. The intensity and time of physical activity for predicting high PhA were also examined. This study involved 67 community-dwelling older adults (mean age: 78.3 ± 5.5; female: 83.6%). We measured the physical activity and body composition of the participants. Physical activity was measured using a triaxial accelerometer, and light-intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) hours per day were calculated from the results. Body composition was measured using Inbody S10, and the PhA was calculated from the measurements of the right side of the body. Bayesian statistical modeling revealed an association between PhA and MVPA (ß = 0.256; p = 0.022; 95% Bayesian confidence interval [CI] = 0.001, 0.012), but not LPA (ß = -0.113; p = 0.341; 95% Bayesian CI = -0.002, 0.001), even after adjustment for confounders. The cutoff value of MVPA predicting high PhA, calculated by the receiver operator characteristic curve, was 19.7 min/d (sensitivity = 0.906; specificity = 0.429). These results can be used to develop strategies to increase PhA in older adults and suggest that MVPA is important in this population.

Appetite and family and friends network among community-dwelling older adults: A cross-sectional study.

OBJECTIVES: The aim of this study was to investigate the stronger correlate of appetite-family or friend networks-in community-dwelling older adults, given that undernutrition can impair physical function, increase mortality, and be influenced by social networks. METHODS: A cross-sectional study was conducted in Kasugai City, Aichi Prefecture, and Nara City, Nara Prefecture, Japan, between August 2019 and March 2023. The analysis included 119 participants (women: 79%, mean age: 76.5 ± 5.6 y). A multiple regression analysis was performed, using the scores from the Japanese version of the Simplified Nutritional Appetite Questionnaire (SNAQ-J) as the dependent variable and family network and friend network as the independent variables. The analysis included social participation, living alone status, sex, age, body mass index, skeletal muscle mass index, grip strength, walking speed, and the Japanese version of the 15 Geriatric Depression Scale score as covariates to examine their relationship with appetite. RESULTS: The mean value of the SNAQ-J score of the participants was 15.4 ± 1.2. Seven participants (6%) had a loss of appetite. Family network was significantly associated with appetite (B = 0.121, ß = 0.266, P <0.05; 95% condidence interval [CI], 0.030-0.212). In the single regression analysis, the friend network was significantly associated with the total score of the SNAQ-J (B = 0.115, P <0.001; 95% CI, 0.052-0.177); however, this association was not observed in the multiple regression analysis (B = 0.002, ß = 0.006, P = 0.954; 95% CI, -0.074-0.078). CONCLUSION: Appetite was associated with family networks. Among social networks, focusing on family networks may help prevent the loss of appetite in older adults in Japan.

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling.

Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors. We formulate this as a decentralized multi-agent imitation-learning problem, leveraging binary partial observation and decentralized policy models based on hierarchical variational recurrent neural networks with physical and biomechanical penalties. Using real-world basketball and soccer datasets, we show the effectiveness of our method in terms of the constraint violations, long-term trajectory prediction, and partial observation. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.

Chemotherapy for pain: reversing inflammatory and neuropathic pain with the anticancer agent mithramycin A.

ABSTRACT: The persistence of inflammatory and neuropathic pain is poorly understood. We investigated a novel therapeutic paradigm by targeting gene networks that sustain or reverse persistent pain states. Our prior observations found that Sp1-like transcription factors drive the expression of TRPV1, a pain receptor, that is blocked in vitro by mithramycin A (MTM), an inhibitor of Sp1-like factors. Here, we investigate the ability of MTM to reverse in vivo models of inflammatory and chemotherapy-induced peripheral neuropathy (CIPN) pain and explore MTM's underlying mechanisms. Mithramycin reversed inflammatory heat hyperalgesia induced by complete Freund adjuvant and cisplatin-induced heat and mechanical hypersensitivity. In addition, MTM reversed both short-term and long-term (1 month) oxaliplatin-induced mechanical and cold hypersensitivity, without the rescue of intraepidermal nerve fiber loss. Mithramycin reversed oxaliplatin-induced cold hypersensitivity and oxaliplatin-induced TRPM8 overexpression in dorsal root ganglion (DRG). Evidence across multiple transcriptomic profiling approaches suggest that MTM reverses inflammatory and neuropathic pain through broad transcriptional and alternative splicing regulatory actions. Mithramycin-dependent changes in gene expression following oxaliplatin treatment were largely opposite to and rarely overlapped with changes in gene expression induced by oxaliplatin alone. Notably, RNAseq analysis revealed MTM rescue of oxaliplatin-induced dysregulation of mitochondrial electron transport chain genes that correlated with in vivo reversal of excess reactive oxygen species in DRG neurons. This finding suggests that the mechanism(s) driving persistent pain states such as CIPN are not fixed but are sustained by ongoing modifiable transcription-dependent processes.

Extracting proficiency differences and individual characteristics in golfers' swing using single-video markerless motion analysis.

In this study, we analyzed golfers' swing movement to extract differences in proficiency and individual characteristics using two-dimensional video data from a single camera. We conducted an experiment with 27 golfers who had a wide range of skill levels, using a 7-iron; we acquired video data with a camera on the sagittal plane. For data extraction, we used pose estimation (using HRNet) and object detection (using DeepLabCut) methods to extract human-joint and club-head data. We examined the relationship between proficiency and individual characteristics vis-à-vis forward tilt angle and club trajectory. The results showed that the stability and reproducibility of the forward tilt angle are characteristics of proficiency. Highly skilled golfers showed low variability and high reproducibility between trials in forward tilt angle. However, we found that club trajectory may not be a characteristic of proficiency but rather an individual characteristic. Club trajectory was divided roughly into clockwise rotation and counterclockwise rotation. Thus, the analysis based on video data from a single markerless camera enabled the extraction of the differences in proficiency and individual characteristics of golf swing. This suggests the usefulness of our system for simply evaluating golf swings and applying it to motor learning and coaching situations.

Can golfers choose low-risk routes in steep putting based on visual feedback of ball trajectory?

This study aims to clarify why the aiming method in golf putting in risky situations differs based on skill level. This study set up a difficult challenge (steep slopes and fast ball rolling greens), which required even professional golfers to change their aim. A total of 12 tour professionals and 12 intermediate amateurs were asked to perform a steep-slope task with no visual feedback of outcomes (no FB) followed by a task with visual feedback (with FB). The aim of the task was for the ball to enter the hole in one shot. Additionally, the participants were told that if the ball did not enter the hole, it was to at least stop as close to it as possible. The participant's aim (as an angle) and the kinematics of the putter head and ball were measured. The results indicated that professionals' highest ball trajectory points were significantly higher than that of amateurs, especially with FB. Additionally, professionals had higher ball-launch angles (the direction of the ball when the line connecting the ball and the center of the hole is 0 degrees) and lower peak putter head velocities than amateurs. Furthermore, the aim angle, indicating the golfer's decision-making, was higher for professionals under both conditions. However, even with FB, the amateurs' aim angles were lower and the difference between trials was smaller than that of professionals. Therefore, this study confirmed that the professionals made more drastic changes to their aim to find low-risk routes than the amateurs and that the amateurs' ability to adjust their aim was lower than that of professionals. The results suggest that the reason for the amateurs' inability to find low-risk routes lies in their decision-making. The professionals found better routes; however, there were individual differences in their routes.

Collisional-energy-cascade model for nonthermal velocity distributions of neutral atoms in plasmas.

Nonthermal velocity distributions with much greater tails than a Maxwellian have been observed for radical atoms in plasmas for a long time. Historically, such velocity distributions have been modeled by a two-temperature Maxwell distribution. In this paper, I propose a model based on collisional energy cascade, which has been studied in the field of granular materials. In the collisional energy cascade, a particle ensemble undergoes energy input at the high-energy region, entropy production by elastic collisions among particles, and energy dissipation. For radical atoms, energy input may be caused by the Franck-Condon energy of molecular dissociation or charge-exchange collision with hot ions, and the input energy is eventually dissipated by collisions with the walls. I show that the steady-state velocity distribution in the collisional energy cascade is approximated by the generalized Mittag-Leffler distribution, which is a one-parameter extension of the Maxwell distribution. This parameter indicates the degree of the nonthermality and is related to the relative importance of energy dissipation over entropy production. This model is compared with a direct molecular dynamics simulation for simplified gaseous systems with energy input, as well as some experimentally observed velocity distributions of light radicals in plasmas.