Computational identification of variables in neonatal vocalizations predictive for postpubertal social behaviors in a mouse model of 16p11.2 deletion.

Autism spectrum disorder (ASD) is often signaled by atypical cries during infancy. Copy number variants (CNVs) provide genetically identifiable cases of ASD, but how early atypical cries predict a later onset of ASD among CNV carriers is not understood in humans. Genetic mouse models of CNVs have provided a reliable tool to experimentally isolate the impact of CNVs and identify early predictors for later abnormalities in behaviors relevant to ASD. However, many technical issues have confounded the phenotypic characterization of such mouse models, including systematically biased genetic backgrounds and weak or absent behavioral phenotypes. To address these issues, we developed a coisogenic mouse model of human proximal 16p11.2 hemizygous deletion and applied computational approaches to identify hidden variables within neonatal vocalizations that have predictive power for postpubertal dimensions relevant to ASD. After variables of neonatal vocalizations were selected by least absolute shrinkage and selection operator (Lasso), random forest, and Markov model, regression models were constructed to predict postpubertal dimensions relevant to ASD. While the average scores of many standard behavioral assays designed to model dimensions did not differentiate a model of 16p11.2 hemizygous deletion and wild-type littermates, specific call types and call sequences of neonatal vocalizations predicted individual variability of postpubertal reciprocal social interaction and olfactory responses to a social cue in a genotype-specific manner. Deep-phenotyping and computational analyses identified hidden variables within neonatal social communication that are predictive of postpubertal behaviors.

Sensing of Microvascular Vasomotion Using Consumer Camera.

In this paper, we will introduce a method for observing microvascular waves (MVW) by extracting different images from the available images in the video taken with consumer cameras. Microvascular vasomotion is a dynamic phenomenon that can fluctuate over time for a variety of reasons and its sensing is used for variety of purposes. The special device, a side stream dark field camera (SDF camera) was developed in 2015 for the medical purpose to observe blood flow from above the epidermis. However, without using SDF cameras, smart signal processing can be combined with a consumer camera to analyze the global motion of microvascular vasomotion. MVW is a propagation pattern of microvascular vasomotions which reflects biological properties of vascular network. In addition, even without SDF cameras, MVW can be analyzed as a spatial and temporal pattern of microvascular vasomotion using a combination of advanced signal processing with consumer cameras. In this paper, we will demonstrate that such vascular movements and MVW can be observed using a consumer cameras. We also show a classification using it.

How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis.

OBJECTIVE: A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. RESULTS: Ten dimensional statistics were extracted from the heart rate data of more than 420,000 people, and gender identification was performed by various major identification methods. Lasso, linear regression, SVM, random forest, logistic regression, k-means, Elastic Net were compared, for Age < 50 and Age ≥ 50. The best Accuracy was 0.681927 for Random Forest for Age < 50. There are no consistent difference between Age < 50 and Age ≥ 50. Although the discrimination results based on these statistics are statistically significant, it was confirmed that they are not accurate enough to determine the gender of an individual.

Effects of aging on foot pedal responses to visual stimuli.

BACKGROUND: Car accidents due to unexpected forward or backward runaway by older drivers are a serious social problem. Although the cause of these accidents is often attributed to stepping on the accelerator instead of the brake, it is difficult to induce such pedal application errors systematically with usual drive simulators. We developed a simple personal computer system that induces the pedal errors, and investigate the effects of age on the error behaviors. METHODS: The system consisted of a laptop computer and a three-pedal foot mouse. It measured response time, accuracy, and flexibility of pedal operation to visual stimuli. The system displayed two open circles on the computer display, lighting one of the circles in a random order and interval. Subjects were instructed to press the foot pedal with their right foot as quickly as possible when the circle was lit; the ipsilateral pedal to the lit circle in a parallel mode and the contralateral pedal in a cross mode. When the correct pedal was pressed, the light went off immediately, but when the wrong pedal was pressed, the buzzer sounded and the light remained on until the correct pedal was pressed. During a 6-min trial, the mode was switched between parallel and cross every 2 min. During the cross mode, a cross mark appears on the display. The pedal responses were evaluated in 52 subjects divided into young (20-29 years), middle-aged (30-64 years), and older (65-84 years) groups. Additionally, the repeatability of the pedal response characteristic indicators was examined in 14 subjects who performed this test twice. RESULTS: The mean response time was 95 ms (17%) longer in the older group than in the young group. More characteristically, however, the older group showed 2.1 times more frequent pedal errors, fell into long hesitations (response freezing > 3 s) 16 times more often, and took 1.8 times longer period to correct the wrong pedal than the young groups. The indicators of pedal response characteristics showed within-individual repeatability to the extent that can identify the age-dependent changes. CONCLUSIONS: Hesitations and extended error correction time can be associated with increased crash risk due to unexpected runaway by older drivers. The system we have developed may help to uncover and evaluate physiological characteristics related to crash risk in the elderly population.