A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect.

Parents' psychological stress during the perinatal and neonatal periods continues to increase in an environment of declining birthrates, aging populations, and shrinking family sizes. The increase in child abuse and neglect cases, most likely by inexperienced and insufficiently knowledgeable parents, necessitates education on childcare and intervention techniques in nursing and midwifery training. In particular, attachment formation early in life between mother and infant is crucial. To accurately teach sensitive and comprehensive information on intervention techniques for mother-child attachment formation, realistic videos, and educational materials are necessary. Although pseudoeducational materials are available, they might be limited in explaining complex realism, particularly to support breastfeeding that involves both parents and child and that encourages interaction between the two. In a previous study in a common marmoset (Callithrix jacchus) model, we experimentally controlled infant feeding and nurturing through 24 h of constant sensing and collected 1 month of quantitative data on psychological indices that possibly translated to psychological development. Age-dependent dynamic visualization of these data by multivariate analyses inferred causal relationships between early parental feeding and psychobiological rhythm formation. In the same primate model, we identified a spontaneous case of breastfeeding failure in which the father inhibited his neonatal infant's feeding and the mother appeared to abandon nurturing, leading to clinically significant weight loss in the infant. Thus, we explored intervention techniques to promote mother-infant interaction. The mother was trained to allow the infant to spontaneously explore her breast. Initially, the mother refused to display the feeding pose potentially due to pain associated with breast engorgement. Massage was used to soften the breast and feeding was reintroduced. We hypothesize that activation of instinctive attachment formation mechanisms by encouraging spontaneity in each parent and child is the key to successful feeding intervention.

Model-free idealization: Adaptive integrated approach for idealization of ion-channel currents.

Single-channel electrophysiological recordings provide insights into transmembrane ion permeation and channel gating mechanisms. The first step in the analysis of the recorded currents involves an "idealization" process, in which noisy raw data are classified into two discrete levels corresponding to the open and closed states of channels. This provides valuable information on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios and baseline drifts, especially when the gating model of the target channel is not identified. We report herein on a highly robust model-free idealization method for achieving this goal. The algorithm, called adaptive integrated approach for idealization of ion-channel currents (AI2), is composed of Kalman filter and Gaussian mixture model clustering and functions without user input. AI2 automatically determines the noise reduction setting based on the degree of separation between the open and closed levels. We validated the method on pseudo-channel-current datasets that contain either computed or experimentally recorded noise. We also investigated the relationship between the noise reduction parameter of the Kalman filter and the cutoff frequency of the low-pass filter. The AI2 algorithm was then tested on actual experimental data for biological channels including gramicidin A, a voltage-gated sodium channel, and other unidentified channels. We compared the idealization results with those obtained by the conventional methods, including the 50%-threshold-crossing method.

Four Temporary Waterslide Designs Adapted to Different Slope Conditions to Encourage Child Socialization in Playgrounds.

Increased urbanization has decreased children's access to various natural outdoor environments. To counteract this deficiency in early life experiences, we designed four temporary waterslides, each tailored to different city-side park conditions. The waterslides were simple to construct, with frames built from easy-to-attain resources such as bamboo rods from a local forest and simple pipes and joints overlaid by a tarp. Plywood boards, cardboard, and a tarp were used to create a pool at the foot of the slides, which were placed on existing slopes or stairs in each park. Water was continually released down the slide during each 1-2 h event. At each park event, children gathered spontaneously to use the slides and interact socially. No serious accidents occurred during the waterslide trials. To understand how the children used each waterslide, the activity at the waterslides was recorded by video. The minute of the highest activity level at the waterslide was quantitatively analyzed to determine the lines of flow surrounding the waterslide and the mean and maximum speeds attained while using the waterslide.

An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

BACKGROUND: Adverse health effects resulting from falls are a major public health concern. Although studies have identified risk factors for falls, none have examined long-term prediction of fall risk. Furthermore, recent evidence suggests that there are additional risk factors, such as psychosocial factors. OBJECTIVE: In this 3-year longitudinal study, we evaluated a predictive model for risk of fall among community-dwelling older adults using machine learning methods. DESIGN: A 3-year follow-up prospective longitudinal study (from 2010 to 2013). SETTING: Twenty-four municipalities in nine of the 47 prefectures (provinces) of Japan. PARTICIPANTS: Community-dwelling individuals aged ≥65 years who were functionally independent at baseline (n = 61,883). METHODS: The baseline survey was conducted from August 2010 to January 2012, and the follow-up survey was conducted from October to December 2013. Both surveys were conducted involving self-reported questionnaires. The measured outcome at the follow-up survey was self-reported multiple falls during the previous year. The 142 variables included in the baseline survey were regarded as candidate predictors. The random-forest-based Boruta algorithm was used to select predictors, and the eXtreme Gradient Boosting algorithm with 10 repetitions of nested k-fold cross-validation was used for modeling and model evaluation. Furthermore, we used shapley additive explanations to gain insight into the behavior of the prediction model. KEY RESULTS: Fourteen out of 142 candidate features were selected as predictors. Among these predictors, experience of falling as of the baseline survey was the most important feature, followed by self-rated health and age. Moreover, sense of coherence was newly identified as a risk factor for falls. CONCLUSIONS: This study suggests that machine learning tools can be adapted to explore new associative factors, make accurate predictions, and provide actionable insights for fall prevention strategies.

Supervised machine learning algorithm identified KRT20, BATF and TP63 as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patients.

OBJECTIVES: This study was carried out to identify biomarkers that distinguish Hunner-type interstitial cystitis from non-Hunner-type interstitial cystitis patients. METHODS: Total ribonucleic acid was purified from 212 punch biopsy specimens of 89 individuals who were diagnosed as interstitial cystitis/bladder pain syndrome. To examine the expression profile of patients' bladder specimens, 68 urothelial master transcription factors and nine known markers (E-cadherin, cytokeratins, uroplakins and sonic hedgehog) were selected. To classify the biopsy samples, principal component analysis was carried out. A decision tree algorithm was adopted to identify critical determinants, in which 102 and 116 bladder specimens were used for learning and validation, respectively. RESULTS: Principal component analysis segregated tissues from Hunner-type and non-Hunner-type interstitial cystitis specimens in principal component axes 2 and 4. Principal components 2 and 4 contained urothelial stem/progenitor transcription factors and cytokeratins, respectively. A decision tree identified KRT20, BATF and TP63 to classify non-Hunner-type and Hunner-type interstitial cystitis specimens. KRT20 was lower in tissues from Hunner-type compared with non-Hunner-type interstitial cystitis specimens (P < 0.001). TP63 was lower in Hunner's lesions compared with adjacent mucosa from Hunner-type interstitial cystitis patients (P < 0.001). Blinded validation using additional biopsy specimens verified that the decision tree showed fairly precise concordance with cystoscopic diagnosis. CONCLUSION: KRT20, BATF and TP63 were identified as biologically relevant biomarkers to classify tissues from interstitial cystitis/bladder pain syndrome specimens. The biologically explainable determinants could contribute to defining the elusive interstitial cystitis/bladder pain syndrome pathogenesis.

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis.

Prevalence of tooth loss has increased due to population aging. Tooth loss negatively affects the overall physical and social well-being of older adults. Understanding the role of socio-demographic and other predictors associated with tooth loss that are measured in non-clinical settings can be useful in community-level prevention. We used high-dimensional epidemiological data to investigate important factors in predicting tooth loss among older adults over a 6-year period of follow-up. Data was from participants of 2010 and 2016 waves of the Japan Gerontological Evaluation Study (JAGES). A total of 19,407 community-dwelling functionally independent older adults aged 65 and older were included in the analysis. Tooth loss was measured as moving from a higher number of teeth category at the baseline to a lower number of teeth category at the follow-up. Out of 119 potential predictors, age, sex, number of teeth, denture use, chewing difficulty, household income, employment, education, smoking, fruit and vegetable consumption, community participation, time since last health check-up, having a hobby, and feeling worthless were selected using Boruta algorithm. Within the 6-year follow-up, 3013 individuals (15.5%) reported incidence of tooth loss. People who experienced tooth loss were older (72.9 ± 5.2 vs 71.8 ± 4.7), and predominantly men (18.3% vs 13.1%). Extreme gradient boosting (XGBoost) machine learning prediction model had a mean accuracy of 90.5% (±0.9%). A visual analysis of machine learning predictions revealed that the prediction of tooth loss was mainly driven by demographic (older age), baseline oral health (having 10-19 teeth, wearing dentures), and socioeconomic (lower household income, manual occupations) variables. Predictors related to wide a range of determinants contribute towards tooth loss among older adults. In addition to oral health related and demographic factors, socioeconomic factors were important in predicting future tooth loss. Understanding the behaviour of these predictors can thus be useful in developing prevention strategies for tooth loss among older adults.

Development of new software enabling automatic identification of the optimal anatomical liver resectable region, incorporating preoperative liver function.

Determining the resectable region and volume of the liver prior to anatomical resection is important. The synapse Vincent (SV) system is the current method for surgical liver resection that relies on the surgeon's individual experience and skill. Additionally, in cases involving abnormal liver function, the resectable region is limited due to deteriorating liver function, thus making the determination of the hepatectomy region challenging. The current study outlines a novel 3D Hariyama-Shimoda Soft (HSS) simulation software that can be used to automatically simulate the optimal hepatectomy region under a limited resectable liver volume. The current study recruited patients with hepatic malignant tumors that were scheduled for anatomical resection. The influence of the tumor on each portal vein point was quantified in accordance with the tumor domination ratio (TDR). The resectable region was subsequently determined so that the sum of the TDR was the maximum estimated resectable liver volume (ERLV). The maximum ERLV settings utilized were within Makuuchi's criteria. ERLV was compared with the actual resected liver volume (ARLV) using SV and HSS. A total of 15 patients were included in the present study. The median ERLV was not significantly different between the two groups (P=0.15). However, the correlation between ERLV and ARLV, for SV and HSS, was statistically significant [SV ERLV (ml) = 1.139 × HSS ERLV (ml) + 30.779 (P=0.001)]. In conclusion, HSS may be an effective 3D simulation system. TDR and ERLV were indicated to be novel factors that may be incorporated into simulation software for use in anatomical resection surgery.

Implementation of a custom hardware-accelerator for short-read mapping using Burrows-Wheeler alignment.

The mapping of millions of short DNA fragments to a large genome is a great challenge in modern computational biology. Usually, it takes many hours or days to map a large genome using software. However, the recent progress of programmable hardware such as field programmable gate arrays (FPGAs) provides a cost effective solution to this challenge. FPGAs contain millions of programmable logic gates to design massively parallel accelerators. This paper proposes a hardware architecture to accelerate the short-read mapping using Burrows-Wheeler alignment. The speed-up of the proposed architecture is estimated to be at least 10 times compared to its equivalent software application.