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.

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.

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.

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.

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.

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.

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.

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.