Skeleton phylogeny reconstructed with transcriptomes for the tribe Drosophilini (Diptera: Drosophilidae).

The family Drosophilidae is one of the most important model systems in evolutionary biology. Thanks to advances in high-throughput sequencing technology, a number of molecular phylogenetic analyses have been undertaken by using large data sets of many genes and many species sampled across this family. Especially, recent analyses using genome sequences have depicted the family-wide skeleton phylogeny with high confidence.

Early Clinical Outcomes of the First Commercialized Human Autologous Ex Vivo Cultivated Oral Mucosal Epithelial Cell Transplantation for Limbal Stem Cell Deficiency: Two Case Reports and Literature Review.

The first product in the world for ex vivo cultivated oral mucosal epithelial cell transplantation (COMET) to treat limbal stem cell deficiency (LSCD), named Ocural, was launched in June 2021 in Japan. COMET was performed on two patients, including the first case in the post-marketing phase of Ocural. Pathological and immunohistochemical examinations were also carried out using specimens obtained before and after COMET and the spare cell sheet.

Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training.

We present semi-supervised information maximizing self-argument training (IMSAT), a neural network-based classification method that works without the preparation of labeled data. Semi-supervised IMSAT can amplify specific differences and avoid undesirable misclassification in accordance with the purpose. We demonstrate that semi-supervised IMSAT has a comparable performance with existing methods for semi-supervised learning of image classification and can also classify real experimental data (X-ray diffraction patterns and thermoelectric hysteresis curves) in the same way even though their shape and dimensions are different.

Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning.

High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to collect, HTC does not represent the actual materials data obtained by HTEs in many cases.

Machine-learning guided discovery of a new thermoelectric material.

Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect.

Flexible heat-flow sensing sheets based on the longitudinal spin Seebeck effect using one-dimensional spin-current conducting films.

Heat-flow sensing is expected to be an important technological component of smart thermal management in the future. Conventionally, the thermoelectric (TE) conversion technique, which is based on the Seebeck effect, has been used to measure a heat flow by converting the flow into electric voltage. However, for ubiquitous heat-flow visualization, thin and flexible sensors with extremely low thermal resistance are highly desired.