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. However, the taxon sampling is still insufficient for minor lineages and non-Drosophila genera. In this study, we carried out phylogenetic analyses using a large number of transcriptome-based nucleotide sequences, focusing on the largest, core tribe Drosophilini in the Drosophilidae. In our analyses, some noise factors against phylogenetic reconstruction were taken into account by removing putative paralogy from the datasets and examining the effects of missing data, i.e. gene occupancy and site coverage, and incomplete lineage sorting. The inferred phylogeny has newly resolved the following phylogenetic positions/relationships at the genomic scale: (i) the monophyly of the subgenus Siphlodora including Zaprionus flavofasciatus to be transferred therein; (ii) the paraphyly of the robusta and melanica species groups within a clade comprised of the robusta, melanica and quadrisetata groups and Z. flavofasciatus; (iii) Drosophila curviceps (representing the curviceps group), D. annulipes (the quadrilineata subgroup of the immigrans group) and D. maculinotata clustered into a clade sister to the Idiomyia + Scaptomyza clade, forming together the expanded Hawaiian drosophilid lineage; (iv) Dichaetophora tenuicauda (representing the lineage comprised of the Zygothrica genus group and Dichaetophora) placed as the sister to the clade of the expanded Hawaiian drosophilid lineage and Siphlodora; and (v) relationships of the subgenus Drosophila and the genus Zaprionus as follows: (Zaprionus, (the quadrilineata subgroup, ((D. sternopleuralis, the immigrans group proper), (the quinaria radiation, the tripunctata radiation)))). These results are to be incorporated into the so-far published phylogenomic tree as a backbone (constraint) tree for grafting much more species based on sequences of a limited number of genes. Such a comprehensive, highly confident phylogenetic tree with extensive and dense taxon sampling will provide an essential framework for comparative studies of the Drosophilidae.

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. In case 1, the ocular surface remained free from epithelial defects for approximately six months. In case 2, although defect of the cornea-like epithelia was observed after COMET for one month, it was resolved after the insertion of lacrimal punctal plugs. In case 1, adjuvant treatment was interrupted due to an accident during the second month after COMET, resulting in conjunctival ingrowth and corneal opacity. Eventually, a lamellar keratoplasty was required at six months after COMET. Immunohistochemistry revealed the presence of markers for stem cells (p63, p75), proliferation (Ki-67), and differentiation (Keratin-3, -4, and -13) in both the cornea-like tissue after COMET and a cultivated oral mucosal epithelial cell sheet. In conclusion, Ocural® can be accomplished without major complications, and the stem cells derived from oral mucosa might be successfully engrafted.

Individual multidisciplinary clinical phenotypes of nasal and ocular symptoms in hay fever: Crowdsourced cross-sectional study using AllerSearch.

BACKGROUND: Multidisciplinary efforts to prospectively collect and analyze symptoms of hay fever are limited. We aimed to identify the characteristics of nasal and ocular symptoms of hay fever, using the AllerSearch smartphone application. METHODS: This mobile health-based prospective observational study using the AllerSearch smartphone application was conducted between February 1, 2018, and May 1, 2020. Individuals who downloaded AllerSearch from Japan and provided comprehensive self-assessments (including 17 items related to quality of life [QoL]-related items) were included. The characteristics and risk factors for allergic rhinitis (AR) and allergic conjunctivitis (AC) were identified using hierarchical heat maps and multivariate logistic regression. RESULTS: Of the 9041 participants with hay fever, 58.8% had AR and AC, 22.2% had AR, and 5.7% had AC. The AR-AC comorbid cohort showed worse symptoms of hay fever and QoL scores than the other cohorts. Factors (odds ratio, 95% confidence interval) associated with AR-AC included a lower age (0.98, 0.97-0.98), female sex (1.31, 1.19-1.45), liver disease (1.58, 1.26-2.35), dry eye disease (1.45, 1.30-1.63), unknown dry eye disease status (1.46, 1.31-1.62), contact lens use discontinuation during the hay fever season (1.69, 1.28-2.23), and bedroom flooring material other than hardwood, carpet, tatami, or vinyl (1.91, 1.16-3.14). CONCLUSIONS: Analysis of medical big data for hay fever performed using a mobile health app helped identify risk factors and characteristics of AC, AR, and AR-AC. Phenotyping of highly variable symptoms of hay fever, such as nasal and ocular symptoms, can facilitate better-quality clinical care.

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. Here we propose using a combination of simple HTEs, HTC, and machine learning to predict material properties. We demonstrate that our method enables accurate and rapid prediction of the Kerr rotation mapping of an FexCoyNi1-x-y composition spread alloy. Our method has the potential to quickly predict the properties of many materials without a difficult and expensive HTE and thereby accelerate materials development.

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. Our algorithm will contribute to the automation of big data processing and artificial intelligence-driven material development.

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. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.

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. Recently, another type of TE effect, the longitudinal spin Seebeck effect (LSSE), has aroused great interest because the LSSE potentially offers favourable features for TE applications such as simple thin-film device structures. Here we demonstrate an LSSE-based flexible TE sheet that is especially suitable for a heat-flow sensing application. This TE sheet contained a Ni0.2Zn0.3Fe2.5O4 film which was formed on a flexible plastic sheet using a spray-coating method known as "ferrite plating". The experimental results suggest that the ferrite-plated film, which has a columnar crystal structure aligned perpendicular to the film plane, functions as a unique one-dimensional spin-current conductor suitable for bendable LSSE-based sensors. This newly developed thin TE sheet may be attached to differently shaped heat sources without obstructing an innate heat flux, paving the way to versatile heat-flow measurements and management.