Morphological, Developmental, and Ecological Characteristics of the Suctorian Ciliate Ephelota gigantea (Ciliophora, Phyllopharyngea, Ephelotidae) Found on Cultured Wakame Seaweed in Northeastern Japan

Wakame seaweed is an important aquatic resource in Iwate Prefecture. However, a suctorian Ephelota gigantea sometimes causes great damage to wakame culture. Since little is known about the biological characteristics of E. gigantea, its detailed morphology and temporal change of biological characteristics during the 2010 culture season were investigated. Scanning electron microscope observations showed that E. gigantea had different striation patterns on the stalk; there was a swell made of cement by which the stalk was attached to wakame firmly; and the buds had cilia arranged in concentric circles about a ring in the center of the ventral side. A suctorian parasite was found to infect E. gigantea, and the infection seemed to have decreased drastically the attached density of E. gigantea on wakame. Cell size of parasite-infected E. gigantea individuals was larger than that of uninfected individuals, probably because larger E. gigantea has larger surface area for attachment of the parasite. Cyst formation or conjugating individuals were not observed

Freshwater mussels (Bivalvia: Unionidae) from the rising sun (Far East Asia): phylogeny, systematics, and distribution

Freshwater mussels (Bivalvia: Unionidae) is a diverse family with around 700 species being widespread in the Northern Hemisphere and Africa. These animals fulfill key ecological functions and provide important services to humans. Unfortunately, populations have declined dramatically over the last century, rendering Unionidae one of the world’s most imperiled taxonomic groups. In Far East Asia (comprising Japan, Korea, and Eastern Russia), conservation actions have been hindered by a lack of basic information on the number, identity, distribution and phylogenetic relationships of species. Available knowledge is restricted to studies on national and sub-national levels. The present study aims to resolve the diversity, biogeography and evolutionary relationships of the Far East Asian Unionidae in a globally comprehensive phylogenetic and systematic context.We reassessed the systematics of all Unionidae species in the region, including newly collected specimens from across Japan, South Korea, and Russia, based on molecular (including molecular species delineation and a COI + 28S phylogeny) and comparative morphological analyses. Biogeographical patterns were then assessed based on available species distribution data from the authors and previous reference works.We revealed that Unionidae species richness in Far East Asia is 30% higher than previously assumed, counting 43 species (41 native + 2 alien) within two Unionidae subfamilies, the Unioninae (32 + 1) and Gonideinae (9 + 1). Four of these species are new to science, i.e. Beringiana gosannensis sp. nov., Beringiana fukuharai sp. nov., Buldowskia kamiyai sp. nov., and Koreosolenaia sitgyensis gen. & sp. nov. We also propose a replacement name for Nodularia sinulata, i.e. Nodularia breviconcha nom. nov. and describe a new tribe (Middendorffinaiini tribe nov.) within the Unioninae subfamily. Biogeographical patterns indicate that this fauna is related to that from China south to Vietnam until the Mekong River basin. The Japanese islands of Honshu, Shikoku, Kyushu, Hokkaido, and the Korean Peninsula were identified as areas of particularly high conservation value, owing to high rates of endemism, diversity and habitat loss. The genetically unique species within the genera Amuranodonta, Obovalis, Koreosolenaia gen. nov., and Middendorffinaia are of high conservation concern

Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning

Reflection high-energy electron diffraction (RHEED) data are important for the in-situ characterization of surface conditions during physical vapor deposition. Surface superstructures obtained by adsorbing exotic atoms onto a clean silicon surface, which exhibit various physical properties, were identified using RHEED. However, this information is too abundant for quantitative analysis; therefore, scientists rely on their expertise to interpret RHEED patterns to assess surface structures and evaluate film thickness, and a large amount of information remains unused. In this study, we adapted machine learning for a RHEED pattern dataset of a clean Si(111) surface during indium deposition in molecular-beam epitaxy growth to use the entire RHEED pattern image information and investigated appropriate machine leaning analysis methods. First, we aimed to determine RHEED pattern similarities in the dataset. Then, five structural phases, 7 × 7 (clean surface), √3×√3, √31×√31, 4 × 1, and 4 × 1 (Room Temperature), were automatically detected by hierarchical clustering using Ward’s method. Next, we aimed to extract the information for each surface superstructure from the dataset. Using non-negative matrix factorization, we successfully estimated the optimal forming conditions for each surface superstructure separately more accurately than the conventional methods. Our proposed strategies could be widely applied to surface structural analysis