Waseda University

About: Waseda University is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Catalysis & Large Hadron Collider. The organization has 24220 authors who have published 46859 publications receiving 837855 citations. The organization is also known as: Waseda daigaku & Sōdai.

Gonadotropin-inhibitory hormone identification, cDNA cloning, and distribution in rhesus macaque brain

Abstract: Gonadotropin-inhibitory hormone (GnIH) is a hypothalamic neuropeptide that modulates the reproductive physiology of birds and mammals by inhibiting gonadotropin secretion from the anterior pituitary gland. GnIH can also directly inhibit reproductive behaviors, possibly via action within the brain. Identification of the distribution of GnIH neurons and fibers may provide us with clues to how the brain controls reproductive activities of the animal. Here, we characterized the location and connectivity of GnIH neurons in the rhesus macaque (Macaca mulatta) brain. We determined the macaque GnIH precursor mRNA, and further identified a mature GnIH peptide (SGRNMEVSLVRQVLNLPQRF-NH(2)) by mass spectrometry combined with immunoaffinity purification. The majority of GnIH precursor mRNA-positive and GnIH-immunoreactive (GnIH-ir) cell bodies were localized in the intermediate periventricular nucleus (IPe) in the hypothalamus, as determined by in situ hybridization and immunocytochemistry, respectively. Abundant GnIH-ir fibers were observed in the nucleus of the stria terminalis in the telencephalon; habenular nucleus, paraventricular nucleus of the thalamus, preoptic area, paraventricular nucleus of the hypothalamus, IPe, arcuate nucleus of hypothalamus, median eminence and dorsal hypothalamic area in the diencephalon; medial region of the superior colliculus, central gray substance of the midbrain and dorsal raphe nucleus in the midbrain; and parabrachial nucleus in the pons. GnIH-ir fibers were observed in close proximity to gonadotropin-releasing hormone-I, dopamine, beta-endorphin, and gonadotropin-releasing hormone-II neurons in the preoptic area, IPe, arcuate nucleus of hypothalamus, and central gray substance of midbrain, respectively. GnIH neurons might thus regulate several neural systems in addition to pituitary gonadotropin release.

Axial rotation of sliding actin filaments revealed by single-fluorophore imaging

Abstract: In the actomyosin motor, myosin slides along an actin filament that has a helical structure with a pitch of ≈72 nm. Whether myosin precisely follows this helical track is an unanswered question bearing directly on the motor mechanism. Here, axial rotation of actin filaments sliding over myosin molecules fixed on a glass surface was visualized through fluorescence polarization imaging of individual tetramethylrhodamine fluorophores sparsely bound to the filaments. The filaments underwent one revolution per sliding distance of ≈1 μm, which is much greater than the 72 nm pitch. Thus, myosin does not “walk” on the helical array of actin protomers; rather it “runs,” skipping many protomers. Possible mechanisms involving sequential interaction of myosin with successive actin protomers are ruled out at least for the preparation described here in which the actin filaments ran rather slowly compared with other in vitro systems. The result also indicates that each “kick” of myosin is primarily along the axis of the actin filament. The successful, real-time observation of the changes in the orientation of a single fluorophore opens the possibility of detecting a conformational change(s) of a single protein molecule at the moment it functions.

SiCloPe: Silhouette-Based Clothed People

Abstract: We introduce a new silhouette-based representation for modeling clothed human bodies using deep generative models. Our method can reconstruct a complete and textured 3D model of a person wearing clothes from a single input picture. Inspired by the visual hull algorithm, our implicit representation uses 2D silhouettes and 3D joints of a body pose to describe the immense shape complexity and variations of clothed people. Given a segmented 2D silhouette of a person and its inferred 3D joints from the input picture, we first synthesize consistent silhouettes from novel view points around the subject. The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction. We then infer the texture of the subject's back view using the frontal image and segmentation mask as input to a conditional generative adversarial network. Our experiments demonstrate that our silhouette-based model is an effective representation and the appearance of the back view can be predicted reliably using an image-to-image translation network. While classic methods based on parametric models often fail for single-view images of subjects with challenging clothing, our approach can still produce successful results, which are comparable to those obtained from multi-view input.

Control of decoherence: Analysis and comparison of three different strategies

Abstract: We analyze and compare three different strategies, all aimed at controlling and eventually halting decoherence. The first strategy hinges upon the quantum Zeno effect, the second makes use of frequent unitary interruptions (``bang-bang'' pulses and their generalization, quantum dynamical decoupling), and the third uses a strong, continuous coupling. Decoherence is shown to be suppressed only if the frequency $N$ of the measurements or pulses is large enough or if the coupling $K$ is sufficiently strong. Otherwise, if $N$ or $K$ is large, but not extremely large, all these control procedures accelerate decoherence. We investigate the problem in a general setting and then consider some practical examples, relevant for quantum computation.