Natural products that exhibit significant biological activity often possess complex molecular structures such as caged frameworks, strained motifs, inherent instability, and many stereogenic carbon centers, etc. Achievement of those total syntheses always requires the powerful methodologies and judicious strategies to fulfill the stereochemical requirements of the target compounds. Building on our successful stereo-controlled syntheses, we have established the concept of conformational constraint, which renders the approach of reactants under a controlled manner during the bond-forming process through the best orbital overlap. Important factors that affect the proper orientation of substrates are (i) acyclic allyl strain, (ii) stereoelectronic effect, (iii) chelation control, etc. Established methodologies include (i) heteroatom directed conjugate addition for diastereoselective C–C bond formation, (ii) 100% α-selective C-glycosidation by using alkynyl-silane, (iii) cobalt acetylene chemistry for medium-size ring formation, followed by its functional group transformation. The author has named such total concept as conformational constraint and has illustrated it with the finished examples of total syntheses. These examples are taken from maytansine, okadaic acid, tautomycin, tetrodotoxin, ciguatoxin, etc.

Sunlight-driven overall water splitting using particulate photocatalysts is of growing interest as a means of producing green hydrogen from water, because systems based on particulate photocatalysts can be spread over large areas using potentially inexpensive processes. Since the first reports on photocatalytic water splitting in 1980, a variety of materials have been developed. Alongside material development, systems designed for the practical implementation of solar hydrogen production technologies using particulate photocatalysts have recently emerged. This review highlights developments in photocatalyst research and examines the current progress in system design for the large-scale production of solar hydrogen (green hydrogen) based on these materials. Such technology represents a crucial solution in the pursuit of a carbon-neutral society—one of the most urgent global challenges.

This study assessed the feasibility of unconstrained deep-learning-based sleep stage classification using cardiorespiratory and body movement activities derived from piezoelectric sensors installed under a bed mattress. Heart and respiratory rates and their respective variabilities, cardiorespiratory coupling index, and body movement were simultaneously acquired through polysomnography (PSG) for 106 untreated participants with suspected sleep apnea. We used a bidirectional long short-term memory network to predict the five sleep stages using five different input feature combinations. The best performance was achieved with a model comprising six parameters, including cardiorespiratory variability features, with a balanced accuracy of 0.70 ± 0.05, Cohen’s κ of 0.40 ± 0.11, and an F1 score of 0.63 ± 0.08. Deming regression and Bland–Altman analyses of the six major sleep parameters estimated by the model and those determined by PSG showed significant correlations (r = 0.426–0.781) with a low bias. These results demonstrate the effectiveness of the proposed approach and its potential to expand opportunities for in-home sleep monitoring.