Rare bird forecast: A combined approach using a long-term dataset of an Arctic seabird and a numerical weather prediction model.

Wildlife observation is a popular activity, and sightings of rare or difficult-to-find animals are often highly desired. However, predicting the sighting probabilities of these animals is a challenge for many observers, and it may only be possible by limited experts with intimate knowledge and skills. To tackle this difficulty, we developed user-friendly forecast systems of the daily observation probabilities of a rare Arctic seabird (Ross's Gull Rhodostethia rosea) in a coastal area in northern Japan. Using a dataset gathered during 16 successive winters, we applied a machine learning technique of self-organizing maps and explored how days with gull sightings were related to the meteorological pressure patterns over the Sea of Okhotsk (Method A). We also built a regression model that explains the relationship between gull sightings and local-scale environmental factors (Method B). We then applied these methods with the operational global numerical weather prediction model (a computer simulation application about the fluid dynamics of Earth's atmosphere) to forecast the daily observation probabilities of our target. Method A demonstrated a strong dependence of gull sightings on the 16 representative weather patterns and forecasted stepwise observation probabilities ranging from 0% to 85.7%. Method B also showed that the strength of the northerly wind and the advancement of the season explained gull sightings and forecasted continuous observation probabilities ranging from 0% to 95.5%. Applying these two methods with the operational global numerical weather prediction model successfully forecasted the varied observation probabilities of Ross's Gull from 1 to 5 days ahead from November to February. A 2-year follow-up observation also validated both forecast systems to be effective for successful observation, especially when both systems forecasted higher observation probabilities. The developed forecast systems would therefore allow cost-effective animal observation and may facilitate a better experience for a variety of wildlife observers.

Amitorines A and B, Nitrogenous Diterpene Metabolites of Theonella swinhoei: Isolation, Structure Elucidation, and Asymmetric Synthesis.

Two new nitrogenous prenylbisabolanes never before found in Lithistid sponges have been isolated from Theonella swinhoei. These new diterpenes, named amitorine A (1) and amitorine B (2), containing a prenylbisabolane skeleton have been characterized by spectroscopic analyses, and the relative and absolute configurations of 1 and 2 were determined by asymmetric synthesis of both diastereomers via the common bicyclic lactone 6 intermediate.

Edge-differentiating deposition of Co on SiO(2)-supported MoS(2) particles.

The chemical vapor deposition of Co(CO)(3)NO under controlled conditions onto MoS(2) catalysts supported on SiO(2) achieves the edge-differentiating deposition of Co between (1010) and (1010) edges of MoS(2) particles, the former edge being preferentially decorated by Co at a low Co content to form a catalytically "active" structure for hydrodesulfurization.

Structure-property relationships of polyselenoethers [-(CH2)ySe-]x (y=1, 2, and 3) and related polyethers and polysulfides.

Conformational characteristics, solution (melt) properties, and thermal properties of polyselenoethers [-(CH2)ySe-]x ( y=1, 2, and 3) have been revealed by the rotational isomeric state analysis of ab initio molecular orbital calculations and (1)H and (13)C NMR experiments for monomeric model compounds and compared with those of typical polyethers and polysulfides. The comparative results are summarized here as correlations in conformation among model compounds and unperturbed and crystalline states of the polymers, and thermal properties of the polymer crystals are discussed in terms of intramolecular and intermolecular interactions. By ab initio molecular orbital calculations under periodic boundary conditions, helical structures of poly(methylene oxide), poly(methylene sulfide), and poly(methylene selenide), and the crystal structure of poly(ethylene selenide) have been optimized, and their electronic structures have been predicted. A systematic methodology to predict and characterize up to higher-order structures and various physical properties of polymers incorporated in different phases has been attempted to be developed.