Tropical cyclone dataset for a high-resolution global nonhydrostatic atmospheric simulation.

This dataset is a time series of tropical cyclones simulated using the high-resolution Nonhydrostatic Icosahedral Atmospheric Model (NICAM). By tracking tropical cyclones from 30 years of simulation data, 2,463 tracks that include the life stages of precursors (pre-TCs), tropical cyclones (TCs), and post-tropical cyclones (post-TCs), if any, were extracted. Each track data includes the time, latitude, longitude, maximum wind speed, minimum pressure, elapsed time since onset, and life-stage label of the tropical cyclone. The numbers of steps (6 h) for pre-TCs, TCs, and post-TCs were 45,288, 55,206, and 37,312, respectively. The dataset for each step also consists of atmospheric field data of multiple physical quantities, such as outgoing longwave radiation at the top-of-the-atmosphere, sea level pressure, sea surface temperature, specific humidity at 600 hPa, and zonal and meridional winds at 850 and 200 hPa over a 1000 km2 area that includes a tropical cyclone at its center. This dataset can be used to develop machine-learning models for the detection, intensity prediction, and cyclogenesis prediction of tropical cyclones.

Molecular characteristics of an adhesion molecule containing cholesterol-dependent cytolysin-motif produced by mitis group streptococci.

Streptococcus pseudopneumoniae (SPpn) is a relatively new species closely related to S. pneumoniae (SPn) and S. mitis (SM) belonging to the Mitis group of the genus Streptococcus (MGS). Although genes encoding various pneumococcal virulence factors have been observed in the SPpn genome, the pathogenicity of SPpn against human, including the roles of virulence factor candidates, is still unclear. The present study focused on and characterized a candidate virulence factor previously reported in SPpn with deduced multiple functional domains, such as lipase domain, two lectin domains, and cholesterol-dependent cytolysin-related domain using various recombinant proteins. The gene was found not only in SPpn but also in the strains of SM and SPn. Moreover, the gene product was expressed in the gene-positive strains as secreted and cell-bound forms. The recombinant of gene product showed lipase activity and human cell-binding activity depending on the function of lectin domain(s), but no hemolytic activity. Thus, based on the distribution of the gene within the MGS and its molecular function, the gene product was named mitilectin (MLC) and its contribution to the potential pathogenicity of the MLC-producing strains was investigated. Consequently, the treatment with anti-MLC antibody and the mlc gene-knockout significantly reduced the human cell-binding activity of MLC-producing strains. Therefore, the multifunctional MLC was suggested to be important as an adhesion molecule in considering the potential pathogenicity of the MLC-producing strains belonging to MGS, such as SPpn and SM.

Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble.

Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere-only and coupled simulations run over the period 1950-2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050.

Cytotoxic property of Streptococcus mitis strain producing two different types of cholesterol-dependent cytolysins.

Streptococcus mitis strain Nm-65 secretes an atypical 5-domain-type cholesterol-dependent cytolysin (CDC) called S. mitis-derived human platelet aggregation factor (Sm-hPAF) originally described as a platelet aggregation factor. Sm-hPAF belongs to Group III CDC that recognize both membrane cholesterol and human CD59 as the receptors, and shows preferential activity towards human cells. Draft genome analyses have shown that the Nm-65 strain also harbors a gene encoding another CDC called mitilysin (MLY). This CDC belongs to Group I CDC that recognizes only membrane cholesterol as a receptor, and it is a homolog of the pneumococcal CDC, pneumolysin. The genes encoding each CDC are located about 20 kb apart on the Nm-65 genome. Analysis of the genomic locus of these CDC-encoding genes in silico showed that the gene encoding Sm-hPAF and the region including the gene encoding MLY were both inserted into a specific locus of the S. mitis genome. The results obtained using deletion mutants of the gene(s) encoding CDC in Nm-65 indicated that each CDC contributes to both hemolysis and cytotoxicity, and that MLY is the major hemolysin/cytolysin in Nm-65. The present study aimed to determine the potential pathogenicity of an S. mitis strain that produces two CDC with different receptor recognition properties and secretion modes.

Madden-Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer.

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden-Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003-2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts.