Correlation between biochemical and clinical hyperandrogenism parameter in polycystic ovary syndrome in relation to age.

BACKGROUND: To assess the correlation between clinical and biochemical hyperandrogenism parameters in polycystic ovary syndrome (PCOS) according to age. METHODS: This prospective study included 256 PCOS patients diagnosed according to the Rotterdam criteria in a university-based hospital. Androgen levels were measured using liquid chromatography-tandem mass spectrometry. Hirsutism, acne, and alopecia were assessed using the modified Ferriman-Gallwey (mF-G) score, Comprehensive Acne Severity Scale (CASS), and the Ludwig scale, respectively. The correlation between biochemical and clinical hyperandrogenism parameters was assessed in younger and older women with PCOS. RESULTS: The 256 PCOS patients were classified by age into two groups: age 18-29 years (n = 151) and age 30-40 years (n = 84). In women with PCOS, mF-G was significantly positively correlated with the free androgen index (FAI), dehydroepiandrosterone (DHEA), and DHEA sulfate (DHEA-S). CASS had a significant positive correlation with DHEA. mF-G was positively correlated with FAI in those aged 18-29 years, but the correlations were not significant in those aged 30-40 years. The positive correlation between specific body regions of clinical hyperandrogenism, especially mF-G of chin, lower abdomen, and thighs, and testosterone, as well as with FAI, was highest in those aged 18-29 years. In those aged 30-40 years clinical hyperandrogenism was mainly affected by DHEA, DHEA-S, and dihydrotestosterone. CONCLUSION: The correlation between biochemical and clinical hyperandrogenism parameters varied with age in our East Asian population. Clinical hyperandrogenism was positively correlated with FAI in younger women with PCOS. The correlation between biochemical and clinical hyperandrogenism was not significant in older women with PCOS.

Simulation and Experimental of Infiltration and Solidification Process for Al2O3(3D)/5083Al Interpenetrating Phase Composite for High Speed Train Prepared by Low-Pressure Infiltration.

Understanding the infiltration and solidification processes of liquid 5083Al alloy into Al2O3 three-dimensional reticulated porous ceramic (Al2O3(3D) RPC) is essential for optimizing the microstructure and properties of Al2O3(3D)/5083Al interpenetrating phase composites (IPCs) prepared by low-pressure infiltration process (LPIP). This study employs ProCAST software to simulate the infiltration and solidification processes of liquid 5083Al with pouring velocities (PV) of 0.4 m/s infiltrating into Al2O3(3D) RPC preforms with varying porosities at different pouring temperatures (PT) to prepare Al2O3(3D)/5083Al IPCs using LPIP. The results demonstrate that pore diameter of Al2O3(3D) RPC preforms and PT of liquid 5083Al significantly influence the of the infiltration. Solidification process analysis reveals that the Al2O3(3D) RPC preform with smaller pore diameters allows the lower pouring velocity of 5083Al to solidify faster compared to the preform with larger pore diameters. Al2O3(3D)/5083Al IPCs were prepared successfully from Al2O3(3D) RPC porosity of 15 PPI with liquid 5083Al at PV 0.4 m/s and PT 800 °C using LPIP, resulting in nearly fully dense composites, where both Al2O3(3D) RPCs and 5083Al interpenetrate throughout the microstructure. The infiltration and solidification defects were reduced under air pressure of 0.3 MPa (corresponding to PV of 0.4 m/s) during LPIP. Finite volume method simulations are in good agreement with experimental data, validating the suitability of the simplified model for Al2O3(3D) RPCs in the infiltration simulation.

Spatial analysis of global Bitcoin mining.

Bitcoin mining is not only the fundamental process to maintain Bitcoin network, but also the key linkage between the virtual cryptocurrency and the physical world. A variety of issues associated with it have been raised, such as network security, cryptoasset management and sustainability impacts. Investigating Bitcoin mining from a spatial perspective will provide new angles and empirical evidence with respect to extant literature. Here we explore the spatial distribution of Bitcoin mining through bottom-up tracking and geospatial statistics. We find that mining activity has been detected at more than 6000 geographical units across 139 countries and regions, which is in line with the distributed design of Bitcoin network. However, in terms of computing power, it has demonstrated a strong tendency of spatial concentration and association with energy production locations. We also discover that the spatial distribution of Bitcoin mining is dynamic, which fluctuates with diverse patterns, according to economic and regulatory changes.

Water cycle changes over the Mediterranean: a comparison study of a super-high-resolution global model with CMIP3.

Water cycle components over the Mediterranean for both a current run (1979-2007) and a future run (2075-2099) are studied with the Japan Meteorological Agency's 20 km grid global climate model. Results are compared with another study using the Coupled Model Intercomparison Project Phase 3 ensemble model (hereafter, the Mariotti model). Our results are surprisingly close to Mariotti's. The projected mean annual change rates of precipitation (P) between the future and the current run for sea and land are -11 per cent and -10 per cent, respectively, which are not as high as Mariotti's. Projected changes for evaporation (E) are +9.3 per cent and -3.6 per cent, compared with +7.2 per cent and -8.1 per cent in Mariotti's study, respectively. However, no significant difference in the change in P-E over the sea body was found between these two studies. The increased E over the eastern Mediterranean was found to be higher than that in the western Mediterranean, but the P decrease was lower. The net moisture budget, P-E, shows that the eastern Mediterranean will become even drier than the western Mediterranean. The river model suggests decreasing water inflow to the Mediterranean of approximately 36 per cent (excluding the Nile).