Despite their relatively low sampling factor, the freely available, randomly sampled status streams of Twitter are very useful sources of geographically embedded social network data. To statistically analyze the information Twitter provides via these streams, we have collected a year's worth of data and built a multi-terabyte relational database from it. The database is designed for fast data loading and to support a wide range of studies focusing on the statistics and geographic features of social networks, as well as on the linguistic analysis of tweets. In this paper we present the method of data collection, the database design, the data loading procedure and special treatment of geo-tagged and multi-lingual data. We also provide some SQL recipes for computing network statistics.

Lake Balaton, a large shallow lake in Central Europe (Hungary), has been the site of extensive ultra‐high‐resolution acoustic and multichannel seismic profiling in the period of 1997–2013. These surveys showed the widespread occurrence of shallow gas in the lake sediments and their immediate substrata. We analyzed about 2000 km of two‐dimensional profiles and mapped the different gas occurrences in the uppermost 20 m. The anomalies caused by free gas were identified, classified, and assigned to upper, middle and lower levels based on gas signatures and stratigraphic position. Monitoring of the uppermost gas front has revealed temporal variations between surveys from different years and seasons that manifested in the changes of free gas content in the upper two levels. Free gas in the lower part of the lake sediments and at around the base of the mud indicated greater stability. The different nature of the three free gas levels can be explained by vertical changes in quantity, production rate, and solubility of methane and carbon dioxide gases. We suggest that methane was derived from the microbial decomposition of organic matter in the mud and Pleistocene peat at the base of the mud, whereas CO2 is transported to the lower mud layers by upwelling fluids.

Purpose To test whether signal intensity percent infarct mapping (SI-PIM) accurately determines the size of myocardial infarct (MI) regardless of its age. Materials and methods Forty-five swine with reperfused MI underwent 1.5T late gadolinium enhancement (LGE) MRI after bolus injection of 0.2mmol/kg Gd(DTPA) on days 2-62 following MI. Animals were classified into acute, healing, and healed groups by pathology. Infarct volume (IV) and infarct fraction (IF) were determined using binary techniques (including 2-5 standard deviations (SD) above the remote, and full-width at half-maximum) and the SI-PIM method by two readers. Triphenyl-tetrazolium-chloride staining (TTC) was performed as reference. Bias (percent under/overestimation of IV relative to TTC) of each quantification method was calculated. Bland-Altman analysis was done to test the accuracy of the quantification methods, while intraclass correlation coefficient (ICC) analysis was done to assess intra-and interobserver agreement. Results Bias of the MRI quantification methods do not depend on the age of the MI. FWHM and SI-PIM gave the best estimate of MI volume determined by the reference TTC (p-values for the FWHM and SI-PIM methods were 0.183, 0.26, 0.95 and 0.073, 0.091, 0.73 in Group 1, Group 2 and Group 3, respectively), while using any of the binary thresholds of 2-4SD above the remote myocardium showed significant overestimation. The 5SD method, however, provided similar IV compared to TTC and was shown to be independent of the size and age of MI. ICC analysis showed excellent inter- and intraobserver agreement between the readers. Conclusions Our results indicate that the SI-PIM method can accurately determine MI volume regardless of the pathological stage of MI. Once tested, it may prove to be useful for the clinic.

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