Characterizing Geo-located Tweets in Brazilian Megacities

This work presents a framework for collecting, processing and mining geo-located tweets in order to extract meaningful and actionable knowledge in the context of smart cities. We collected and characterized more than 9M tweets from the two biggest cities in Brazil, Rio de Janeiro and S\~ao Paulo. We performed topic modeling using the Latent Dirichlet Allocation model to produce an unsupervised distribution of semantic topics over the stream of geo-located tweets as well as a distribution of words over those topics. We manually labeled and aggregated similar topics obtaining a total of 29 different topics across both cities. Results showed similarities in the majority of topics for both cities, reflecting similar interests and concerns among the population of Rio de Janeiro and S\~ao Paulo. Nevertheless, some specific topics are more predominant in one of the cities

第698回 千葉医学会例会・第18回 肺癌研究施設例会 40.

<div><p>Dietary protein restriction is not only beneficial to health and longevity in humans, but also protects against air pollution and minimizes feeding cost in livestock production. However, its impact on amino acid (AA) absorption and metabolism is not quite understood. Therefore, the study aimed to explore the effect of protein restriction on nitrogen balance, circulating AA pool size, and AA absorption using a pig model. In Exp.1, 72 gilts weighting 29.9 ± 1.5 kg were allocated to 1 of the 3 diets containing 14, 16, or 18% CP for a 28-d trial. Growth (n = 24), nitrogen balance (n = 6), and the expression of small intestinal AA and peptide transporters (n = 6) were evaluated. In Exp.2, 12 barrows weighting 22.7 ± 1.3 kg were surgically fitted with catheters in the portal and jejunal veins as well as the carotid artery and assigned to a diet containing 14 or 18% CP. A series of blood samples were collected before and after feeding for determining the pool size of circulating AA and AA absorption in the portal vein, respectively. Protein restriction did not sacrifice body weight gain and protein retention, since nitrogen digestibility was increased as dietary protein content reduced. However, the pool size of circulating AA except for lysine and threonine, and most AA flux through the portal vein were reduced in pigs fed the low protein diet. Meanwhile, the expression of peptide transporter 1 (PepT-1) was stimulated, but the expression of the neutral and cationic AA transporter systems was depressed. These results evidenced that protein restriction with essential AA-balanced diets, decreased AA absorption and reduced circulating AA pool size. Increased expression of small intestinal peptide transporter PepT-1 could not compensate for the depressed expression of jejunal AA transporters for AA absorption.</p></div

京都府与謝野町における障害者福祉と福祉ガバナンスに関する調査報告

departmental bulletin pape

Observation of B+ → χc0K+

journal articl

Video_SW_on_off.mp4

This video demonstrte the SW is generated through line-by-line control on our MEMS based metasurface in real-time

Table_3_An online tool for survival prediction of extrapulmonary small cell carcinoma with random forest.xlsx

PurposeExtrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking.MethodsA total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5–5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model.ResultsAmong machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736–0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739–0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/).ConclusionThis study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model.</p

Table_4_An online tool for survival prediction of extrapulmonary small cell carcinoma with random forest.xlsx

PurposeExtrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking.MethodsA total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5–5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model.ResultsAmong machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736–0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739–0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/).ConclusionThis study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model.</p

Table_2_An online tool for survival prediction of extrapulmonary small cell carcinoma with random forest.xlsx

PurposeExtrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking.MethodsA total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5–5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model.ResultsAmong machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736–0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739–0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/).ConclusionThis study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model.</p

Table_1_An online tool for survival prediction of extrapulmonary small cell carcinoma with random forest.docx

PurposeExtrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking.MethodsA total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5–5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model.ResultsAmong machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736–0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739–0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/).ConclusionThis study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model.</p

調査報告 : 米国におけるRoboticsの研究動向調査

departmental bulletin pape