Table 25 Sample Clauses

Table 25. Do you think that focusing on the vocabulary could be a better perspective to learn how to handle English manuals?................………….. 54
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Table 25. Sequence Example 1 from the Spanish-Catalan dataset (5-6 y.o. age group, session ID ES_12_A_KL2) 61 Table 26: Sequence Example 2 from the Spanish/Catalan dataset (8-9 y.o. age group, session ID ES_6_B_KL1) 63 Table 27: Sequence Example 3 from the Spanish/Catalan dataset (14-15 y.o. age group, session ID ES_21_C_KL2) 65 Table 28: Sequence Example 1 from the Cypriot dataset (5-6 y.o. age group, session ID CY_4_A_KL1) 66 Table 29: Sequence Example 2 from the Cypriot dataset (8-9 y.o. age group, session ID CY_11_B_KL1) 68 Table 30: Sequence Example 3 from the Cypriot dataset (8-9 y.o. age group, session ID CY_11_B_KL1) 70 Table 31: Sequence Example 1 from the Israeli dataset (5-6 y.o. age group, session ID IL_2_A_KL1) 72 Table 32: Sequence Example 2 from the Israeli dataset (primary age group, session ID IL_10_B_KL1) 73 Table 33: Sequence Example 3 from the Israeli dataset (14-15 y.o. age group, session ID IL_21_C_KL2) 76 Table 34: Sequence Example 1 from the German dataset (5-6 y.o. age group, session ID DE_17_A_KL2) 78 Table 35: Sequence Example 2 from the German dataset (8-9 y.o. age group, session ID DE_5_B_KL1) 79 Table 36: Sequence Example 3 from the German dataset (14-15 y.o. age group, session ID DE_17_C_KL1) 81 Table 37: Sequence Example 1 from the Lithuanian dataset (5-6 y.o. age group, session ID LT_19_A_KL2) 82 Table 38: Sequence Example 2 from the Lithuanian dataset (8-9 y.o. age group, session ID LT_5_B_KL1) 84 Table 39: Sequence Example 3 from the Lithuanian dataset (14-15 y.o. age group, session ID LT_12_C_KL1) 85 Table 40: Qualitative levels of students’ Expanding moves in the DIALLS dataset 87 Table 41: Qualitative levels of students’ Inviting moves in the DIALLS dataset. 90 Table 42: Qualitative levels of students’ Reasoning moves in the DIALLS dataset 92
Table 25. Does your High School have a language laboratory to practice in? ……………………………………………………………………………..48
Table 25. NPS Estimated Cutthroat Trout Spawning Abundance and Genetic Budget and Schedule 2015 May - July Conduct four to five bi-weekly spawner surveys at 8 side channel index sites (1-GS/9 Ecologist and 1- GS/6 Bio Tech for a total of 52 person-days) $7,256 $2,856 $2, 200 $2, 200 Aug-Sept Conduct two snorkel surveys (Aug/Sept) in 8 side-channel/trib. index reaches (1–GS/9 Ecologist and 1-GS/6 Bio Tech for total of 8 person-days) $1,876 $876 $500 $500 Aug-Sept Collect cutthroat/rainbow young-of- year for genetic analysis in four side channel reaches (1–GS/9 Ecologist and 1-GS/6 Bio Tech for total of 4 person-days) $938 $338 $300 $300 Oct-Dec USGS WFRC Lab analyses, Data Mgt. and Reporting (100 samples @ $55/sample including Overhead) $5,500 $2,500 $1,500 $1,500 Nov-Dec Data Mgt. and Reporting (1-GS/9 Ecol. for 12 days, 1-GS/12 Ecol. for 3 days) $5,496 $5,496 May-Oct Travel (Ferry and per diem) $1,556 $1,556 Vehicle (1.5 months @ $800/month) $840 $840 Supplies $1,000 $1,000 2015 Estimated Totals: $24,462 $9,966 $4,500 $9,966
Table 25. Incidence crude rate of MDET before and after the revision Incidence rate before revision Incidence rate after revision NEURO ENDOCRINE TUMOURS 2.63 2.60 Well diff endocrine tumours, carcinoid 0.37 0.37 Well diff endocrine tumours, atypical carcinoid 0.00 0.00 Mixed endocrine exocrine carcinoma 0.00 0.00 Endocrine carcinoma of Thyroid gland 0.29 0.29 Well diff endocrine carcinoma functioning Pancreas Digest tract 0.02 0.02 Endocrine carcinoma of Skin 0.14 0.14 Relative Survival (%) Relative Survival (%) before revision after revision 1 year 5-year 1 year 5-year NEURO ENDOCRINE TUMOURS 69.8 54.4 69.4 54.1 Well diff endocrine tumours, carcinoid 64.9 37.2 64.9 37.2 Well diff endocrine tumours, atypical carcinoid + + + + Mixed endocrine exocrine carcinoma 50.9 55.4 50.9 55.4 Endocrine carcinoma of Thyroid gland 90.7 82.6 90.7 82.6 Well diff endocrine carcinoma functioning Pancreas Digest tract 88.8 63.0 87.0 61.6 Endocrine carcinoma of Skin 76.9 48.9 76.9 48.9 + statistic could not be calculated The revision had a marginal impact on mesothelioma, liver angiosarcoma, sarcoma and atypical chronic myeloid leukemia estimates. The survival decreased after the revision for all the tumours considered: mesothelioma from 7.2% to 5.5%; liver angiosarcoma from 23% to 19%, GIST from 85% to 68%. The incidence of atypical chronic myeloid leukemia reached 0.02/100,000 and the 5-year survival was estimated to 25%. Before the revision, the limited number of cases didn't allow an adequate estimate of both, incidence and 5-year survival. The correction of misclassified cases allowed to provide more precise estimates of incidence and survival. In same cases (atypical chronic myeloid leukemia), it allowed to have an estimate previously xxxxxxx because of the too limited number of cases. For the other tumours, the revision had no impact on incidence or survival. The proportion of unspecified morphologies remained high for the majority of the tumours revised. This confirms that a certain proportion of “NOS” cases exists and is related to difficulties in reaching a diagnosis probably due to the health care organisation. An efficient health system is particularly important in dealing with rare cancers because of the difficulty of diagnosis and of the complexity of treatments. The misclassification problems addressed by this revision focused on quality, completeness of diagnosis and follow-up. Although relevant, the proportion of cases with possible problems of misclassification were o...

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