Entry record in the Land Register

The diploma thesis deals with the entry record to the Land Register of the Czech Republic. The adoption of the Act no. 89/2012 Coll., Civil Code and the Act no. 256/2013 Coll., On the Land Register, caused extended changes in the real estate register and cadastral legislation. This changes had a significant influence on some legal concepts relevant for this field of law. The diploma thesis in divided into four parts. The first part is used for the acquisition of basic knowledge on the issue of Land Register, with emphasis on principals that control the operation of the Lend Registry and making entries in it. The second part provides a thorough analysis of the current form of the institute of entry record and registration procedure with regard to previous legislation. The third section is devoted to examining of the German legislation, registration of real estate with an emphasis on the Land Registry, cadastral principles and registration of rights in the Land Register. Content of the last part of the thesis is the comparison of Czech and German legislation, focusing on selected issues and institutes. Powered by TCPDF (www.tcpdf.org

Irrationality of consumer choice and the effect of nudging decision-making: A field experiment on tipping

Ekonomy i psychology po celém světě vždy zajímala otázka procesu lidského rozhodování. Eko- nomové zd·razňují význam teorie úžitku během rozhodování, kdežto psychologové se zaměřují na r·zné vnitřní a vnější stimuly ovlivňující naše rozhodnutí. Oba tyto přístupy m·žeme najít v be- haviorální ekonomií, kde oba koncepty mezi sebou těsně spolupracují. Znalost r·zných ekonomic- kých i psychologických faktor·, které ovlivňují náš výběr m·že být mimo jiné přínosná například ve výzkumu spropitného. Abychom našli r·zné d·vody pro placení spropitného, vytvořili jsme experiment ohledně výše spropitného u českých zákazník·. Procentuální výše spropitného byla zkoumána v závislosti na pohlaví obsluhy, velikosti skupiny, která navštívila restauraci, velikosti účtu na jednu osobu a také byl zkoumán vliv tří na sobě nezávislých intervencí. Tyto intervence byly zaměřeny na altruistické chování, reciprocitu a dobrou náladu zp·sobenou personalizací. Výsledky experimentu jsou překvapující a neshodují se s výsledky z předchozích experiment·. Bylo zjištěno, že intervence spojené s altruismem a reciprocitou mají negativní vliv na výši spro- pitného, avšak intervence spojená s personalizací nemá žádný vliv na spropitné. Ve výsledku se také poukazuje na klíčový vliv země, ve které je experiment proveden.

Additional file 8: Figure S5. of Measure transcript integrity using RNA-seq data

Barplot showing Pearson correlation coefficients between TIN and RNA fragment size. 12 Glioblastoma (GBM) samples were stratified by RIN score; RIN = 10 (red), RIN = 8 (purple), RIN = 6 (orange), RIN = 4 (blue) and RIN = 2 (cyan). (PDF 120 kb

Additional file 14: Table S4. of Measure transcript integrity using RNA-seq data

List of 10 low RIN/medTIN mCRPC and 10 higher RIN/medTIN mCRPC samples used for differential expression analysis. “N” = Metastatic bone site, “V1” = Visit 1. Whole datasets are available with accession # GSM1722952. (XLS 97 kb

Additional file 11: Figure S8. of Measure transcript integrity using RNA-seq data

Relationship between 3′UTR (untranslated regoin) size and TIN score for 12 Glioblastoma (GBM) samples. (a)-(c) three samples with RIN value of 10; (d)-(e), two samples with RIN value of 8; (f)-(h) three samples with RIN value of 6; (i)-(k) three samples with RIN value of 4; (l) one sample with RIN value of 2. r, Pearson correlation coefficient. r, Pearson correlation coefficient. Linear regression lines fitted to data are indicated as black dashed lines. (PDF 7201 kb

Additional file 6: Figure S3. of Measure transcript integrity using RNA-seq data

Evaluating RIN and median TIN score using sample level RNA fragment size as benchmark. Only 28 mCRPC samples with RIN < 3 were used. (a) Scatterplot showing relationship between RIN and average RNA fragment size. (b) Scatterplot showing relationship between median TIN score and RNA fragment size. Linear regression lines fitted to data are indicated as black dashed lines. (PDF 186 kb

Additional file 9: Figure S6. of Measure transcript integrity using RNA-seq data

Smoothed scatter plots showing correlation between TIN score and transcript size. (a)-(c) three samples with RIN value of 10; (d)-(e), two samples with RIN value of 8; (f)-(h) three samples with RIN value of 6; (i)-(k) three samples with RIN value of 4; (l) one sample with RIN value of 2. Blue, orange and red represents low, median and high density of data points, respectively. Transcripts with no read coverage or smaller then 100 nucleotide were removed. r, Pearson correlation coefficient. Linear regression lines fitted to data are indicated as black dashed lines. (PDF 6875 kb

Additional file 19: Table S6. of Measure transcript integrity using RNA-seq data

edgeR detected 289 differentially expressed genes (FDR cutoff = 0.01) in mCRPC samples (after TIN correction). FC = Fold Change; CPM = Count Per Million; FDR = False Discovery Rate. (XLS 45 kb

Additional file 18: Table S5. of Measure transcript integrity using RNA-seq data

edgeR detected 665 differentially expressed genes (FDR cutoff = 0.01) in mCRPC samples (without TIN correction). FC = Fold Change; CPM = Count Per Million; FDR = False Discovery Rate. (XLS 97 kb

Additional file 2: Table S2. of Measure transcript integrity using RNA-seq data

Twenty RNA-seq datasets generated from human peripheral blood mononuclear cell (PBMC)1. Accession number, RNA Integrity Numbers (RIN), and the median Transcript Integrity Numbers (medTIN), total reads, total reads with mapping quality >30, and number of gene with at least 10 reads are listed. The PBMC samples were stored at room temperature for 0 h, 12 h, 24 h, 48 h and 84 h. Each time point contains 4 individuals (replicates). (XLS 9 kb