Anlisis Technology Acceptance Model Pada Industri Perbankan

The first aim of this study was to determine the influence perception of the perceived benefits (PU) and perceived ease of use (PEU) of the attitude (ATU) in receiving information technology (ATI). This research is an explanatory research. Respondents are bank employees as much as 118 peoples. To test the effect of each variable used structural equation modeling analysis techniques (SEM). The results showed a variable perception ease of use (PEU) positive and significant impact on the attitude of using IT (ATU). Variable perception perceived benefits (PU) is positive but not significant effect on attitudes using IT (ATU). Variable perception easy to use (PEU) positive and significant impact on the perceived benefits perception variables (PU). Variable attitude of using IT (ATU) positive and significant impact on the acceptance of IT (ATI). There are research findings that do not support the results of previous studies, namely, perception perceived benefits (PU) but not significant positive effect on the attitude of using IT (ATU). This shows the attitude of the management agreed that the use of information technology is an important banking and its presence is felt very beneficial to the organization and operational staff, but not the key element in determining the attitude to use IT. The management should be able to realize the quality of skilled workers and professional service. The banking industry can standardize that can be used as a reference for the development of IT. IT use also should be able to grow the level of trust and a culture conducive to the customer as a party that directly or indirectly affect the use of IT

Hierarchical clustering and interclass correlations between hESCs and OBNSCs.

<p>A. Hierarchical clustering of 1252 differentially expressed genes was performed using the mean signal intensity for each replicate. Biological replicates of hESCs and OBNSCs were compared and showed high intraclass correlations compared with interclass correlations. Two distinct clusters were distinguishable based on the expression patterns of the different cell types. The differentially expressed transcripts were clustered into two expression groups, including 203 genes that were up regulated in OBNSC compared to 1049 genes that were up regulated in hENSC. B. Expression patterns of representative genes from different expression clusters. Transcripts that are highly up-regulated in hENSC (red) compared with the OBNSC (green).</p

Fluorescence image (20X) of GIBCOR hNSCs at passage 3 that have been cultured in StemProR NSC SFM and stained for the NSC phenotype markers nestin (green) and the proliferation marker Ki67 (red, a).

<p>Cell nuclei were counterstained with DAPI (blue,a). Approximately 90% of the cells stain positive for the undifferentiated NSC marker nestin and the proliferation marker Ki67. Lack of Oct4 staining indicates that there are no remnant hESCs in the culture (data not shown) (Invitrogen, Manual part no. A11592, MAN0001758). Fluorescence images (20X) of GIBCOR hNSCs that have been cultured in StemProR NSC SFM for three passages, and then allowed to differentiate into neurons, oligodendrocytes, or astrocytes. Upon directed differentiation, cells start to lose the undifferentiated NSC marker, nestin, but stain positive for the differentiated cell type markers Dcx, GalC, and GFAP. Cells were stained for the undifferentiated NSC markers nestin (red, b) and SOX2 (green, c) prior to directed differentiation. Cell were then differentiated into neurons and glial cells, and respectively stained for the neuronal marker Dcx (green, c), for the oligodendrocyte marker GalC (red, d), or for the astrocyte marker, GFAP (green, e). The nuclei were counterstained with DAPI (blue) in panels B–D (Invitrogen, Manual part no. A11592, MAN0001758).</p

Expression patterns of representative genes from different GO clusters of OBNSC (class 1) and human ENSC (Class 2).

<p>Expression patterns of representative genes from different GO clusters of OBNSC (class 1) and human ENSC (Class 2).</p

Differentiation potential of long-term proliferated human OBNS cells.

<p>Fluorescent Phase contrast images of 20 human OBNS immunostained for the GFAP astrocytes marker, MAP2 immature neuronal marker, and β-Tubulin mature neuronal marker. Scale bar, 100 µm. The nuclei were stained blue with DAPI. The plot shows the percentage of positive GFAP astrocytes, MAP2 immature, and β-Tubulin mature neurons, generated by each cell type.</p

Additional file 2: of VEGF-121 plasma level as biomarker for response to anti-angiogenetic therapy in recurrent glioblastoma

Figure S1. Panels A and B. The panels show the significant correlation between plasma level of VEGF-121 and, respectively, OS (panel A; linear regression test: p = 0.0013; r2 = 0,9417), and PFS (panel B; linear regression test: p = 0.0001; r2 = 0,9913). Panels C and D. The panels show the significant correlation between differential plasma value of VEGF-121 (∆VEGF121: VEGF-121 level at baseline – VEGF-121 level after bevacizumab infusion) and, respectively, OS (panel C; linear regression test: p = 0.0008; r2 = 0,9731), and PFS (panel D; linear regression test: p = 0.0003; r2 = 0,9742). (TIF 1478 kb

Additional file 1: of VEGF-121 plasma level as biomarker for response to anti-angiogenetic therapy in recurrent glioblastoma

Statistical analysis. (DOCX 13 kb

Transcriptional and post-translational modulation of CDC25A, DUB3 and Wee1 during cell cycle in PTEN active cell lines.

<p>CDC25A mRNA is constantly produced throughout all phases while DUB3 expression increases in the G1/S phase and wee1 expression decreases during the phase M. CDC25A modulation occurs at post translational level with DUB3 inhibiting its ubiquitination and Wee1 controlling negatively the activity of Cyclin-B/Cdk1.</p

Patients characteristics.

<p> =  sample identifier, sex  =  patient sex, KPS (score)  =  Karnofsky Performance Status score; Sympt. (mo)  =  symptom duration in month; Surgery (type)  =  origin of tumor tissue from the patient brain (temporal/parietal/occipital/frontal); ki67-%  = % of cells expressing ki-67; p53 =  p53 positivity (less than 5% of nuclei); MGMT  =  MGMT promoter metylation; EGFR  =  EGFR positivity (moderate-to-strong signal on >% of cells); PFS  =  progression-free survival; OS  =  overall survival; PTEN activ  =  PTEN activation group according to clustering of phospoproteomics profiles. Characteristics of patients from which samples were collected. Legend: Sample </p

Transcripts fold change in differentiation.

<p>Green, red and black indicate transcripts that decrease, increase or maintain their concentration during induction respectively.</p