Career self: a longitudinal study with college students

O self de carreira constituí um subconjunto organizado do universo cognitivo de uma pessoa, responsável pelo carácter subjetivo que a mesma confere à carreira. Este estudo pretende avaliar mudanças no conteúdo do self de carreira de estudantes universitários, do início para o final do último ano de graduação. Para tal, recorreu-se a medidas repetidas dos índices da Grelha de Repertório da Carreira (Silva & Taveira, 2005; Silva, 2008). Na investigação, participaram 80 estudantes, dos quais 49 são mulheres (61,25%) e 31 são homens (38,75%), com idades entre os 21 e os 45 anos (M= 23,9, DP= 4,31). Os resultados indicam que, no final da licenciatura, os estudantes diminuem a distância como se constrõem em relação aos outros e mantêm uma construção positiva do self de carreira.Fundação para a Ciência e a Tecnologia (FCT

Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene Interaction in a case-control study

Abstract Background There is a growing awareness that interaction between multiple genes play an important role in the risk of common, complex multi-factorial diseases. Many common diseases are affected by certain genotype combinations (associated with some genes and their interactions). The identification and characterization of these susceptibility genes and gene-gene interaction have been limited by small sample size and large number of potential interactions between genes. Several methods have been proposed to detect gene-gene interaction in a case control study. The penalized logistic regression (PLR), a variant of logistic regression with L2 regularization, is a parametric approach to detect gene-gene interaction. On the other hand, the Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free approach to detect genotype combinations associated with disease risk. Methods We compared the power of MDR and PLR for detecting two-way and three-way interactions in a case-control study through extensive simulations. We generated several interaction models with different magnitudes of interaction effect. For each model, we simulated 100 datasets, each with 200 cases and 200 controls and 20 SNPs. We considered a wide variety of models such as models with just main effects, models with only interaction effects or models with both main and interaction effects. We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients. Results In this paper, we have studied the power of MDR and PLR for detecting gene-gene interaction in a case-control study through extensive simulation. We have compared their performances for different two-way and three-way interaction models. We have studied the effect of different allele frequencies on these methods. We have also implemented their performance on a real dataset. As expected, none of these methods were consistently better for all data scenarios, but, generally MDR outperformed PLR for more complex models. The ROC analysis on the real dataset suggests that MDR outperforms PLR in detecting gene-gene interaction on the real dataset. Conclusion As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of the methods to detect gene-gene interaction.</p

Differentially Expressed Gene Transcripts Using RNA Sequencing from the Blood of Immunosuppressed Kidney Allograft Recipients

<div><p>We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because it’s not limited to available probes, has increased sensitivity, and detects alternative and previously unknown transcripts. DEGs were determined in 32 adult kidney recipients, without clinical acute rejection (AR), treated with antibody induction, calcineurin inhibitor, mycophenolate, with and without steroids. Blood was obtained pre-transplant (baseline), week 1, months 3 and 6 post-transplant. PBMCs were isolated, RNA extracted and gene expression measured using RNAseq. Principal components (PCs) were computed using a surrogate variable approach. DEGs post-transplant were identified by controlling false discovery rate (FDR) at < 0.01 with at least a 2 fold change in expression from pre-transplant. The top 5 DEGs with higher levels of transcripts in blood at week 1 were <i>TOMM40L</i>, <i>TMEM205</i>, <i>OLFM4</i>, <i>MMP8</i>, and <i>OSBPL9</i> compared to baseline. The top 5 DEGs with lower levels at week 1 post-transplant were <i>IL7R</i>, <i>KLRC3</i>, <i>CD3E</i>, <i>CD3D</i>, and <i>KLRC2</i> (Striking Image) compared to baseline. The top pathways from genes with lower levels at 1 week post-transplant compared to baseline, were T cell receptor signaling and iCOS-iCOSL signaling while the top pathways from genes with higher levels than baseline were axonal guidance signaling and LXR/RXR activation. Gene expression signatures at month 3 were similar to week 1. DEGs at 6 months post-transplant create a different gene signature than week 1 or month 3 post-transplant. RNAseq analysis identified more DEGs with lower than higher levels in blood compared to baseline at week 1 and month 3. The number of DEGs decreased with time post-transplant. Further investigations to determine the specific lymphocyte(s) responsible for differential gene expression may be important in selecting and personalizing immune suppressant drugs and may lead to targeted therapies.</p></div

Top Differentially Expressed Genes Post-transplant Following Kidney Transplant compared to baseline with FDR < 0.01 and 2 or greater fold change.

<p>Top Differentially Expressed Genes Post-transplant Following Kidney Transplant compared to baseline with FDR < 0.01 and 2 or greater fold change.</p

Top altered pathways from blood following kidney transplant.

<p>Top canonical gene pathways altered at week 1, months 3 and 6 compared to baseline using Ingenuity Pathway Analysis of DEGs with false discovery rate (FDR) < 0.01 and 2 or greater fold change. The number of DEGs in the pathway is shown in parentheses.</p><p>DEG = Differentially Expressed Gene.</p><p>Baseline = prior to transplant.</p><p>Top altered pathways from blood following kidney transplant.</p

Expression of representative genes over time in kidney transplant recipients.

<p>Representative time series of fold expression changes relative to baseline (time 0) of some top genes with higher and lower level compared to baseline. Each line on each graph represents the expression of the particular gene in a separate kidney allograft recipient. Note that all patients do not have data all the time points. CD3E = CD3 Epsilon TCR complex; CD3D = CD3 Delta TCR complex; MMP8 = Matrix Metallopeptidase 8; IL7R = Interleukin 7 Receptor; OLFM4 = Olfactomedin 4; KLRC3 = Killer Cell Lectin-like Receptor subfamily C, member 3.</p

Alignment of paired end RNAseq reads with Reference Genome, n = 96.

<p>RNA sequencing reads were aligned to human genome (GRCh37/hg19 assembly) via Tophat2.</p><p>Alignment of paired end RNAseq reads with Reference Genome, n = 96.</p

Kidney Recipient and Donor Characteristics.

<p>Kidney Recipient and Donor Characteristics.</p