Recovery, empowerment and rehabilitation: Do inpatient psychiatric rehabilitation services empower the individual?

Perceptions of the course and outcome from serious mental illness have changed over the last century and, more recently, the concept of recovery has gained prominence in this field. This paper reviews recent literature on recovery from serious mental illness and discusses both the meaning of the concept and the key contributing factors. Research suggests that empowerment is one of the most salient factors contributing to recovery and the relationship between recovery and empowerment is examined. Most research in the area of empowerment has, to date, focused on community settings and this paper considers the relevance of these ideas in other mental health settings. The relationship between empowerment, recovery and mental health services is discussed. Finally, conclusions are drawn and recommendations for further research are outlined

Konseptual Framework Untuk Pengukuran Kualitas Website Pada Sistem Informasi Akademik Dengan Metode Gqm

Konseptual framework yang diusulkan dalam penelitian ini berupa model konseptual yang merupakan gambaran proses pengukuran kualitas beserta tahapan yang dilakukan dalam pengukuran kualitas website sistem informasi akademik. Model konseptual yang sudah ada selama ini masih bersifat luas dan tidak spesifik pada domain tertentu. Terdapat banyak website yang dibangun oleh web developer, namun masih sedikit yang dibangun sesuai dengan kebutuhan pengguna. Salah satu website online dibidang pendidikan adalah sistem informasi akademik. Sistem informasi akademik merupakan layanan website oleh universitas dalam menyediakan informasi dan pengelolaan data-data akademik. Karakteristik dari sistem informasi akademik adalah academic content, periodic acccessibility, level of user authority, precission dan accurateness. Beberapa dari karakteristik tersebut kemudian dipetakan kedalam faktor-faktor kualitas yang diadopsi dari berbagai model, seperti ISO-9126, Website quality Model, dan academic website quality model. akademik. Hasil pemetaan tersebut memperoleh 5 faktor kualitas yang diusulkan untuk melakukan pengukuran kualitas, yaitu USAbility, functionality, content, efficiency dan reliability. Kelima faktor kualitas ini dijadikan sebagai tujuan pengukuran. Metode GQM digunakan untuk memperoleh metric internal agar menghasilkan pengukuran yang objektif dan kuantitatif. Metric-metric yang dihasilkan dari metode GQM divalidasi dengan menggunakan validasi empiris. Metric internal produk diterapkan dalam studi kasus sistem informasi akademik berbasis web universitas di Pekanbaru. Hasil validasi dari framework pengukuran yang dibangun adalah memiliki nilai baik pada faktor kualitas functionality, content dan reliability, dan nilai cukup pada faktor kualitas USAbility dan efficiency

Table S1

List of genes coexpressed with PCDH gene clusters. Microarray analysis results stored in public databases were analyzed for genes showing coexpression with the different PCDH gene clusters (XLSX 13 kb

Table S3

Total and damaging missense variation in pathways. The pathways that had at least 500 hits for total missense variants and at least a 1 % ratio of damaging missense variant hits to total missense variant hits are listed (DOCX 16 kb

Table S2

Functional annotation analysis of genes coexpressed with PCDH gene clusters. The genes that coexpress with PCDH gene clusters were analyzed with regard to annotated function using DAVID (XLSX 21 kb

RNAi screens validate a role for the novel RLR candidates in RIG-I-mediated IFNβ induction.

<p>(<b>A</b>) Flow chart of the RNAi validation screens. 187 candidate RLR genes were screened for RIG-I pathway activity in three different RNAi screens. In screens 1 and 2, HeLa cells stably expressing an IFNβ promoter-controlled firefly luciferase (Fluc) reporter were stimulated with a 5’-ppp-containing RIG-I RNA ligand. The 57 hits (15 up, 42 down) with the largest effect on IFNβ induction upon siRNA knockdown in screen 1 (stringent Z-score <-2 or >2) were tested again in screen 2 with a different set of siRNAs. The 19 top hits from screen 2 were then picked for screen 3, which is similar to the first two screens except that it measures <i>IFNβ</i> mRNA levels using quantitative real-time qRT-PCR. (<b>B</b>) Correlation between the negative control-based robust Z-scores of RNAi screens 1 and 2. The 57 top hits with Z-scores <-2 or >2 in screen 1 were tested again in screen 2 (purple data points). N.T., non-transfected; SCR, scrambled. (<b>C</b>) Overview of the 19 novel RIG-I pathway genes with the largest effects on IFNβ induction in screens 1 and 2 (Z-score <-2 in both screens). Black data points correspond to genes whose knockdown also causes a reduction in <i>IFNβ</i> mRNA levels in screen 3. (<b>D</b>) RNAi screen 3. 13 of the 19 top hits from screens 1 and 2 also reduce RIG-I-mediated <i>IFNβ</i> mRNA production (black bars). Experiments were performed in triplicate (n = 3). Bars (mean±SEM) display the fold induction of <i>IFNβ</i> mRNA (corrected for actin mRNA levels) compared to the mock-treated control. Statistical significance was assessed by one-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparison test, comparing the values for each of the 19 test genes to the combined negative control conditions (scrambled and LGP2, red bars). ** <i>P</i> < 0.01; *** <i>P</i> < 0.001. (<b>E</b>) Correlation between the <i>in silico</i> integrated RLR score and the probability of experimental confirmation in RNAi screen 1. The dark purple line represents all 94 hits with Z-score <-1.25 or >1.25; the light purple line represents the top 57 hits with Z-score <-2 or >2. The 187 experimentally tested genes were rank-ordered based on the RLR score and precision was calculated sequentially as the fraction of validated hits among all tested genes having a certain RLR score or higher.</p

Human and viral protein interaction networks connecting the known RLR pathway with the newly identified RIG-I factors DDX17 and SNW1.

<p>Human proteins are represented by circles, viral proteins by rounded rectangles (purple nodes). Green nodes represent known components of the RLR pathway. Orange nodes (DDX17 and SNW1) are novel RIG-I pathway components discovered in our study, which are connected to the RLR network through interactions with the green nodes. Edges between human proteins represent physical interactions (both low- and high-throughput) obtained from BioGRID Release 3.3 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.ref054" target="_blank">54</a>]. Interactions between human and viral proteins were obtained from the PHISTO database (29 Sep. 2014) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.ref028" target="_blank">28</a>]. See <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.s001" target="_blank">S1 Fig</a></b> for a more complete representation of the RLR pathway containing the curated set of 49 known RLR genes. LaCV, La Crosse virus; EBV, Epstein-Barr virus; SFSV, Sandfly fever Sicilian virus; PRRSV, Porcine reproductive and respiratory syndrome virus; HPV, Human papillomavirus.</p

Overlap between innate (antiviral) response data sets and the top 354 RLR predictions excluding known RLR genes.

<p>^a These PPIs were not part of the RLR interaction network used for the RLR predictions (i.e. for the ‘RLR pathway PPI’ signature)</p><p>^b These interactions were not used to determine the virus-interacting human proteins used for the RLR predictions (i.e. for the ‘PPI with viruses’ signature)</p><p>Overlap between innate (antiviral) response data sets and the top 354 RLR predictions excluding known RLR genes.</p

Bayesian integration of ten molecular signatures of RLR pathway components from genomics data.

<p>(<b>A</b>) Distributions of the 49 known RLR pathway components (RLR genes, green) and 5,818 genes unlikely to be part of the pathway (non-RLR genes, red) across the 10 molecular signature data sets we identified as predictive of the RLR system (see also <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#pcbi.1004553.t001" target="_blank">Table 1</a></b>). Data sets were binned into discrete intervals and fractions of (non-)RLR genes add up to one. Arrows indicate the behavior of RIG-I across the data. The top five signatures describe the relationship of RLR signaling with viruses; the bottom five describe properties of the pathway itself. (<b>B</b>) Boxplots of the genome-wide integrated RLR score (Bayesian posterior probability score). Genes were grouped into one of five classes: known RLR genes (green, see [A]), components of other PRR signaling pathways (‘TLR, CLR, NLR, cytDNA’; purple), genes functioning in other aspects of the innate immune response (‘innate immunity’; blue), and non-RLR genes (red, see [A]). The remaining genes are classified as ‘other’ (gray). (<b>C</b>) The 50 genes with the highest RLR scores. Representative RLR and other innate antiviral response genes are indicated. The pie chart shows the occurrences of the different gene classes in the top 354 RLR ranks. (<b>D</b>) Receiver operating characteristic (ROC) curve illustrating the performance of the integrated RLR score (solid black line) and the individual molecular signatures (black dots) for predicting known RLR versus non-RLR genes. Sensitivity and specificity were calculated at various score thresholds (for the RLR score), or at specific thresholds that include all bins with positive likelihood ratio scores (for the individual data sets; see (A)). The asterisk denotes the sensitivity and specificity corresponding to a false discovery rate (FDR) of 57% (top 354 genes). Note that, to avoid circularity, the predictive ability of the co-expression, protein domain and RLR pathway PPI data sets in (A) and (D) was assessed using the set of TLR, CLR, NLR, cytDNA genes instead of the RLR genes (see <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004553#sec026" target="_blank">Methods</a></b>).</p

Validations of our predicted RLR candidates by independent studies.

<p>^a '+': positive regulator (expected decrease in IFNβ induction upon knockdown). '-': negative regulator (expected increase in IFNβ induction upon knockdown).</p><p>^b Annotated cells (‘+’, ‘-’, ‘0’) indicate 11 candidate RLR genes that were tested in RNAi screen 1. ‘+’: down-hits from RNAi screen 1 (decreased RIG-I-mediated IFNβ induction upon knockdown, Z-score <-1.25). ‘-’: up-hits from RNAi screen 1 (increased RIG-I-mediated IFNβ induction upon knockdown, Z-score >1.25). ‘0’: no hit in RNAi screen 1, or inconsistent effect across RNAi screens 1 and 2 (<i>CSNK2A1</i> and <i>CSNK2A2</i>, ^c).</p><p>Validations of our predicted RLR candidates by independent studies.</p