Why make the effort? Exploring Recovery College Engagement

Purpose - Whilst there is growing evidence to suggest that the Recovery College (RC) environment supports students towards their mental health recovery (Meddings et al., 2015b), students’ initial motivations for engagement, alongside factors that may hinder or support attendance, have yet to be exclusively explored. Design - All new RC students were invited to take part in a semi-structured interview three months following their enrolment. Four participants completed an interview which were later analysed using Thematic Analysis. Findings - Four themes emerged within analysis: Making the effort; Being “too unwell”; Friendly Environment and Glad I came. These are discussed alongside the literature, and it is proposed that whilst there is a substantial struggle involved in engagement with a RC, likely related to mental health and social factors, the RC environment, peer support and support of the tutors helps students to overcome the impact of this. Research limitations / implications - Due to the small sample size and exploratory stance of this study, additional research into the complexities around engagement with RCs is strongly recommended. Only students who had attended at least one RC course chose to participate in this study, therefore an under-researched population of non-attendees may provide a valuable contribution to further understanding. Originality / value - This is one of the first studies to qualitatively explore factors which may support, or hinder, initial and ongoing engagement with a RC. It is proposed that a greater understanding of these important issues could be used to increase RC accessibility and inclusion

Additional file 1: of Dietary supplementation with olive mill wastewaters induces modifications on chicken jejunum epithelial cell transcriptome and modulates jejunum morphology

Table S1. Significant differentially expressed genes (DEG), up regulated (logFC> 1) and down regulated (logFC<− 1) in OMWW group. Information contained in the table are significant Gene ID, GalgalEnsembl gene id (e.g. data ENSGALG00000041621); Transcript ID, GalgalEnsembl transcript id (e.g. data ENSGALT00000059872); Gene name, associated name of genes (e.g. LY6E); Gene description, description of gene name (e.g. Lymphocyte Antigen 6 Family Member); logFC, log Fold Change (e.g. 4,44E + 00); logCPM, log2 counts-per-million (e.g. 8,92E + 00); PValue, p-value evaluated in multiple testing (e.g. 3,96E-08); q-value, adjusted p-value (e.g. 2,41E-05). (XLSX 91 kb

Additional file 5: of Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils

Table S5. Liver Female_Module36. Significant GO terms by ClueGO enrichment analysis (FDR < 0.05). Information contained in the table are significant GO-ID, GO term, accession number (e.g. GO:0036064); GOTerm, name of GO term (e.g. ciliary basal body); Ontology source, ontology vocabularies (e.g. GO_CellularComponent); FDR, False Discovery Rate after Benjamini-Hochberg correction (e.g. 41,0E-3); % Associated Genes, the percentage of input genes found per term (e.g. 7,50); Nr. Genes, number of input genes found per term (e.g 3,00); Associated Genes Found, associated name of genes found per term (e.g. [CENPJ, KIAA0586, POC1A]). (XLS 34 kb

Additional file 4: of Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils

Table S4. Enrichment analysis by ClueGo using highly connectivity genes in Female_Module16 module (FDR < 0.05). Information contained in the table are significant GO-ID, GO term, accession number (e.g. GO:0070085); GOTerm, name of GO term (e.g glycosylation); Ontology source, ontology vocabularies or Kyoto Encyclopaedia of Genes and Genomes (e.g. GO_BiologicalProcess or KEGG); FDR, False Discovery Rate after Benjamini-Hochberg correction (e.g. 16,0E-3); % Associated Genes, the percentage of input genes found per term (e.g. 4,65); Nr. Genes, number of input genes found per term (e.g. 4,00); Associated Genes Found, associated name of genes found per term (e.g. [B4GALT7, DDOST, DHDDS, DPAGT1]). (XLS 31 kb

Additional file 3: of Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils

Table S3. Liver Female_Module16. Significant GO terms by ClueGO enrichment analysis (FDR < 0.05). Information contained in the table are significant GO-ID, GO term, accession number (e.g. GO:0042730); GOTerm, name of GO term (e.g. fibrinolysis); Ontology source, ontology vocabularies (e.g. GO_BiologicalProcess); FDR, False Discovery Rate after Benjamini-Hochberg correction (e.g. 18,0E-3); % Associated Genes, percentage of input genes found per term (e.g. 35,71); Nr. Genes, number of input genes found per term (e.g. 5,00); Associated Genes Found, associated name of genes found per term (e.g. [FGA, FGB, FGG, KLKB1, TMPRSS6]). (XLS 32 kb

Additional file 3: of Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils

Table S3. Liver Female_Module16. Significant GO terms by ClueGO enrichment analysis (FDR < 0.05). Information contained in the table are significant GO-ID, GO term, accession number (e.g. GO:0042730); GOTerm, name of GO term (e.g. fibrinolysis); Ontology source, ontology vocabularies (e.g. GO_BiologicalProcess); FDR, False Discovery Rate after Benjamini-Hochberg correction (e.g. 18,0E-3); % Associated Genes, percentage of input genes found per term (e.g. 35,71); Nr. Genes, number of input genes found per term (e.g. 5,00); Associated Genes Found, associated name of genes found per term (e.g. [FGA, FGB, FGG, KLKB1, TMPRSS6]). (XLS 32 kb

Additional file 7: of Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils

Table S7. Sheet 1 (ClueGO Results): Liver ConsM_K. Significant GO terms and KEGG pathway by ClueGO enrichment analysis (FDR < 0.05). Information contained in the table are significant GO-ID, GO term, accession number (e.g. GO:0007599); GOTerm, name of GO term (e.g. hemostasis); Ontology source, ontology vocabularies or Kyoto Encyclopaedia of Genes and Genomes (e.g. GO_BiologicalProcess or KEGG); FDR, False Discovery Rate after Benjamini-Hochberg correction (e.g. 39,0E-3); % Associated Genes, the percentage of input genes found per term (e.g. 4,35); Nr. Genes, number of input genes found per term (e.g. 3,00); Associated Genes Found, associated name of genes found per term (e.g [FGA, FGB, FGG]). Sheet 2 (Liver_consensus_module-trait): Liver consensus module-trait relationships. Information contained in the table are ConsMs, conserved modules in females and in males identified in Consensus analysis (e.g. consM_A); Cor, correlation value to EO diet (e.g. − 0.55) for females and males; p-value, corresponding p-value (e.g. 0.2). (XLS 55 kb

Annotation results according to cufflinks intronic and intergenic fragments output.

<p>The transcripts were search against the non redundant nucleotide database (NT), non redundant protein database (NR) and a database of non coding sequences (NONCODE).</p

Venn diagram showing the number of splice sites identified in the T1 and T2 samples.

<p>A) Splicing sites confirmed by previously reported annotation of horse genes. B) Novel splicing sites.</p

(A) Basal (T1) and race (T2) sample reads map to different genomic regions.

<p>The majority of the reads map to known genes (CDS, 3′ UTR and 5′ UTR), while a large fraction maps to non-coding regions (introns, intergenic regions, and the 1-kb regions up- and downstream of genes). Comparison between T1 and T2 show a transcriptional shift from coding to non-coding predicted regions. (B) Expression density was calculated as number of reads normalized by the lengths of each genomic region. (C) Fraction of bases covered in the different genomic regions. (D) Fraction of reads that map to the sense (light) and antisense (dark) strands in each genomic region. In the intergenic region, the fraction was calculated using the number of reads from the plus and minus strands.</p