L’utilisation des réseaux sociaux (Snapchat, WhatsApp et Instagram) et le cyberbullying

100% des jeunes possèdent un téléphone portable, 99% ont un ordinateur et 97% ont accès à Internet (Waller et al., 2016). Ces nouveaux moyens technologiques font partie de notre quotidien. Depuis l’apparition de ces réseaux, un nouveau mouvement est né : le cyberbullying. Ce harcèlement par Internet consiste à l’utilisation de technologies modernes de communication afin de nuire aux autres de manière délibérée et agressive. Quand les jeunes arrivent en classe, ils apportent avec eux l’entier de leur vécu quotidien, familial ou encore émotionnel. Les problèmes liés à l’utilisation massive de ces réseaux font partie de notre quotidien d’enseignant. Malheureusement, les études faites jusqu’au jour d’aujourd’hui portent en majeure partie sur les élèves entre 13 ans et plus. Mais qu’en est-il des jeunes âgés entre 9 et 12 ans ? Notre travail de recherche porte donc sur l’utilisation des réseaux sociaux (Snapchat, Instagram et WhatsApp) et le cyberbullying. Deux outils différents ont été utilisés lors de cette recherche : des questionnaires afin d’avoir des résultats quantitatifs et deux entretiens afin d’avoir un point de vue qualitatif. Nos résultats montrent que WhatsApp est le réseau social le plus utilisé, suivi d’Instagram en deuxième position et finalement de Snapchat. Les élèves considèrent le nombre de dangers et de conflits sur les réseaux comme très faibles. Ils avouent tout de même donner plus d’informations personnelles sur WhatsApp que sur les autres réseaux choisis dans l’étude. Concernant leur vision du contrôle des parents, ils l’estiment très faible. Cependant, il s’agit uniquement de leur avis, il serait intéressant de savoir la réalité des faits en interrogeant les parents. Les deux sujets interrogés savent définir le cyberbullying et connaissent les différents acteurs agissant au sein de cette forme de harcèlement. Ils sont également conscients des différents risques, conséquences ou sentiments que peut ressentir une cyber-victime mais n’abordent pas du tout ceux concernant le témoin ou le cyber-harceleur. En conclusion, notre recherche montre que les réseaux sociaux font partie intégrante du quotidien d’un grand nombre d’élèves. Il est donc essentiel que les enseignants s’interrogent sur les moyens de gérer les problèmes que ceux-ci peuvent amener en classe mais également les moyens de les éviter

Correlation matrix and variability of the methylation levels measured at 1,320 CpG sites across the 63 samples included in the study.

<p>(A) Each individual sample is indicated by a black line on the axes. The methylation levels in the samples taken at remission during induction therapy at day 29 and during consolidation therapy at days 50 and 106 are highly correlated with the methylation levels in the non-leukemic samples (median Pearson's correlation coefficient (<i>R</i>) = 0.96), while the diagnostic ALL samples are less similar both to each other and to the samples taken after treatment, and to the non-leukemic samples (median <i>R</i> = 0.83). The scale for the correlation coefficients is shown to the right of the matrix. The red color indicates higher correlation (greater similarity), while the light yellow indicates less correlation (less similarity). (B) Histograms of the standard deviations (SD) for the methylation levels measured for 1,320 CpG sites across 20 ALL samples (blue) and across the combined 33 remission samples and 13 non-leukemic controls (red). SD bins are shown on the horizontal axis. The vertical bars show the proportion of observations in each SD bin. The CpG sites show greater variability in the ALL samples than in the remission samples and non-leukemic controls (Wilcoxon Rank-Sum P<0.001).</p

Differential methylation in ALL cells.

<p>(A) Heatmap of the methylation profiles of the 28 CpG sites that are differentially methylated between the diagnostic ALL samples, bone marrow cells at remission and non-leukemic bone marrow cells. The ALL samples (orange) and bone marrow cells during remission (blue) form two distinct groups. Thirteen bone marrow cell samples from non-leukemic controls (purple) cluster among the samples collected during remission. The scale for the methylation β-values is shown below the heatmap. The elongated heights of the dendrogram branches between the ALL samples compared to the normal samples illustrate the increased variability in the ALL samples for the 28 CpG sites. Graphs showing the differences in methylation level between CpG sites in the (B) <i>WDR35</i> and (C) <i>FXYD2</i> genes at the time of diagnosis (left vertical axis) and during remission (right vertical axis). The data points for each paired sample are connected with a red line for B-cell precursor (BCP) samples and with a blue line for T-ALL samples. The corresponding CpG methylation levels in 13 non-leukemic control samples are shown as black horizontal lines to the right of the graphs. The CpG site at chr2:20,052,748 in the <i>WDR35</i> gene (B) was hypermethylated in diagnostic ALL samples and hypomethylated at remission and in non-leukemic controls, while the CpG site at chr11:7,203,745 in the <i>FXYD2</i> gene (C) displayed the opposite pattern. The BCP and T-ALL samples display the same pattern of methylation difference in these two genes.</p

Clinical information for the 20 patients with acute lymphoblastic leukemia and 13 controls included in the study.

<p>BCP indicates B-cell precursor ALL; T-ALL, T-cell ALL; HeH, high hyperdiploidy; amp(21), amplification of chr 21; HR, high risk; SR, standard risk; IR, intermediate risk; NA, not available.</p>a<p>White blood cell count at diagnosis (10⁹/L).</p>b<p>The NOPHO ALL 2000 protocol was used.</p>c<p>DNA from was available from bone marrow taken from the patients on day 29,50, and/or 106 after the initiation of therapy, all patients were in morphological remission with less than 5% leukemic blasts.</p

Correlation between the methylation levels (β-values) of two CpG sites located in the <i>COL6A2</i>, <i>EYA4</i>, <i>FXYD2</i> and <i>MYO3A</i> genes.

<p>The Pearson's correlation coefficients (<i>R</i>) across the 20 acute lymphoblastic leukemia (ALL) samples taken at ALL diagnosis (green) and the 20 matched bone marrow samples taken at remission (blue) for the four genes are shown in panels A–D. The positions of the CpG sites for which the β-values are plotted are indicated on the axes in each panel (Human Genome Build 36). The inter-individual variation between the pairs of CpG sites in the remission cells is consistently lower than between the ALL cells, which speaks against the variation in ALL cells arising because of methodological factors.</p

Multi-modal single cell sequencing of B cells in primary Sjögren’s Syndrome (processed VDJ data).

Meta data record for processed VDJ data from the publication "Multi-modal single cell sequencing of B cells in primary Sjögren’s Syndrome". Abstract: Primary Sjögren’s syndrome (pSS) is an autoimmune disease characterized by lymphocytic infiltration in the salivary and lacrimal glands, B cell activation, SSA/SSB autoantibodies and an increased risk of B cell lymphoma. By generating sorted B cell single-cell gene expression and BCR libraries from 24 pSS patients stratified by SSA/SSB antibodies and four healthy controls, we defined 16 B cell subtypes. Interferon response genes were upregulated in pSS across all B cell subtypes, with the highest levels in pSS with both SSAB antibodies. The SSAB group showed a higher proportion of naïve B cells and lower proportion of memory B cells compared with controls. Memory B cells from SSAB patients were not class switched and expressed unmutated VDJ sequences. IGHV1-69 repertoire frequencies were higher in pSS patients than controls and 1287 clonotypes were unique for pSS. The present study describes molecular differences which may enable stratification of pSS patients at improved resolution. Repository content: 10X Genomics 5' VDJ (v1.1) BCR data from B cells from  Primary Sjögren's Syndrome (pSS) patients and healthy controls. Output from cellranger (5.0.1) for all samples where targeted VDJ libraries were successfully created and sequenced (23/24 samples). Files to be used as input to, for instance,  the immcantation workflow or the Bioconductor R package scRepertoire. Data is available upon reasonable request. Processed data files included for each sample: *_filtered_contig_annotations.csv *_filtered_contig.fasta Command used to generate the files: cellranger vdj \   --id="${sample}B" \   --sample=${bcrsamples} \   --fastqs=${fqdir} \   --reference="$CELLRANGER_VDJ_DATA/refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0" \   --localcores=16 \   --localmem=112 List of files: C001_B_filtered_contig.fasta 3.1M C001_B_filtered_contig_annotations.csv 1.0M C002_B_filtered_contig.fasta 9.8M C002_B_filtered_contig_annotations.csv 3.2M C003_B_filtered_contig.fasta 9.8M C003_B_filtered_contig_annotations.csv 3.2M C004_B_filtered_contig.fasta 12M C004_B_filtered_contig_annotations.csv 3.8M P001_B_filtered_contig.fasta 7.4M P001_B_filtered_contig_annotations.csv 2.5M P002_B_filtered_contig.fasta 9.1M P002_B_filtered_contig_annotations.csv 3.1M P003_B_filtered_contig.fasta 3.7M P003_B_filtered_contig_annotations.csv 1.2M P004_B_filtered_contig.fasta 11M P004_B_filtered_contig_annotations.csv 3.6M P005_B_filtered_contig.fasta 10M P005_B_filtered_contig_annotations.csv 3.4M P006_B_filtered_contig.fasta 13M P006_B_filtered_contig_annotations.csv 4.4M P007a_B_filtered_contig.fasta 4.9M P007a_B_filtered_contig_annotations.csv 1.6M P007b_B_filtered_contig.fasta 8.0M P007b_B_filtered_contig_annotations.csv 2.7M P008a_B_filtered_contig.fasta 8.8M P008a_B_filtered_contig_annotations.csv 2.8M P008b_B_filtered_contig.fasta 12M P008b_B_filtered_contig_annotations.csv 3.8M P009_B_filtered_contig.fasta 7.5M P009_B_filtered_contig_annotations.csv 2.5M P010_B_filtered_contig.fasta 15M P010_B_filtered_contig_annotations.csv 5.0M P011_B_filtered_contig.fasta 9.7M P011_B_filtered_contig_annotations.csv 3.3M P012_B_filtered_contig.fasta 12M P012_B_filtered_contig_annotations.csv 4.1M P013_B_filtered_contig.fasta 9.1M P013_B_filtered_contig_annotations.csv 3.0M P014_B_filtered_contig.fasta 11M P014_B_filtered_contig_annotations.csv 3.8M P015_B_filtered_contig.fasta 11M P015_B_filtered_contig_annotations.csv 3.7M P016_B_filtered_contig.fasta 13M P016_B_filtered_contig_annotations.csv 4.2M P017_B_filtered_contig.fasta 7.2M P017_B_filtered_contig_annotations.csv 2.4M P018_B_filtered_contig.fasta 15M P018_B_filtered_contig_annotations.csv 4.9M P019_B_filtered_contig.fasta 20M P019_B_filtered_contig_annotations.csv 6.5M P020_B_filtered_contig.fasta 10M P020_B_filtered_contig_annotations.csv 3.5M P021_B_filtered_contig.fasta 7.8M P021_B_filtered_contig_annotations.csv 2.6M P022_B_filtered_contig.fasta 9.4M P022_B_filtered_contig_annotations.csv 3.1M P023_B_filtered_contig.fasta 7.2M P023_B_filtered_contig_annotations.csv 2.4M _CHECKSUMS.txt 3.8K _README.txt 637B</p

Volcano plot of the AI data from 105 heterozygous cSNPs in 13 cell lines

<p><b>Copyright information:</b></p><p>Taken from "Allelic imbalance in gene expression as a guide to -acting regulatory single nucleotide polymorphisms in cancer cells"</p><p></p><p>Nucleic Acids Research 2007;35(5):e34-e34.</p><p>Published online 31 Jan 2007</p><p>PMCID:PMC1865061.</p><p>© 2007 The Author(s).</p> AI for each SNP was determined by calculating the fluorescence signal ratio between the two alleles (/) in RNA (cDNA) and genomic DNA for each heterozygous SNP. The level of AI obtained by dividing the signal ratio in RNA by the corresponding ratio in DNA is plotted on the horizontal axis. The -value for the difference between allelic ratios in RNA and DNA based on five replicate assays is plotted on the vertical axis. Spots above the horizontal dashed line represent the SNPs showing AI at a -value < 0.0001 that were selected for further analysis

Recovery of genes and SNPs at the different stages of our process for screening for allelic imbalance

<p><b>Copyright information:</b></p><p>Taken from "Allelic imbalance in gene expression as a guide to -acting regulatory single nucleotide polymorphisms in cancer cells"</p><p></p><p>Nucleic Acids Research 2007;35(5):e34-e34.</p><p>Published online 31 Jan 2007</p><p>PMCID:PMC1865061.</p><p>© 2007 The Author(s).</p

Electrophoretic mobility shift assay images for the SNP alleles of the and genes

<p><b>Copyright information:</b></p><p>Taken from "Allelic imbalance in gene expression as a guide to -acting regulatory single nucleotide polymorphisms in cancer cells"</p><p></p><p>Nucleic Acids Research 2007;35(5):e34-e34.</p><p>Published online 31 Jan 2007</p><p>PMCID:PMC1865061.</p><p>© 2007 The Author(s).</p> Three lanes are shown for each SNP allele. From left to right these are: a control reaction with labeled probe only, a reaction containing both labeled probe and nuclear extract and a reaction where an unlabeled probe is added in excess as a competitor, in addition to the labeled probe and nuclear extract. For the and genes, the two lanes are shown: a reaction with labeled probe and nuclear extract and a reaction where the unlabeled competitor probe is added. The sequences of the allele-specific EMSA probes are given in

Illustration of a region with a SNP from genome wide association studies (GWAS) which is associated with ASE of lncRNAs.

<p>The tracks are from top to bottom in each panel: Horizontal red bars represent lncRNA transcript windows (with genomic coordinates) used for determination of ASE levels; grey lines show p-values for the association of GWAS SNPs with ASE levels in the transcript window; a grey line overlayed with a red dotted line indicates that a <i>cis</i>-rSNP overlaps with the reported SNP in the GWAS catalog; red vertical lines are median ASE-levels for each SNP.</p