Preference for novelty was measured using a 2-min ITI in WT and P2X7R^−/− mice.

<p>Both strain exhibited a clear preferential exploration of the novelty (Arm effect, ***p<0.001).</p

IL-1β mRNA expression after the exposure to the Y maze in P2X₇R^−/− mice.

<p>IL-1β mRNA was measured by real-time PCR on WT and P2X₇R^−/− mice sacrificed either after free exploration (control group) or completion of behavioral testing (Y-maze group). All data are expressed as mean relative fold change±SEM (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006006#s2" target="_blank">Materials and Methods</a> for explanation). (A) IL-1β mRNA on hippocampus extracts from the 2-min ITI Y-maze group and their controls (left-hand panel) and the 30-min ITI Y-maze group and their controls (right-hand panel). (B) IL-1β mRNA on hypothalamus extracts from the 30-min ITI Y-maze group and their controls. Y maze exposure significantly increased the IL-1β mRNA expression in the hippocampus of WT mice but not of P2X₇R^−/− mice. Whatever the group considered (WT or P2X₇R^−/−), no induction of IL-1β mRNA expression was detected in hypothalamus after the Y maze experience. ***p<0.001, **p<0.01.</p

Recognition memory performance after 30 minutes of retention in P2X₇R^−/− mice.

<p>(A) Time spent (sec) in the novel or the familiar arm after a 30-min ITI. (B) Time spent (sec) exploring the novel or the familiar object after a 30-min ITI. Spatial recognition memory, but not object recognition memory, was impaired in P2X₇R^−/− mice after a 30-min ITI. ***p<0.001, **p<0.01,*p<0.05.</p

Spatial and object recognition memory.

<p>(A) Time spent (in sec) in the novel or the familiar arm after a 5-min ITI in 3-month-old (young) and 22-month-old (aged) mice fed with the control diet or the LCω3 diet for 2 months. (B) Time spent (in sec) in the novel or the familiar object after a 1-hr ITI in 3-month-old (young) and 22-month-old (aged) mice fed with the control diet or the LCω3 diet for 2 months. *** p<0.001, ** p<0.01, * p<0.05.</p

Brain fatty acid composition.

<p>dGLA: dihomo-gamma-linolenic acid (20:3 ω6); AA: arachidonic acid (20:4 ω6); EPA: eicosapentaenoic acid (20:5 ω3); DHA: docosahexaenoic acid; DMA: dimethyl acetal; LC ω6: long chain ω6 (20:2 ω6+20:3 ω6+20:4 ω6+22:4 ω6+22:5 ω6); LC ω3: long chain ω3 (20:5 ω3+22:5 ω3+22:6 ω3); NS: not significant.</p>***<p>p<0.001 as compared to aged control diet;</p>**<p>p<0.01 as compared to aged control diet;</p>*<p>p<0.05 as compared to aged control diet.</p

Morphometry of astrocytic processes in the hippocampus.

<p>(A) Confocal analysis of GFAP immunofluorescence was performed in the DG, the CA1 and the CA3 regions of the hippocampus of 3-month old (young) and 22-month old (aged) mice fed with the control diet or the LCω3 diet for 2 months using a 63X oil-immersion lens with a 3.10X zoom and IMARIS software. Images are representative of GFAP 3D immunofluorescence in the DG (upper panel), CA1 (central panel) and CA3 (lower panel). Scale bar = 8.5 µm. (A) Morphometric analysis of astrocytes in the DG, CA1 and CA3 regions of the hippocampus was performed using the “filament tracer” program/function. Data present means of primary and secondary processes lengths expressed in µm ± SEM. *** p<0.001, ** p<0.01, * p<0.05.</p

c-Fos expression in the DG, CA1 and CA3 regions of the hippocampus.

<p>c-Fos immunohistochemical analysis was performed in the hippocampus of 3-month-old (young) and 22-month-old (aged) mice fed with the control diet or the LCω3 diet for 2 months and sacrificed 90 min after the spatial recognition acquisition session. (A) Representative images of c-Fos immunohistochemistry in the DG (left panel), the CA1 (central panel) and the CA3 region (right panel) of the hippocampus. Scale bar = 100 µm. (B) Quantification of c-Fos-positive cells was performed in the DG, the CA1 and the CA3 regions of the hippocampus. Data are presented as mean ± SEM. ** p<0.01. (C) Correlation between the number of c-Fos positive cells induced by the Y-maze task in the DG (left panel), the CA1 (central panel) and the CA3 (right panel) and the spatial recognition score. Pearson's correlation coefficients (r) and corresponding significance (p) are displayed within each correlation window. The number of c-Fos positive cells in the DG and CA1, but not in the CA3 regions of the hippocampus was positively correlated to the spatial recognition score.</p

Table_1_Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT.xls

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.</p

DataSheet_1_Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT.docx

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.</p