Journal: Scientific Reports

Article Title: Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (horizon 2030) scenarios

doi: 10.1038/s41598-018-24965-0

Figure Lengend Snippet: ( a ) Mean climatological surface chlorophyll- a (Chla) concentration (mg m −3 ) in the NW Mediterranean (see text for definition) from MODIS data (2003–2015). ( b ) Mean surface Chla (mg m −3 ) spatial distribution for the blooming period (vertical bars in panel a) from MODIS data (2003–2015). Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Article Snippet: Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Techniques: Concentration Assay, Software

Journal: Scientific Reports

Article Title: Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (horizon 2030) scenarios

doi: 10.1038/s41598-018-24965-0

Figure Lengend Snippet: ( a ) Climatological seasonal mixed layer depth (m) on the NW Mediterranean (see text for definition) computed by the model for the 2000–2015 period. Black circles mean daily value, gray lines standard deviation range. ( b ) Mean mixed layer depth (m) during the convective period (from day 18 to day 110) during the simulation period (2000–2015). ( c ) Accumulated number of days during the simulation period (2000–2015) in which the mixed layer depth exceed the 1000 m threshold. ( d ) Scatter plot of the mean mixed layer depth distribution (panel b) versus the mean MODIS surface chlorophyll map during the blooming period (Fig. ). Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Article Snippet: Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Techniques: Standard Deviation, Software

Journal: Scientific Reports

Article Title: Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (horizon 2030) scenarios

doi: 10.1038/s41598-018-24965-0

Figure Lengend Snippet: ( a ) Mean surface water circulation for the hindcast simulation (2010–2015). Background color indicate the mean velocity (m s-1) while the black arrows show the direction. The magenta line indicate the region occupied by the Northern Current (see text for details). ( b ) Same as a) but for the ENSEMBLE simulation. ( c ) Mean kinetic energy of the Northern Current (the magenta area in panel a) for the hindcast (black line) and the four different scenarios (colored lines). ( d ) Relative anomaly (in %) of the mean kinetic energy of the North Current in the different scenarios with respect to the hindcast simulation. Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Article Snippet: Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Techniques: Northern Blot, Software

Journal: Scientific Reports

Article Title: Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (horizon 2030) scenarios

doi: 10.1038/s41598-018-24965-0

Figure Lengend Snippet: ( a ) Climatological sea surface temperature (SST) in the NW Mediterranean for the hindcast (thick gray line), for the ENSEMBLE simulation (thick black line) and for the individual members of the ENSEMBLE (thin black lines). ( b ) The same as a) but for sea surface salinity (SSS). ( c ) The same as (a) but for surface density. ( d ) Scatter plot of the anomalies (ENSEMBLE – hindcast) of SST versus surface density. The linear regression is shown as a gray line. ( e ) Mean surface density anomaly (ENSEMBLE – hindcast) map. Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Article Snippet: Maps were created by the authors using MATLAB software vR2014b ( https://it.mathworks.com/products/matlab/matlab-graphics.html ).

Techniques: Software

Journal: eLife

Article Title: New insights on the modeling of the molecular mechanisms underlying neural maps alignment in the midbrain

doi: 10.7554/eLife.59754

Figure Lengend Snippet:

Article Snippet: The 3-step map alignment model in MATLAB ( ) was used to simulate the formation of both the RC and CC map in the presence of an oscillatory Epha gradient in the retina.

Techniques: SYBR Green Assay, Software

Journal: Scientific Reports

Article Title: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming

doi: 10.1038/s41598-019-49965-6

Figure Lengend Snippet: Variability of the winter (DJF) mean sea ice cover in the Barents Sea region during the ESO period. ( a ) Climatological sea ice edge (15% SIC contour, black line) on the background of the climatological mean SST (colours) obtained by averaging data over all ESO winters. The arrows with acronyms depict the West Spitsbergen Current (WSC) and the East Greenland Current (EGC). ( b ) (thin contours and colour shading) Undetrended SIC anomalies regressed onto the principal component time series (PC1 SIC−BS ) of the leading EOF mode of the SIC variability in the Barents Sea region (BS box) and (thick lines) the mean 15% SIC contour in winters 2003/04 (black line) and 2017/18 (red line). The thin blue (resp. red) contours represent negative (resp. positive) anomalies. The contour interval (CI) is 5% per unit PC1 SIC−BS . The zero contour is omitted. Aquamarine (resp. pink) shading marks negative (resp. positive) anomalies statistically significant at the 95% confidence level. ( c ) (solid blue curve) Time series of the sea ice area in the Barents Sea region (SIA BS index) and (dashed blue line) its continuous piecewise linear trend with the breakpoint in winter 2003/04. The blue circle, magenta square and magenta triangle mark the onset time (OT) of the sea ice decline, and the first (CP1) and second (CP2) regime change points, respectively (see Methods). ( d ) (solid curves) Standardised time series of SIA BS (blue curve) and PC1 SIC−BS (red curve), and their OT points (circles) and continuous piecewise linear trends with the breakpoint in winter 2003/04 (dashed lines). In ( a , b ) the maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ). In ( c , d ) each year on the horizontal axis includes January of the DJF season.

Article Snippet: The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Techniques: Generated

Journal: Scientific Reports

Article Title: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming

doi: 10.1038/s41598-019-49965-6

Figure Lengend Snippet: Relationship between the winter mean sea surface temperature and sea ice concentration in the Barents/Nordic Seas region during the ESO period. ( a ) (thin contours and colour shading) Difference in the mean SST between the last three (LAST3) winters (2015/16–2017/18) and the winters of the EARLY period (1981/82–2003/04), and (thick lines) the 15% contour of the mean wintertime SIC in the LAST3 winters (blue line) and the EARLY period (black line). ( b ) (thin contours and colour shading) Undetrended SST anomalies regressed onto the principal component time series (PC1 SST−BS ) of the leading EOF mode of the SST variability in the Barents Sea region (BS box) in the ESO period and (thick lines) the mean 15% SIC contour in the EARLY period (black line) and the LATE period (winters 2003/04–2017/18, blue line). ( c ) (solid curves) Standardised time series of (red curve) the average SST over the southern Barents Sea region (sBS box in a ) and (blue curve) sea ice area in the Barents Sea region (SIA BS index), and their OT points (circles) and continuous piecewise linear trends with the breakpoint in winter 2003/04 (dashed lines). ( d ) (solid curves) Standardised time series of (red curve) PC1 SST−BS and (blue curve) PC1 SIC−BS , and their OT points (circles) and continuous piecewise linear trends with the breakpoint in winter 2003/04 (dashed lines). In ( a , b ) the thin contour and shading colours are explained in the caption to Fig. . The CI is 0.2 °C and 0.1 °C per unit PC1 SST−BS , respectively. The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ). In ( c , d ) each year on the horizontal axis includes January of the DJF season.

Article Snippet: The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Techniques: Concentration Assay, Generated

Journal: Scientific Reports

Article Title: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming

doi: 10.1038/s41598-019-49965-6

Figure Lengend Snippet: Leading modes of the variability in the winter mean Arctic sea ice concentration and surface air temperature during the ESO period. ( a ) Undetrended SIC anomalies regressed onto the principal component time series (PC1 SIC−A40 ) of the leading EOF mode of the SIC variability north of 40°N. ( b ) Undetrended SAT anomalies regressed onto the principal component time series (PC1 SAT−A70 ) of the leading EOF mode of the SAT variability north of 70°N. ( c ) (solid curves) Standardised time series of (blue curve) PC1 SIC−A40 and (red curve) PC1 of the SIC variability in the Barents Sea region (BS box in a ), their OT points (circles)and continuous piecewise linear trends with the breakpoint in winter 2003/04 (dashed lines), and (for PC1 SIC−A40 only) the continuous piecewise linear trend with the breakpoint in winter 1997/98 (dotted line). ( d ) (solid curves) Standardised time series of (blue) PC1 SAT−A70 and (red) the average SAT over the northern Barents Sea region (nBS box in b ), and their OT points (circles) and continuous piecewise linear trends with the breakpoint in winter 2003/04 (dashed lines). In ( a , b ) the thin contour and shading colours are explained in the caption to Fig. . The CI is 5% per unit PC1 SIC−A40 and 0.5 °C per unit PC1 SAT−A70 , respectively. The thick black lines indicate the climatological mean wintertime ice edge (15% SIC contour). The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ). In ( c , d ) each year on the horizontal axis includes January of the DJF season.

Article Snippet: The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Techniques: Concentration Assay, Northern Blot, Generated

Journal: Scientific Reports

Article Title: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming

doi: 10.1038/s41598-019-49965-6

Figure Lengend Snippet: Relationship between the summer (JJA) mean subsurface ocean temperature and the following winter sea ice area in the Barents/Nordic Seas region during the ESO period. ( a ) (colours) Climatological mean temperature averaged over the 150–250 m layer ( T 150−250 ) in the EARLY period (summers 1981–2003). ( b ) As in ( a ) but for the LATE period (summers 2004–2017). ( c ) (colours) Difference in the mean of T 150−250 (in °C) between the LATE and EARLY periods. ( d ) (colours) Difference in the composite mean of T 150−250 between six summers with the smallest and six summers with the largest sea ice coverage in the Barents/Nordic Seas region marked as BNS box (SIA BNS index) in the following winter during the EARLY period. ( e ) As in ( d ) but for the summers preceding three winters with the smallest and four winters with the largest SIA BNS during the LATE period. ( f ) (solid curves) Standardised time series of (blue curve) the summer mean Atlantic water temperature averaged over the southern Svalbard slope area (SSS box in e ) and (red curve) the following winter SIA BNS index, their OT points (circles), their continuous piecewise linear trends with the breakpoint in summer 1997 and winter 1997/98 (dotted lines), and their continuous piecewise linear trends with the breakpoint in summer 2003 and winter 2003/04 (dashed lines), respectively. Each year on the horizontal axis refers to the summer season. In ( a – e ) grid lines are masked in the areas of valid data. Grid cells shallower than 150 m are white shaded, while areas of missing ocean data are dark shaded. In ( c – e ) differences smaller than 0.2 °C or nonsignificant at the 95% confidence level are white shaded. The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Article Snippet: The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Techniques: Generated

Journal: Scientific Reports

Article Title: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming

doi: 10.1038/s41598-019-49965-6

Figure Lengend Snippet: Spatial structure, seasonal evolution and predictability of the sea ice concentration anomalies in the Barents/Nordic Seas region during the EARLY and LATE periods. ( a , b ) (thin contours and colour shading) Detrended winter mean SIC anomalies regressed onto the detrended previous summer anomalies of Atlantic water temperature (AWT SSS index, blue curve in Fig. ) in the EARLY ( a ) and LATE ( b ) periods. The thin contour and shading colours are explained in the caption to Fig. . The CI is 5% per unit AWT SSS index re-standardised for the anomalies in the two periods. The thick black lines (15% and 85% SIC contours) delineate the marginal ice zone in the two periods. The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ). ( c , d ) Time-lagged correlation coefficient of the summer mean AWT SSS anomalies with the seasonal (3-month) mean anomalies of the sea ice area in (blue line) the Barents/Nordic Seas (BNS box in a ) and Barents Sea (BS box in b ) regions during the EARLY ( c ) and LATE ( d ) periods. The filled circles denote correlations statistically significant at the 95% confidence level. Positive (resp. negative) lags correspond to the AWT SSS anomalies leading (resp. lagging) the SIA anomalies. ( e , f ) Time series of the observed (blue curve) and predicted (red curve) wintertime SIA anomalies in the Barents/Nordic Seas region in the EARLY ( e ) and LATE ( f ) periods. The predictions are for the DJF and NDJ mean SIA BNS anomalies in the EARLY and LATE periods, respectively. They are based on leave-1-yr-out cross-validation forecasts with the previous JJA mean AWT SSS anomalies as the predictor. Each year on the horizontal axis includes January of the winter season.

Article Snippet: The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Techniques: Concentration Assay, Generated

Journal: Scientific Reports

Article Title: Subsurface ocean flywheel of coupled climate variability in the Barents Sea hotspot of global warming

doi: 10.1038/s41598-019-49965-6

Figure Lengend Snippet: Spatial structure and seasonal evolution of SST and surface wind anomalies in the Barents/Nordic Seas region during the LATE period. ( a ) Difference in early winter (NDJ) SST (colours) and u s (arrows) between 2004/05 and 2003/04. ( b ) Early winter anomalies of SST and u s regressed onto the winter (DJF) SIA BS index multiplied by −1. ( c ) Correlation coefficient of the autumn (SON) SST anomalies with the following winter SIA BS index (multiplied by −1) and autumn u s anomalies regressed onto that index. ( d ) Early winter anomalies of SST and u s regressed onto the previous summer AWT SSS index. ( e ) Time-lagged correlation coefficient of the detrended (blue curve) and raw (red curve) summer AWT SSS index with the corresponding seasonal mean SSTs averaged over the southern Barents Sea (sBS box in d ). ( f ) As in ( e ) but for the seasonal mean surface meridional winds (positive northward) averaged over the eastern Barents Sea (eBS box in d ). In ( b – d ) the time series were detrended before the analysis. The thin contour and shading colours (explained in the caption to Fig. ) are for the SST anomalies. The CI is 0.1 °C per unit SIA BS index, 0.1 and 0.1 °C per unit AWT SSS index, respectively. The anomalies of u s are subsampled and masked if both components are nonsignificant at the 95% confidence level. In ( a – d ) the thick black lines are the mean 15% SIC contours in early winter 2004/05, early winters and autumns of the LATE period, and early winters of the LATE period, respectively. The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ). In ( e , f ) the filled circles denote correlations statistically significant at the 95% confidence level. Positive (resp. negative) lags correspond to AWT SSS leading (resp. lagging) the surface variables.

Article Snippet: The maps were generated by MathWorks MATLAB R2014a with M_Map ( http://www.eoas.ubc.ca/rich/map.html ).

Techniques: Generated

Journal: Magnetic resonance in medicine

Article Title: Dynamic volumetric hyperpolarized 13 C imaging with multi-echo EPI

doi: 10.1002/mrm.28466

Figure Lengend Snippet: A, The time courses for all metabolites summed over the entire field of view (FOV). B, The plot for each metabolite is baseline-corrected by subtracting the maximum value of the noise (time points before the injection). The red and blue rectangles mark the durations defined as early (9 to 15 seconds) and late (18 to 27 seconds) time points. C, Representative sum of metabolite maps reconstructed from 13C echo-planar imaging (EPI) acquired 9 to 15 seconds (early time points) and 18 to 27 seconds (late time points) after injection. Axial and coronal 1H T2-weighted images are shown for reference. The axial slice displays the heart, and the coronal slice displays slices for the abdominal aorta and liver. White lines show the plane of coronal slice on the axial image and the plane of the axial slice on the coronal image. The intensity of [1-13C]pyruvate in the heart is high at the early time points and sharply reduced at the late time points, but still maintained in the liver. The metabolic products are localized in the heart in early time points and are visible in other organs in the late time points. AUC, area under the curve

Article Snippet: Complex 13C Digital Imaging and Communications in Medicine (DICOM) EPI images were exported from the MRI scanner, and linearized by applying R2* and B0 inhomogeneity maps estimated by direct phase-fitting of all echoes,28 and processed into metabolite maps in MATLAB (R2017a; MathWorks, Natick, MA) using the IDEAL implementation described in the 2.

Techniques: Injection, Imaging

Journal: Magnetic resonance in medicine

Article Title: Dynamic volumetric hyperpolarized 13 C imaging with multi-echo EPI

doi: 10.1002/mrm.28466

Figure Lengend Snippet: 4Average region-of-interest (ROI) measurements for metabolic biodistribution in selected organs. A, ROIs are prescribed on anatomical 1H T2-weighted images of slices containing the kidneys, liver, and heart and copied to metabolite maps for quantifying the average metabolite image intensity. B, The average for three mice of the mean organ intensity for each metabolite is shown plotted over time. C, The area-under-the-curve (AUC) ratio in each organ is shown for each metabolite

Article Snippet: Complex 13C Digital Imaging and Communications in Medicine (DICOM) EPI images were exported from the MRI scanner, and linearized by applying R2* and B0 inhomogeneity maps estimated by direct phase-fitting of all echoes,28 and processed into metabolite maps in MATLAB (R2017a; MathWorks, Natick, MA) using the IDEAL implementation described in the 2.

Techniques: