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There is no support for R’s notion of missing values, in particular not for NA_INTEGER nor the distinction between NA and NaN for doubles.

A little care is needed to use the random-number routines. You will need to supply the uniform random number generator

or use the one supplied (and with a shared library or DLL you may have to use the one supplied, which is the Marsaglia-multicarry with an entry point

to set its seeds).

The facilities to change the normal random number generator are available through the constant N01_kind. This takes values from the enumeration type

(and ‘ USER_NORM ’ is not available).

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If R has not already been made in the directory tree, configure must be run as described in the main build instructions.

Then (in src/nmath/standalone )

will make standalone libraries libRmath.a and ( libRmath.dylib on macOS): ‘ make static ’ and ‘ make shared ’ will create just one of them.

To use the routines in your own C or C++ programs, include

and link against ‘ -lRmath ’ (and ‘ -lm ’ if needed on your OS). The example file test.c does nothing useful, but is provided to test the process (via make test ). Note that you will probably not be able to run it unless you add the directory containing to the LD_LIBRARY_PATH environment variable ( libRmath.dylib , DYLD_FALLBACK_LIBRARY_PATH on macOS).

The targets

will (un)install the header Rmath.h and shared and static libraries (if built). Both prefix= and DESTDIR are supported, together with more precise control as described for the main build.

make install ’ installs a file for pkg-config to use by e.g.

On some systems ‘ make install-strip ’ will install a stripped shared library.

Previous: Unix-alike standalone , Up: The standalone Rmath library [ Contents ][ Marc Jacobs Low top sneakers mXzq6Od

You need to set up 37 almost all the tools to make R and then run (in a Unix-like shell)

Alternatively, in a cmd.exe shell use

This creates a static library libRmath.a and a DLL Rmath.dll . If you want an import library libRmath.dll.a (you don’t need one), use

Localizer run data were pre-processed with motion correction using a trilinear/sinc interpolation algorithm with the volume collected temporally closest to the acquisition of the T1-weighted anatomical MRI used as the reference volume. Head movement did not exceed 2mm in any direction for any of the participants in the study. Slice time correction and linear trend removal were also performed on each localizer run. The data were also spatially smoothed using a 6 mm full-width half-maximum isotropic kernel. Design matrices for general linear model (GLM) analysis were produced from each participant’s stimulation protocol using a boxcar design convolved with a hemodynamic response function. Within each participant’s stimulation protocol, separate predictors were defined for the cue periods, instructed-delay periods, and movement periods of each trial for both of the two conditions; however, trials for the standard and non-standard mapping conditions were pooled together to reveal areas active in both conditions. Predictors were also defined for each of the instruction periods preceding each of the two blocks of trials. Within each of the two conditions, trials toward the left and right targets were pooled together in stimulation protocols. The head motion profiles (3 linear and 3 rotational directions) were added to each participant’s stimulation protocol as predictors of non-interest.

Localizer run data were normalized to Talairach space. Runs collected for the AVG player group and the AVG non-player group were analyzed using a conjunction analysis to localize regions of interest that were active in both groups. Thresholding of the resulting statistical map used a false discovery rate approach (FDR) with q set to 0.05. The resulting regions of interest (ROIs) are summarized below in Table 1 .

Table 1. Regions of interest localized across all participants.

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Comparison of head motion between groups during experimental imaging runs.

Since part of the analysis procedure involved comparing fMRI signals occurring after the go signal for initiating movement, we quantified in-scan head motion for comparison between the experimental groups. Head motion was calculated using Brainvoyager QX in x, y, and z directions of linear translation and in x, y, and z (pitch, roll, and yaw) directions of rotation for each volume over the course of each imaging run. Composite translation and composite rotation values over time were calculated separately by squaring each x, y, and z value, then summing the resulting squared values and then taking the square root of this sum at each time point. These measurements were combined across imaging runs for each participant. For each participant’s resulting composite translation and rotation values over time, the area under the curve was estimated using Riemann sums and used to test for differences in amounts of translational and/or rotational head motion between the AVG-player and non-player groups using two-tailed independent t-tests.

fMRI data analyses.

Regions of interest (ROIs) derived from the mean Talairach normalized localizer runs of all participants (see Table 1 ) were applied to the experimental runs collected from participants. Experimental runs were left in native subject space but ACPC aligned. For each participant, base-line normalized beta weights were calculated for each model predictor using a random effects, general linear model (GLM) approach. Two-factor, mixed-effect ANOVAs with repeated measures were run with beta weights associated with the instructed-delay period of the standard and non-standard mapping conditions as the within subjects factor and experimental group (i.e. AVG players or non-players) as the between subjects factor.

Two post-hoc analyses were run on data from ROIs in which a significant between group difference was detected in the ANOVA analyses. For the first analysis, a linear regression was performed for each participant’s mean BOLD signal over the instructed-delay period versus self-reported estimates of average amounts of time spent playing action video games per week in the year prior to scanning. For the second post-hoc examination, event-related averaging of BOLD signal time-courses in these regions of interest was performed for each individual participant for the standard and non-standard visuomotor mappings such that epochs were time-locked to the onset of the instructed-delay period and extracted from 2 volumes prior to delay onset to 11 volumes after delay onset. Therefore, these averages included the cue period, instructed delay period, movement period and inter-trial intervals. All 4 experimental runs were pooled together for each participant to obtain event-related averages. For each participant, the peak beta weight after the Go signal was obtained from the event-related average as well as the volume number (relative to the onset of the delay period) at which this peak occurred. Peak fMRI beta weights after the Go signal were compared using two-factor, mixed-effect ANOVAS with repeated measures (i.e. experimental group as the between subjects factor, and visuomotor mapping as the within subjects factor). Similarly, the timing at which this peak occurred (i.e. the volume number after the onset of the instructed-delay period) was also compared using two-factor, mixed-effect ANOVAs with repeated measures. In addition, linear regression analyses were performed for the timing (i.e. volume number) at which the peak fMRI beta weight occurred after the Go signal versus self-reported estimates of average amounts of time spent playing action video games per week in the year prior to scanning were run.

In-magnet behavioural data.

The total number of reaching errors made in the magnet did not significantly differ between the AVG player and non-player groups ( = 4.90, = 0.25). There were also no significant differences in total number of errors between the two visuomotor mapping conditions ( = 0.084, = 0.78). The mean number of reaching errors across all 4 experimental imaging runs and both conditions that were made by AVG players was 1.4 errors +/- 1.8 SD and in the non-players 0.7 errors +/- 1.2 SD. Trials containing these errors were excluded from further analysis. Of this small number of excluded trials, there were four different types of errors: failing to complete the movement before the end of the trial (69.23% of the total amount of errors in AVG players and 62.96% of the total amount of errors in non-players), moving prior to the go signal (15.38% of the total amount of errors in AVG players and 22.22% of the total amount of errors in non-players), failing to initiate a movement, (0% of the total amount of errors in AVG players and 11.11% of the total amount of errors in non-players), and moving the arm in the wrong direction or making a direction reversal (15.38% of the total amount of errors in AVG players and 3.70% of the total amount of errors in non-players).

In the MRI, participants were instructed to make slow, smooth arm movements in order to minimize the translation of arm movements to the head. The mean reaction times (RT) for the two groups of participants were not significantly different ( = 2.19, = 0.16). There were also no significant differences in RT between the two visuomotor mapping conditions ( = 1.8, = 0.19). Mean RT values (pooled across visuomotor mapping conditions) were 1.15 s +/- 0.16 SD for the AVG players and 1.3 s +/- 0.28 SD for the non-players. Similarly, the mean movement times (MT) for the two groups of participants were not significantly different ( = 0.29, = 0.60) and there were also no significant differences in MT between the two visuomotor conditions ( = 1.85, = 0.19). Mean MT values (pooled across visuomotor mapping conditions) were 0.424 s +/- 0.215 SD for the AVG players and 0.374 s +/- 0.189 SD for the non-players.

As noted above, participant’s heads were tilted forward in the scanner to allow direct viewing of targets projected onto the screen. This head position made it difficult to position the eye tracker optimally. Eye position data were sometimes intermittent due to the eyelid blocking the camera’s view of part of the pupil in the majority of participants. Therefore, eye data were reliable enough to obtain the direction of movements but not eye movement latency or precise movement end points. In total, the percentage of trials in which the eye data did not contain enough information to infer the direction of eye movements was 13.5% in the AVG players and 19.5% in the non-players. The entire eye movement data set for one AVG player and one non-player were excluded from analysis. Of the remaining data, the number of direction reversals or movements to the wrong target did not significantly differ between the AVG players and non-players ( = 0.90, = 0.36) and there were also no significant differences in these measures between the two experimental conditions ( = 0.36, = 0.55). The mean number of eye movement errors (pooled across visuomotor conditions and all 4 experimental runs) were 1.67 +/- 2.25 for the AVG players and 0.95 +/- 1.42 for the non-players.

Two-tailed independent t-test comparisons of area under the curve (AUC) estimates for group mean head motion (composite translational motion and composite rotational motion) did not reveal significant differences between the gamer and non-gamer groups of participants ( Steve Madden Troopah Boots in BFB8GnV
). Mean AUC for translational head motion over experimental imaging run collection was 96 (mm x volumes) +/- 125.9 SD for the gamer group and 129 +/- 52.1 for the non-gamer group, t = 1.79, p = 0.09. Mean AUC for rotation head motion was 131 (degrees x volumes) +/- 62.4 for the gamer group and 121 +/- 38.8 for the non-gamer group, t = 0.43, p = 0.67.

Recently, however, there has been renewed interest in studying the possible role of the epidermis in restraining the growth of the inner root tissues in Arabidopsis roots ( Ubeda-Tomas et al ., 2005 ). It is also possible that the transition to more rapid cell elongation between the meristem and the elongation zone of Arabidopsis may be influenced by the presence of the outer root cap cell layer (J Haseloff, personal communication). It could be that there are significant species differences: Arabidopsis roots have an outer root cap cell layer that extends a distance of 3–4 times the root diameter from the apex, whereas, in maize, the outer cap cell layer may only reach 1.5 times the root diameter from the apex. Only a single cortical cell layer is present in Arabidopsis , in comparison to the many cell layers in the maize and wheat root cortices. In maize and wheat the epidermis may account for only a relatively small percentage of the root cross-sectional area, perhaps 5–10%, compared with 45–50% in Arabidopsis . In maize or wheat, therefore, to impose the same force limitation on the extension of the inner root tissues would require >5-fold greater average tensile stress in the epidermis, than in the Arabidopsis epidermis. In addition, there are preliminary observations that for pea roots grown in compacted sand the epidermal cell layer may collapse within a few mm of the root tip (Dr Trudi Gillespie, personal communication), suggesting that the epidermis is unlikely to regulate cell extension in this case.

To elucidate the tissues that control the root elongation rate requires spatial and temporal resolution adequate to discern the expansion rates of individual cells with a time-scale of minutes. One promising approach is the recent application of optical flow techniques to root growth kinematic analysis ( Walter et al ., 2002 ; Van der Weele et al ., 2003 ). In preliminary experiments two optical flow techniques have successfully been applied to in vivo images of Arabidopsis roots ( Fig. 5 ), labelled with Green Fluorescent Protein (GFP) markers or fluorescent stain. It was found that it was possible to track changes in growth for groups of cells with a temporal resolution of minutes. Higher temporal resolution (e.g. tens of seconds) would depend on there being sufficient spatial displacement between sequential images and so would only be achievable at relatively high magnification. The development of methods for tracking cells and cellular structures, alongside optical flow techniques, offers the opportunity to develop more sophisticated three-dimensional models of root cell growth than has been possible previously. This approach will add dynamics to the visualization of cell morphogenesis ( Haseloff, 2003 ) and, alongside targeted molecular approaches to manipulating cell properties, enable the complex process of root growth to be analysed. It is evident that many practical hurdles exist on the way, such as analysing and accommodating the growth rate changes due to root nutation: Such nutational movements are relatively large at high magnification, and increase the movements around the main axis of extension ( Shabala and Newman, 1997 ; Walter et al ., 2003 ). Further care must also be taken to check lens calibration within the field of view, translating a rigid object across a distorting lens will give rise to apparent strain (growth) as an artefact. The method of staining or marking the internal structure of the root requires further investigation to optimize image texture and contrast of the cell walls, without interfering with the natural growth processes.

12th Man Rising

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MIAMI, FL - JULY 11: In this photo illustration, a Papa John's pizza is seen on July 11, 2018 in Miami, Florida. The founder of Papa John's pizza, John Schnatter, apologized Wednesday for using the N-word on a conference call in May. (Photo illustration by Joe Raedle/Getty Images)

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