Today, Red Giant Software released the latest addition to their already impressive collection of effects and grading suites. The Red Giant VFX Suite offers 9 freshly coded effects for immediate use in Adobe After Effects. Four of the nine also work in Premiere Pro. And all these effects can be tweaked and tailored to your specific needs, of course.

Located on the fourth level of the ballpark are Oracle Park's 61 private luxury suites. Suite number 1 (the Tony Bennett Suite) through number 61 provide the ultimate place to entertain friends, family or business associates. Suites hold 12-30 people and are equipped with 3 televisions, dual-line telephones, and refrigerator. Other amenities include: in-suite catering, Alaska Airlines Club Level access, concierge service and priority reserved parking spaces. Outside food and beverage is not permitted on the Oracle Suite Level.

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Founded in 2002, Red Giant creates an ever-expanding universe of effects tools ranging from plug-in suites, applications and mobile apps to Guru Presets, free products and sharing communities. We provide software for motion design, photography and color correction that is used for everything from major motion pictures to worldwide television programming to web production. Red Giant offers the industry-leading Trapcode tools for broadcast design; Magic Bullet Suite for color correction; and over 60 products that run in After Effects, Final Cut Pro, Motion, Premiere Pro, Photoshop, Lightroom, Aperture, Avid, Vegas, Nuke, and Studio. Our effects have enhanced dozens of feature films such as Angels & Demons and The Social Network, and added sparkle to networks like NBC Universal, ESPN, Disney, CNN, Comedy Central, MTV, and TNT.

Methods:  We conducted a comprehensive search (PubMed, Cochrane) for all randomized controlled trials between 1/1/95 to 12/31/04. Eligible studies include those that focused upon orthopaedic trauma. Baseline characteristics and treatment effects were abstracted by two reviewers. Briefly, for continuous outcome measures (ie functional scores), we calculated effect sizes (mean difference/standard deviation). Dichotomous variables (ie infection, nonunion) were summarized as absolute risk differences and relative risk reductions (RRR). Effect sizes >0.80 and RRRs>50% were defined as large effects. Using regression analysis we examined the association between the total number of outcome events and treatment effect (dichotomous outcomes).

Conclusion:  Our review suggests that statistically significant results in orthopaedic trials have the following implications-1) On average large risk reductions are reported 2) Large treatment effects (>50% relative risk reduction) are correlated with few number of total outcome events. Readers should interpret the results of such small trials with these issues in mind.

Throughout the entire trip, astronauts must be protected from two sources of radiation. The first comes from the sun, which regularly releases a steady stream of solar particles, as well as occasional larger bursts in the wake of giant explosions, such as solar flares and coronal mass ejections, on the sun. These energetic particles are almost all protons, and, though the sun releases an unfathomably large number of them, the proton energy is low enough that they can almost all be physically shielded by the structure of the spacecraft.

Now, I've given you a few examples of how language can profoundly shape the way we think, and it does so in a variety of ways. So language can have big effects, like we saw with space and time, where people can lay out space and time in completely different coordinate frames from each other. Language can also have really deep effects -- that's what we saw with the case of number. Having count words in your language, having number words, opens up the whole world of mathematics. Of course, if you don't count, you can't do algebra, you can't do any of the things that would be required to build a room like this or make this broadcast, right? This little trick of number words gives you a stepping stone into a whole cognitive realm.

It is generally recommended that meta-analyses are undertaken using risk ratios (taking care to make a sensible choice over which category of outcome is classified as the event) or odds ratios. This is because it seems important to avoid using summary statistics for which there is empirical evidence that they are unlikely to give consistent estimates of intervention effects (the risk difference), and it is impossible to use statistics for which meta-analysis cannot be performed (the number needed to treat for an additional beneficial outcome). It may be wise to plan to undertake a sensitivity analysis to investigate whether choice of summary statistic (and selection of the event category) is critical to the conclusions of the meta-analysis (see Section 10.14).

*The importance of the observed value of I2 depends on (1) magnitude and direction of effects, and (2) strength of evidence for heterogeneity (e.g. P value from the Chi2 test, or a confidence interval for I2: uncertainty in the value of I2 is substantial when the number of studies is small).

An estimate of the between-study variance in a random-effects meta-analysis is typically presented as part of its results. The square root of this number (i.e. Tau) is the estimated standard deviation of underlying effects across studies. Prediction intervals are a way of expressing this value in an interpretable way.

Prediction intervals have proved a popular way of expressing the amount of heterogeneity in a meta-analysis (Riley et al 2011). They are, however, strongly based on the assumption of a normal distribution for the effects across studies, and can be very problematic when the number of studies is small, in which case they can appear spuriously wide or spuriously narrow. Nevertheless, we encourage their use when the number of studies is reasonable (e.g. more than ten) and there is no clear funnel plot asymmetry.

If studies are divided into subgroups (see Section 10.11.2), this may be viewed as an investigation of how a categorical study characteristic is associated with the intervention effects in the meta-analysis. For example, studies in which allocation sequence concealment was adequate may yield different results from those in which it was inadequate. Here, allocation sequence concealment, being either adequate or inadequate, is a categorical characteristic at the study level. Meta-regression is an extension to subgroup analyses that allows the effect of continuous, as well as categorical, characteristics to be investigated, and in principle allows the effects of multiple factors to be investigated simultaneously (although this is rarely possible due to inadequate numbers of studies) (Thompson and Higgins 2002). Meta-regression should generally not be considered when there are fewer than ten studies in a meta-analysis.

Review authors may undertake sensitivity analyses to assess the potential impact of missing outcome data, based on assumptions about the relationship between missingness in the outcome and its true value. Several methods are available (Akl et al 2015). For dichotomous outcomes, Higgins and colleagues propose a strategy involving different assumptions about how the risk of the event among the missing participants differs from the risk of the event among the observed participants, taking account of uncertainty introduced by the assumptions (Higgins et al 2008a). Akl and colleagues propose a suite of simple imputation methods, including a similar approach to that of Higgins and colleagues based on relative risks of the event in missing versus observed participants. Similar ideas can be applied to continuous outcome data (Ebrahim et al 2013, Ebrahim et al 2014). Particular care is required to avoid double counting events, since it can be unclear whether reported numbers of events in trial reports apply to the full randomized sample or only to those who did not drop out (Akl et al 2016).

Most Bayesian meta-analyses use non-informative (or very weakly informative) prior distributions to represent beliefs about intervention effects, since many regard it as controversial to combine objective trial data with subjective opinion. However, prior distributions are increasingly used for the extent of among-study variation in a random-effects analysis. This is particularly advantageous when the number of studies in the meta-analysis is small, say fewer than five or ten. Libraries of data-based prior distributions are available that have been derived from re-analyses of many thousands of meta-analyses in the Cochrane Database of Systematic Reviews (Turner et al 2012). 589ccfa754

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