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Snake Eyes (version 65) was created using the alternate head and body of Snake Eyes (v54) (2011).

Note that the base figure of Snake Eyes (v65) is extremely similar to that of Snake Eyes (v54) - however, Snake Eyes (v65) lacks the gloss black paint applications of Snake Eyes (v54), lacks the GI Joe unit logo on the left shoulder, and features a larger Arashikage symbol on his right shoulder.

a the fraction of tropical vegetated area affected by extreme droughts for tropical (T.) forests and semi-arid regions. The solid line presents the mean fraction of tropical vegetated area affected by extreme droughts in every 20 years window from 1959 to 2016, with the x axis indicating the center of each window. The shaded area indicates the variability of the fraction (e.g., one standard deviation) in each 20-year window; c the fraction of tropical vegetated area affected by extreme droughts for tropical America, tropical Africa, and tropical Asia; e the changes of the extreme drought hotspots in three 20 years periods. Hotspots are defined as regions that are under extreme droughts for more than 10% of time; b, d, f The impacts of tropical extreme droughts on global net ecosystem exchange variability (STDNEE) in three independent 20-year periods. The extreme drought-induced STDNEE was estimated using the relationship between drought-affected area and STDCGR (shown in Fig. 2c) in combination with a spatial weight based on FLUXCOM NEE (see Methods). The average impact from each region is indicated by the numbers in the figure, while the error bars indicate the variability of extreme drought-induced STDNEE in each 20-year window propagated from the variability in the fraction of area affected by extreme droughts shown in (a) and (c). The impact of droughts estimated by DGVMs and FLUXCOM are included as reference, where the error bars indicate the intermodel variations. Fire emissions are obtained from Global Fire Emissions Database (GFED4s).

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Extreme events are known to influence the terrestrial carbon cycle39, and drought is the most critical one40. Several studies have suggested that a few extreme events explained a significant amount of the variance in land-atmosphere carbon exchange, at seasonal or interannual time scales41,42. Here, our results show that extreme droughts influence CGR at the bi-decadal scale by amplifying STDCGR and \({{{{{{\rm{\gamma }}}}}}}_{{{{{{\rm{CGR}}}}}}}^{{{{{{\rm{T}}}}}}}\). Extreme droughts induce various concurrent and lagged effects on ecosystems43, which include processes of either carbon emissions or carbon uptake. Though we find a tight correlation between extreme drought-affected area and STDCGR (Fig. 2c), we acknowledge that the affected area is an integrated indicator of drought effects, which does not distinguish the various induced processes and their respective functioning time scales. Among the drought-induced effects, fire is unlikely to be the sole reason for the changes in STDCGR as our analysis does not show a change in the long-term variability in fire emissions (Supplementary Fig. 2), meaning the role of other process needs further investigations. We suggest that extreme droughts in semi-arid ecosystems and tropical Africa and Asia deserve more attention for understanding long-term dynamics of the terrestrial carbon cycle.

Based on the tight correlation between extreme drought-affected area and STDCGR, we adopted a time-for-space substitution to quantify regional (i.e., tropical America, tropical Africa, tropical Asia, tropical semi-arid ecosystems and tropical forests) contributions to STDCGR, using the drought-affected area detected for these regions. To consider the spatial variation of regions in their land-atmospheric CO2 exchange capacity, we used the multiyear average FLUXCOM NEE map as the spatial weight, as FLUXCOM NEE represents our best estimate of the spatial variation in NEE58 and the product shows potential in capturing the extreme drought influence on NEE (Fig. 2d; Supplementary Fig. 5).

We note that to estimate the regional contribution of extreme droughts to STDCGR, it is necessary to account for all transient and long-term carbon fluxes incurred by extreme droughts. In theory, this can be simulated in process-based DGVMs. But current DGVMs, as our results have suggested (Fig. 2d), inadequately capture the drought impacts on STDCGR. Another option is to use remotely sensed aboveground biomass (AGB)77 rather than data-driven NEE as the spatial weight. However, the method would imply that extreme droughts induced carbon losses proportionally to AGB. The assumption was questionable as drylands have less biomass but usually comparable net carbon exchange than wet forests13,14. Therefore, using the data-driven net flux product (i.e., FLUXCOM NEE) as a weight would be the preferred available option to approximate the regional contribution to STDCGR, as FLUXCOM NEE showed potential in capturing the extreme drought influence on NEE (Fig. 2d; Supplementary Fig. 5). Importantly, our study is designed to provide a first-order quantification of the regional contribution to STDCGR at coarse continental and biome scales, while the fine-scale variations at the pixel-level within each continent and biome remain to be addressed.

The 2015 Toyota Tacoma is finally here to take on the extreme Ike Gauntlet towing challenge. This midsize pickup truck has dominated the sales in recent years and this generation is in its last year. An all-new version is coming for the 2016 model year with more power and efficiency (according to Toyota). TFLtruck wanted to run the 2015 truck to get baseline data and also to make the Tacoma eligible for the 2015 Gold Hitch Awards.

We are pleased to announce that the SciChart WPF v6.5 build 13720 has now been released! This update includes a number of excellent new features, lots of stability fixes and performance & memory enhancements. This release is a drop-in replacement for 6.x and it is backward compatible for users of SciChart WPF version 6.

When you call Toyota Customer Service, they reference the owners manual which indeed recommends 60K for extreme driving. Customer service rep said she had internal documentation that recommends 60K but no later than 90K for normal driving.

Engineered to be robust, system incorporates High-thermal transfer intercooler for maximum aircharge cooling, yielding super low inlet air temps for consistent power from startup to shut down and to abate belt slip even under extreme conditions of 4-wheeling, the TVS1900 boasts a custom engineered supercharger belt-drive system for worry-free driving both on and off-road.

Note: Available for all 2016-2023 Toyota Tacoma's including stock and modified vehicles. For extreme builds, please contact our sales team to verify suitability for tuning. See Fitment tab for more details.

Calibration available for stock or modified vehicles. Modifications must be documented when submitting the Calibration File Form to Magnuson. For extreme builds, please contact our Sales Department to verify suitability for tuning in advance of purchasing kit.

The following axis classifications described are based on adults. If the QRS axis falls between -30 degrees and -90 degrees, it is considered LAD. In this case, the QRS vector is directed upward and to the left. If the QRS axis falls between +90 degrees and 180 degrees, or beyond +100 degrees if the adult range is used, then RAD is present. The QRS vector would be directed downward and to the right. If the QRS axis happens to fall between -90 degrees and 180 degrees, this would be referred to as extreme axis deviation, whereby the ventricular vector is directed upward and to the right. Lastly, if the QRS complex is isoelectric or equiphasic in all leads with no dominant QRS deflection, it is considered an indeterminate axis. The electrical axis classifications are summarized in Figure 2.

Method 1. One simple way to determine the electrical axis is to inspect limb leads I and aVF. This is referred to as the quadrant approach or two-lead method. Each of the four quadrants represents 90 degrees and an axis type. In other words, 0 degrees to +90 degrees is a normal axis, +90 degrees to 180 degrees is RAD, 0 degrees to -90 degrees is LAD, and -90 degrees to 180 degrees is an extreme axis. Therefore, if leads I and aVF are both positive, then the axis falls within the normal axis range. If lead I is positive and lead aVF is negative, then there is LAD. If lead I is negative and lead aVF is positive, then there is RAD. And, if both leads I and aVF are negative, then the axis falls within the extreme axis range. This quadrant approach is summarized in Figure 3.

Method 2. A more accurate approach than the simple quadrant approach takes into account leads I and aVF, as well as lead II. This is referred to as the three-lead method. If the net QRS deflection is positive in both leads I and II, the QRS axis is normal. If the net QRS deflection is positive in lead I but negative lead in II, then there is LAD. Notice that in both cases, lead aVF was not needed. In other words, if lead I is positive, look next to lead II. Now, if lead I is negative, look next to lead aVF. If lead aVF is positive, then the axis is rightward; however, if lead aVF is also negative, then there is the extreme axis. This approach is summarized in Figure 4 and Table 1.

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