NO addition enhances outward currents of SM LNs <i>in vitro</i> at higher doses.

<p><b>A</b>. Representative traces showing (left to right) saline (control), ProliNO (100 mM) and subtracted current responses. <b>B</b>. I–V plot of normalized current responses of <i>in vitro</i> SM LNs in saline, increasing ProliNO doses, and washout (means, n = 4). <b>C</b>. NO addition significantly enhances an outward current at 100 & 250 mM ProliNO.</p

Cultured antennal lobe neurons examined in this study after 14 day <i>in vitro</i>.

<p><b>A</b>. Projection neuron (PN) B. RickRack local interneuron (RR LN) showing recording electrode <b>C</b>. Fuzzy compact local interneuron (FC LN) D. Symmetrical local interneuron (SM LN). Scale bar = 50 µm.</p

NO addition enhanced outward current of one subset of <i>in vitro</i> RR LNs and inward current of another subset.

<p><b>A</b>. Representative traces showing (left to right) saline (control), ProliNO (100 mM) and subtracted current responses of an RR LN with basal outward current. <b>B</b>. I–V plot of normalized current responses of <i>in vitro</i> RR LNs in saline, increasing ProliNO doses, and washout (means, n = 3). <b>C</b>. NO addition significantly enhances an outward current in RR LNs with basal outward current in 1 mM,10 mM, and 250 mM ProliNO (means +/− SD, n = 3). <b>D–F</b>. Same as above for RR LNs with basal inward currents. F. NO addition significantly enhances inward currents of RR LNs with basal inward current at 250 mM ProliNO (means +/− SD, n = 4).</p

NO addition enhances outward current of FC LNs <i>in vitro</i>.

<p><b>A</b>. Representative traces showing (left to right) saline (control), ProliNO (250 mM) and subtracted current responses. <b>B</b>. I–V plot of normalized current responses of <i>in vitro</i> FC LNs in saline, increasing ProliNO doses, and washout (means, n = 4). <b>C</b>. NO addition significantly enhances an outward current in a dose-dependent manner above 1 mM ProliNO.</p

NO addition attenuates outward current of two subsets of PNs <i>in vitro</i>.

<p><b>A</b>. Representative trace of PN with basal outward current <i>in vitro</i> showing (left to right) saline (control), ProliNO (100 mM) and subtracted current responses, elicited by the depolarization protocol (<i>bottom</i>): Voltage stepped from −90 to +50 mV in 10 mV increments for 100 ms from a holding potential of −70 mV. <b>B</b>. I–V plot of normalized current for control, increasing ProliNO doses (1, 10, 100, &250 mM), and washout (mean, n = 4). <b>C</b>. NO addition inhibited significantly the steady-state outward current of PNs with basal outward current in a dose-dependent manner (means +/− SD, n = 4). <b>D–F</b>. NO addition enhances significantly inward current responses of PNs with basal inward current. <b>D</b>. Representative traces of saline, ProliNO (100 mM) and subtracted current responses. <b>E, F</b>. Normalized I–V plot and histogram showing responses of <i>in vitro</i> PNs with basal inward current responses to increasing doses of ProliNO (Means +/− SD, n = 4).</p

NO inhibition (L-NAME) <i>in viv</i>o results in increasing outward current in some PNs (A, B, C) and decreasing inward current in others (D, E, F).

<p><b>A</b>. Representative traces of net outward currents elicited by the depolarization protocol (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042556#pone-0042556-g001" target="_blank">figure 1</a>) before (saline), 30 min. after treatment with L-NAME, and the difference (L-NAME - Saline). <b>B</b>. Normalized current I–V plot for control, L-NAME (15 mM), and washout (mean +/− SE; n = 4). <b>C</b>. L-NAME significantly increased an outward current (p<0.05). <b>D</b>. I–V plot showing increasing doses on NO inhibitor (L-NAME) <i>in vivo</i> results in dose-dependent increases in outward current. <b>E–G</b>. Current responses of neurons with inward basal response (mean +/− SE, n = 5). L-NAME decreases inward current significantly (p<0.01).</p

Bin ranges for input variables used to produce a stratified map of the region over which carbon modeling was performed (see methods).

<p>Twenty total bands were dispersed non-randomly according to the strength of each variable in predicting carbon stocks, which has been shown to be an effective stratification method in previous studies (e.g., <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085993#pone.0085993-Asner3" target="_blank">[17]</a>). Thereafter, the input variables were subset by quantiles to determine bin ranges for the bands. These class combinations were subsequently intersected with a 134-class habitat map as described in the methods, resulting in 8,035 unique classes within the focal area of the present study. A hard bracket indicates values “greater than or equal to”, while a parenthesis indicates values that are “less than”.</p

Performance of three modeling techniques as assessed in 36 validation cells.

<p>Left panels highlight model performance against LiDAR-observed aboveground carbon density from CAO aircraft data (Mg C ha⁻¹), while right panels highlight the model performance by increasing distance from CAO aircraft data. The color-scale reflects the two-dimensional density of observations, adjusted to one dimension using a square root transformation.</p

Three side-by-side carbon map comparisons.

<p>(a) Stratification minus Random Forest without position information, (b) Stratification minus Random Forest with position information, (c) Random Forest without position information minus Random Forest with position information. Areas consistently lower when position information is included (yellows in b and c) are largely low, inundated swamps and wetlands or mid-elevation pasturelands—all of which maintain high levels of photosynthetic vegetation cover (PV) but are comprised of lower carbon stocks in the airborne LiDAR data. Light blue areas (in b and c) are mostly low-elevation floodplain forests. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085993#s4" target="_blank">Discussion</a> section regarding two annotated regions.</p

Predicted carbon stocks using three different methodologies.

<p>(a) Stratification and mapping of median carbon stocks in each class, (b) Random Forest without the inclusion of position information, (c) Random Forest using additional model inputs for position.</p