The dorsal surface of the thorax was partially dissected to expose the VNC.

During nerve stimulations and heat stimulations, PERin dendrites were imaged at 1.1 Hz (nine 1 μm Z-sections at 100 ms/μm) on a 3i spinning disk confocal system, using a 20× water objective and 2× optical zoom. For the heat stimulus, a custom heat probe was placed directly under the fly, and the temperature was ramped to 36°C while imaging. For channelrhodopsin-2 experiments, PERin dendrites were imaged on a Zeiss PASCAL microscope with a 20× water objective and digital zoom factor of 3, at a rate of ∼4 Hz (56.6 μm thick optical section). Heat maps were generated using ImageJ. The mean of four frames prior to stimulus were used as the baseline fluorescence value. PERin axons were imaged during movement by immobilizing the fly in a manner similar to that previously described for selleck chemicals llc electrophysiology (Marella et al., 2012). The distal segments of the forelegs were removed to prevent them from contacting the bath solution, but otherwise the fly’s legs were allowed to move freely during imaging. Calcium responses were monitored using a 40× water objective and a 3× optical zoom at 3.3 Hz (17.7 μm thick optical section). PERin axons in the SOG were monitored because

leg movement rendered imaging in the ventral nerve cord problematic. Movement of the legs was monitored using a 1800USBPS Palbociclib Penscope (http://1800endoscope.com). Only movement involving all six fly legs was scored as movement. The movie was scored for movement using LifesongX 0.8 (Neumann et al., 1992) and resampled at 3.33 Hz (to match the calcium imaging rate) using zeros and ones to indicate

periods of no movement and movement, respectively. This signal was used to generate correlations (r) between movement and ΔF/F values. All analyses and statistics were performed in MATLAB. The MycoClean Mycoplasma Removal Kit correlation coefficient (R) between the ΔF/F signal and the movement array showed high R values (mean = 0.4559, SD = 0.182). With the exception of one animal, all correlations were highly significant (p < 0.0002). To test if significant R values are an artifact of correlating two highly time-varying signals, we shuffled the data and computed the correlation coefficients for all possible movement array and ΔF/F combinations. The distributions of the R values for congruent correlations (n = 10) and shuffled data (n = 102 – 10 = 90) were compared with a two-sided t test. Student’s t test was used to analyze single comparisons in normally distributed data. Paired t test was used for comparison of spiking responses in the same neuron prestimulus and during stimulation. Fisher’s exact test was used to analyze binomial data. ANOVA was used to analyze multiple comparisons in normally distributed data. Two-way ANOVA was used when there was more than one variable (genotype, temperature or genotype, wax).

35 It is a useful marker, because it provides an indicator of the effectiveness of an intervention in clinical terms. Among children with disability, high levels of effectiveness were apparent in reducing sedentary time and increasing MVPA time as most of the participants displayed such changes

beyond the MDC90 reference. In children without disability, the proportion of participants who showed reduced sedentary time was notably less, and those who manifested increased MVPA time were the minority. The findings of this analysis also lend support to the hypothesis that FMS proficiency could influence PA participation among children with disability to a greater extent than in children without disability.

These findings are deemed consistent with the ICF model, which suggests a bidirectional relationship between the human function components ABT-199 chemical structure of motor proficiency and PA participation.16 Considering the limitations of this pilot study, it would be necessary to implement further research to confirm these findings using alternative study designs (e.g., randomization). Heightened engagement in MVPA is needed to generate the important health benefits associated with PA,39 www.selleckchem.com/products/ly2157299.html and this pilot study suggests that improved FMS proficiency in children with disability could contribute towards achieving this, at least on weekends. The physical impairments typically found in children with CP are known to limit movement,7 and its effect on PA engagement should not come as a surprise. It appears that through skill-specific training that allowed children with CP to become better at moving, PA engagement is possibly heightened.

In the associational analysis of this study, improved movement patterns of children with CP appear to have significant correlations with reduced sedentary time and heightened MVPA time. Interestingly, such associations were not similarly consistent when changes in movement outcomes were considered as only the change in jumping distance was found to be associated with change in sedentary time. This converges with the findings of a previous study on children with CP, which showed that FMS movement patterns, rather than outcomes were Peroxiredoxin 1 stronger predictors of PA.36 Children with CP have been known to require greater energy consumption with locomotion (i.e., walking, running) as a consequence of spasticity and impaired postural control.40 and 41 Improvement in FMS movement patterns could be taken as an indicator of adopting a more energy-efficient movement pattern.42 It is thus possible that when movements are more cost-effective, children with CP may tend to engage in PA more. However, these potential explanations need to be explored further in future research.

05 and < 0.005, respectively, one sample t test comparison to 0 pA∗ms). On average a small buy Ipilimumab reduction in the total charge was observed following the first stimulus (Figure 1E; −216 ± 47 pA∗ms, p < 0.05, one sample t test comparison to 0 pA∗ms). The enhancement of net outward synaptic current by NA could reflect

an increase in inhibitory conductance and/or a decrease in excitatory conductance. NA did not have any effect on the peak amplitude (EPSC1 control: −203 ± 39 pA, NA: −195 ± 31 pA, p = 0.40, n = 5) or short-term facilitation (EPSC2/1 control: 1.73 ± 0.27, NA: 1.69 ± 0.28; p = 0.59; EPSC3/1 control 1.92 ± 0.77, NA: 1.93 ± 0.38, p = 0.93, n = 5) of evoked parallel fiber EPSCs recorded from fusiform cells (inhibitory transmission blocked with 10 μM gabazine, 0.5 μM strychnine) (see Figure S1 available online). Thus, NA specifically altered inhibitory input to fusiform cells. In addition to the enhancement of stimulus-evoked inhibitory postsynaptic currents (IPSCs), we also observed that NA sharply reduced spontaneous IPSCs (sIPSCs) recorded in fusiform cells (Figure 2A). Application of NA (10 μM) significantly decreased both frequency (Figure 2B; mean frequency control: 93.0 ± 8.2 Hz, NA: 15.3 ± selleck chemicals 3.9 Hz; p < 0.001, paired t test, n = 6) and peak amplitude (Figure 2C;

control 78.9 ± 6.5 pA, NA 46.6 ± 4.3 pA; p < 0.01, paired t test, n = 6) of spontaneous events in all cells tested. The opposing effects of NA upon spontaneous and parallel fiber stimulation-evoked IPSCs led to a dramatic Ketanserin shift in the balance between these two modes of inhibitory input. In control, sIPSCs occurred frequently and often had amplitudes similar to those evoked by parallel fiber stimulation (Figure 3A, top). In the presence of NA, the near elimination of spontaneous IPSCs together with the enhancement of stimulus-evoked IPSCs resulted in a marked difference between stimulus-driven versus background currents (Figure 3A, bottom). To

quantify the change in background input produced by NA, we measured root-mean-square (rms) values of individual current sweeps over a 250 ms period just prior to parallel fiber stimulation (left side of Figure 3A). NA (10 μM) significantly reduced the rms of background currents (Figure 3B; control: 33.06 ± 4.45 pA, NA: 13.79 ± 1.23 pA, p < 0.005, n = 6). We quantified the change in relative amplitudes between evoked and spontaneous currents by dividing evoked IPSC peak amplitudes by the rms of background currents (signal-to-noise ratio). Signal-to-noise of the first parallel fiber stimulus was not significantly changed between control and NA (1.36 ± 0.50 and 2.83 ± 1.36, respectively; p = 0.16), but NA application resulted in a 7-8-fold change in signal-to-noise ratios for the second and third stimuli in a train (stim 2 control: 3.3 ± 1.3, NA: 23.2 ± 6.9, p < 0.02; stim 3 control 2.8 ± 0.7, NA: 22.1 ± 3.

Weak acid transport was tested using a modification of the method described by Stratford and Rose (1986). Exponentially-growing yeast cells, Z. bailii (NCYC 1766), were obtained from 40 ml shaken cultures, YEPD pH 4.0, at OD 1.65–2.2. Sub-populations were grown in 6 mM sorbic acid for five days as described in Section 2.7. Yeast concentrations were determined by optical density and converted to dry weight using calibration curves. The uptake medium consisted of 6 ml yeast growth culture in YEPD equilibrated

at 25 °C for 3 min. Uptake was initiated by addition of acetic acid (30 mM final concentration) and 5 μCi 14C-acetic acid (PerkinElmer, UK). Samples, 1 ml, were removed over 1–10 min, and were rapidly filtered through 28 mm cellulose nitrate filters, pore size 0.45 μm. Filters were pre-washed with 3 ml YEPD containing 30 mM acetic acid pH 4.0 (no 14C).

Immediately after sample filtration, filters were again rapidly washed Compound C chemical structure with 3 mls YEPD containing 30 mM acetic acid, pH 4.0. Filters were placed into 5 ml ScintiSafe 3 liquid scintillation cocktail (Fisher Scientific, UK) and samples were counted using Selleckchem INCB28060 a Packard TRI-CARB 2100 TR liquid scintillation analyser. A total of 38 strains of Z. bailii were initially tested, firstly to confirm preservative resistance, secondly to select typical strains, and thirdly to examine variations in preservative resistance between strains. Strains were selected from a global distribution, no predominantly from a variety of spoiled foods and beverages ( Table 1) but also included factory isolates and strains from fermented Kombucha tea, which frequently contains high levels of acetic acid. The identity of all strains was confirmed as Z. bailii by D1/D2 rDNA sequencing

( Kurtzman, 2003). Two strains of S. cerevisiae were also included as reference strains. Previous research had shown these strains to be typical representatives of S. cerevisiae with respect to weak-acids ( Stratford et al., 2013). Tests were carried out on the resistance of strains to sorbic, benzoic and acetic acids in YEPD at pH 4.0 ( Table 1). Results showed variation in the resistance of Z. bailii strains to sorbic acid, MIC from 4.5 mM to 9.5 mM, MIC of benzoic acid 6.3 mM to 11 mM and the MIC of acetic acid, from 275 mM to 580 mM. In all strains examined, sorbic acid inhibited growth at a much lower concentration than acetic acid. The mean Z. bailii MIC of sorbic acid was 7.1 mM at pH 4.0, benzoic acid MIC 8.75 mM and mean acetic acid MIC was 466 mM. The resistance of S. cerevisiae strains to preservatives was far lower, with MICs in the region of 3 mM for sorbic acid or benzoic acid and 130 mM for acetic acid. The origin of yeast strains appeared unrelated to their preservative-resistance characteristics. Overall, this confirms that all strains of Z. bailii tested showed extreme resistance to sorbic, benzoic and acetic acid, and enabled selection of typical representative strains. Tests were carried out using a single strain of Z.

g., Petrovich, 2011). Specifically, areas of the amygdala (LA, BA, ABA) RGFP966 process these learned cues associated with food and relay them to the LH. Such cues, if sufficiently potent, can stimulate eating in animals that are sated. Feeding does not occur in a vacuum. As noted above, when threat levels rise, feeding is suppressed (Gray, 1987, Lima and Dill, 1990, Blanchard et al., 1990 and Fanselow, 1994). For example, a tone previously paired with shock inhibits feeding (Petrovich, 2011)

and food-motivated instrumental behavior (e.g., Cardinal et al., 2002). Connections from the basolateral amygdala to the LH facilitate feeding by a CS associated with food, while the suppression of feeding by an aversive CS involves outputs of the CEA. The exact target remains to be determined but CEA connects with LH both directly and indirectly (Petrovich et al., 1996 and Pitkänen

et al., 1997). While threat processing normally trumps feeding, at some point the risk of encountering harm is balanced against the risk of starvation. A similar case can be made for the suppression of other behaviors by threat processing. For example, medial amygdala areas that process threat related odors suppress reproduction via connections Gefitinib cell line to VHM reproductive circuits (Choi et al., 2005). The fact that the amygdala contributes to appetitive states (e.g., Rolls, 1999, Rolls, 2005, Everitt et al., 1999, Everitt et al., 2003, Gallagher and Asenapine Holland, 1994, Holland and Gallagher, 2004, Cardinal et al., 2002, Baxter and Murray, 2002 and Moscarello et al., 2009) as well as defense (see above) does not mean that the amygdala processes food and threat

related cues in the same way. Similarly, the fact that both appetitive and aversive stimuli activate the amgydala in fMRI studies (e.g., Canli et al., 2002, Hamann et al., 2002 and Lane et al., 1999) does not mean that these stimuli are processed the same by the amygdala. Recent unit recording studies in primates show that appetitive and aversive signals are processed by distinct neuronal populations of cells in the lateral/basal amygdala (Paton et al., 2006, Belova et al., 2007, Belova et al., 2008, Morrison and Salzman, 2010, Ono and Nishijo, 1992, Rolls, 1992, Rolls, 1999 and Rolls, 2005). Molecular imaging techniques with cellular resolution show that similarities in activation at the level of brain areas obscures differences at the microcircuit level (Lin et al., 2011). Because different groups of mammals faced different selective pressures, the behavioral responses controlled by conserved survival circuits can differ. As ethologists have long noted, many survival-related behaviors are expressed in species-specific ways (e.g., Tinbergen, 1951, Lorenz, 1981 and Manning, 1967). Consider escape from a threat. We’ve seen evidence for conserved defense circuits across mammals and even across vertebrates, but behavioral responses controlled by these circuits can differ dramatically.

A tenet of the proposal

is that particular misfolding-prone Trichostatin A molecular weight proteins may accumulate upon cell stress in or near the vulnerable neurons (first vulnerability), to then selectively interfere with neuronal function and cause more neuronal stress due to vulnerability to misfolding protein targets in those neurons (second vulnerability). The presence of such specific vulnerability combinations in particular neurons would thus favor proteostasis instability through vicious cycles involving cell stress and misfolding protein targets. In suggesting that stressor levels have a critical role throughout disease, the model differs from views that alterations in cellular stress pathways in neurons are just late consequences of disease. The model implies the following: • NDDs may be initiated by chronic perturbations acting at any of several critical components of cellular homeostasis pathways in vulnerable cells. We first provide a general overview of cellular stress and homeostasis regulatory pathways and then review main features of NDDs and how they may be accounted for by a stressor-threshold model of selective neuronal vulnerabilities. All cells are endowed with homeostatic regulatory mechanisms to cope with altered physiological demands, survive periods of intense stress, adapt to milder but chronic stress, or self-destroy.

Cells can experience different types of stress, including protein misfolding, high biosynthetic or secretory Protein Tyrosine Kinase inhibitor demands, alterations in redox balance (e.g., oxydative stress), alterations in organellar calcium, inflammatory reactions, caloric restriction, and aging (Mattson and Magnus, 2006, Lin et al., 2008, Hotamisligil, 2010, Rutkowski and Hegde, 2010 and Roth and Balch, 2011). The cellular homeostasis processes that respond to cell stress include combinations of specific pathways that deal with particular stressors (Rutkowski and Hegde, 2010 and Roth and Balch, 2011). Not surprisingly,

Resminostat these pathways are highly interconnected, leading to extensive crosstalk and comorbidities among them. Notably, however, in spite of the great variety of specific cellular homeostasis responses, the stress sensors associated with the endoplasmic reticulum (ER) membrane system seem to have central roles in orchestrating cell adaptions to altered physiological demands and in response to stressors (Bernales et al., 2006, Lin et al., 2008 and Rutkowski and Hegde, 2010). Such uniquely central roles likely relate to the fact that the ER has major biosynthetic and secretory roles, is distributed throughout the internal volume of cells, and exhibits specialized interfaces with other membrane organelles such as the nucleus, mitochondria, the Golgi apparatus, lysosomes, phagosomes, and the plasma membrane, where stress signals can be exchanged.

Using statistical correlation analysis, we found strong evidence that there is a one-to-one correspondence between the healthy network’s

eigenmodes and atrophy patterns of normal aging, AD, and bvFTD. Interestingly, these eigenmodes also recapitulate recent findings of dissociated brain networks selectively targeted by different dementias (Seeley et al., 2009, Zhou et al., 2010, Buckner et al., 2005 and Du et al., 2007). This may help provide a systemic explanation for the network degeneration theory, hitherto unexplained, as a simple consequence of network dynamics. The network diffusion model can accurately infer the population-wide prevalence rates of various dementias and can explain, why bvFTD has higher prevalence than AD in early stages, and why it subsequently becomes much less

prevalent than AD. There is no need to invoke region-specific neuropathy, selleck chemicals e.g., mesial temporal origin (Braak et al., 2000), or selective vulnerability within dissociated functional networks (Seeley et al., 2009). This implies that all dementias, hitherto considered pathophysiologically and etiologically distinct, might share check details a common progression mechanism. We demonstrate the role of network eigenmodes as biomarkers and as highly effective basis functions for dimensionality reduction, classification, and automated differential diagnosis. This might be especially advantageous for heterogeneous and mixed dementia, which are poorly served by classically described clinical phenotypes. Most important, the model provides a clear path for predicting future atrophy in individuals starting from baseline scans. Figure 1 provides an overview of the datasets and processing steps, with network analysis using 14 healthy young subjects (left panel) and volumetric analysis of T1-weighted MRI scans of 18 AD, 18 bvFTD, and 19 age-matched normal subjects Cell (right panel). The t-statistic of cortical volumes of AD and bvFTD patients, normalized by

young healthy controls, are shown in Figures 2 and 3 as wire-and-ball plots, along with the values of two eigenmodes of the healthy network evaluated at the same brain regions. The wires denote network connections, the size of each ball is proportional to the atrophy level in that region of interest (ROI) (normalized by ROI size), and the color denotes lobar membership. ROIs showing negative atrophy are considered statistical noise and are not shown. T-scores of cortical atrophy as well as eigenmodes are shown in Figure 4 mapped on the cortical surface of the 90-region cerebral atlas. Extreme levels (±2 SD from mean values) were capped to aid visualization. Since the colors are uniform within each ROI, the apparent spatial resolution of these surface renderings may be somewhat deceptive.

Two male adult macaque monkeys were used in this study. Standard operant conditioning techniques were used to train the subjects to fixate and to press buttons for a small liquid reward. Eye movements were recorded using the EyeLink II video tracking system Proton pump inhibitor (SR Research, Osgoode, Ontario, Canada) running at 500 Hz. When the monkeys were ready for recordings, we implanted custom chambers that allowed for a dorsal access to ITC (Horsley-Clark coordinates, +15 anterior, +20 lateral). Based on reconstructed

electrode trajectories, we believe most of our recordings took place from the lateral convexity of ITC, ventral to the lower bank of the superior temporal sulcus (STS) and lateral to the perirhinal cortex (Figure S2). Recordings were obtained with fine tungsten microelectrodes (Alpha Omega Engineering, Alpharetta, GA, USA, or Frederic Haer Company, Bowdoinham, ME, USA). Single units were isolated online using a threshold and dual-amplitude windows, while analog signals were streamed to disk for offline

analysis. All stimuli used were taken from Hemera Photo-Objects Vols. 1, 2, and 3 (Hemera Technologies), subtended about 2° × 2° of visual angle at a viewing distance of 90 cm, and were presented centrally on top of a uniform gray background. Both monkeys were familiarized with the same set of 125 stimuli (Figure S1A). During the familiarization phase the monkeys saw the images in either a passive fixation task or in a delayed match-to-sample task. When the familiarization click here phase was completed, we began the recordings. All recordings were obtained during a passive fixation task in which eye position was constrained to be within 1° of the center of the screen, as ten stimuli (no repeats) were presented. At the end of the stimulus presentation epoch, an extrafoveal square target was presented (eccentricity = 6°) to which the monkey had to saccade to obtain its juice reward. Because the goal of this experiment was to compare neuronal responses to familiar and novel stimuli, for every recording

session we selected a new set of 125 never before seen stimuli. Although the selection process Ribavirin was random, we used the scale invariant feature transform and the dot product of normalized color histograms to eliminate from this novel set stimuli which looked either too similar to the familiar ones or to one another (see Supplemental Experimental Procedures). We attempted to record from every well-isolated and visually responsive unit in ITC. To avoid a neuronal selection bias, the vast majority of visually responsive units (n = 40/50, 80% for monkey D; n = 35/38, 92% for monkey I) were found and isolated with an independent set of 50 initially novel stimuli that gradually became familiar as the recording sessions accumulated. Thus, the results presented here are not a consequence of selecting units that we knew ahead of time would be responsive to familiar items.

In a second step, we checked the fits to the Hill function by eye to ensure they gave us reasonable estimates for I1/2 and the Hill coefficient. To calculate the release rate in a bipolar cell terminal we begin with the following relation: equation(Equation 11) dNoutdt=Vexo(t)−Vendo(t)where Nout is the number of vesicles fused to the terminal membrane and Vexo and Vendo are the speeds of exocytosis LBH589 research buy and endocytosis, respectively. Because equation(Equation 12) Vendo(t)=kendo·Nout(t),Vendo(t)=kendo·Nout(t),the speed of exocytosis is equation(Equation 13)

Vexo(t)=dNoutdt+kendo·Nout(t)where kendo is the rate-constant of endocytosis, which has been measured to be ∼0.1 s−1 during ongoing activity in isolated bipolar cells (Neves and Lagnado,

1999) and in vivo (Figure 3B). Fast endocytosis (∼1 s) will not contribute significantly to these estimates because it has a limited capacity and primarily operates on vesicles LY2109761 concentration released within the first tens of milliseconds of a large calcium transient (Neves et al., 2001). Further, the fluorescence of the pHluorin is quenched with a time constant of 4–5 s only after endocytosis, reflecting the time required for reacidification of the interior of the vesicle by the

proton pump ( Granseth et al., 2006). Decay of the sypHy signal with a time constant of 4–5 s was not observed ( Figure 3B), consistent with the fast mode of retrieval being very small compared to the much larger number of vesicles retrieved with a time constant of 10 s. We assume that vesicles are in one of two states; internalized and quenched (with unitary fluorescence, Fvq), and released and unquenched (Fvu). A number of studies using pHluorin-based reporters have also demonstrated a standing pool of unquenched Etomidate reporter on the cell surface (Granseth et al., 2006), so the total sypHy fluorescence F at time t was assumed to be the sum of these three different sources of fluorescence, as follows: equation(Equation 14) F(t)=(Nout(t)⋅Fvu)+((Ntotal−Nout(t))⋅Fvq)+(Ntotal⋅αmin⋅Fvu)F(t)=(Nout(t)⋅Fvu)+((Ntotal−Nout(t))⋅Fvq)+(Ntotal⋅αmin⋅Fvu)where αmin is the fraction of vesicles “stuck” on the terminal membrane and not involved in the vesicle cycling process, and Ntotal is the total number of vesicles in the terminal. We estimated αmin and Ntotal as described below. Equation 14 can be arranged to equation(Equation 15) Nout(t)=F(t)−(Ntotal⋅(Fvq+(αmin⋅Fvu)))Fvu−Fvq Because Fvq = Fvu/20 (Sankaranarayanan et al.

We will also be hosting several anniversary events at SFN. Neuron has organized an SFN satellite meeting,

The Networked Brain, part of the Cell Symposia series, and we are still accepting registration to the meeting (http://www.cell-symposia-networkedbrain.com). The speaker list is outstanding and we hope you can join us at this pre-SFN satellite meeting. In addition, Neuron and Cell Press, in Selleckchem 3-Methyladenine collaboration with Zeiss, will be a hosting a roundtable discussion, “The State of The Mind: A Conversation about Neuroscience Today and Tomorrow,” on Saturday, November 9th. Advance registration is required for this event, but if you can’t make it to the roundtable discussion, the event will be videotaped and webcast at a later date, so stay tuned. In closing, a key to the vision and success of Neuron find protocol has always been the neuroscience community, and it is a true privilege for Neuron and Cell Press to be a part of this community. We are grateful to our authors—thousands

of you over the years—who have entrusted the journal with your best work; to our Editorial Board members, for acting as trusted advisors to the journal; to the reviewers, who have provided thoughtful, fair, and constructive feedback; and of course, to all of our readers. Neuroscience has taken off in spectacular ways in the last 25 years and we feel lucky here at Neuron to have been along for the ride! “
“Figure options Download full-size image Download high-quality image (61 K) Download as PowerPoint slideThe individual on the cover is Endel Tulving, Professor Emeritus at the University of Toronto and one of the most influential memory researchers in experimental psychology. Our Neuron findings contradicted a prominent theory of memory lateralization put forth by Dr. Tulving and colleagues that argued for a left-hemisphere bias when encoding

information into memory and a right-hemisphere bias when retrieving information from memory. With Dr. Tulving’s permission, we thought it would be entertaining to display the contradictory findings directly in his head. At the time, Dr. Tulving was a Visiting Professor of Psychology with us at Washington University, St. Louis. The chair of Psychology, Roddy Roediger, a former colleague of Dr. Tulving, approached see more him on our behalf about the cover idea. According to Roddy, the exchange went something like the following. Roddy: “Endel, how would you like to be on the cover of Neuron? Not your research, but your actual picture. I’m not in a position to guarantee it, but I can suggest it.” Endel: “How much do I have to pay to get myself on the cover?” —Steven Petersen and William Kelley Figure options Download full-size image Download high-quality image (76 K) Download as PowerPoint slideWe originally presented several cover ideas, all following the themes of snakes, toxins, and the brain.