First, changes “across sleep” were defined as differences between the first and the last non-REM episodes in a sleep session. Second, changes in “within non-REM” episodes refer to differences between the first and the last thirds of each non-REM. Third, changes in “within REM” episodes refer to differences between the first and the last thirds of each REM. Finally, we examined the relationship between these categories. Since non-REM sleep is characterized by alternating periods

of population activity and inactivity in both the neocortex (Steriade et al., 1993) and hippocampus (Ji and Wilson, 2007; Isomura et al., 2006), we defined active periods as those in which smoothed gamma and epsilon band (30–300 Hz) LFP activity was at

least 0.5 SDs above the mean for at least 50 ms. Conversely, inactive periods mTOR inhibitor were detected as those in which gamma and epsilon band activity was 0.5 SDs below the mean for at least 50 ms (see Supplemental Experimental Procedures, see also Figure S2 for an analogous spike-based analysis). The incidence of active periods decreased, whereas the incidence of inactive periods increased significantly from the first to the last non-REM episodes of each session (i.e., across sleep; Figure 1B; Table S1). The firing rates of both pyramidal cells and interneurons decreased significantly across sleep (Figure 1B). These findings are in OTX015 cell line accord with the two-process model of sleep and indicate similarities between sleep-related activity of neurons between the neocortex and hippocampus (Borbély, 1982; Tononi and Cirelli, 2006; Vyazovskiy et al., 2009). During sleep, the Calpain hippocampal neural population fires synchronously during sharp-wave ripple events and relatively asynchronously between ripples (Buzsáki et al., 1992). The discharge rate of pyramidal neurons between ripples decreased significantly across sleep (Figure 1B), similar to the decrease in global firing rate. Conversely, the mean firing rate

of pyramidal cells within the short-lived ripple events increased during the course of sleep (Figure 1B). This increase in ripple-related activity across sleep was the result of an increase in the percentage of ripples within which pyramidal cells participated (i.e., fired at least one spike) rather than an increase of the within-ripple firing rates of individual neurons in individual ripples (Figure 1B; Figure S3). Concurrent with the increase of within-ripple participation, the coefficient of variation of within-ripple firing rate across cells decreased (Figure 1B; Figure S3), suggesting that the within-ripple participation was more evenly distributed across the population of pyramidal cells at the end compared to the beginning of sleep. Synchrony, as measured by the correlation strength of pyramidal cell pairs in nonoverlapping 100 ms bins (Wilson and McNaughton, 1994), also increased across sleep (Figure 1B), probably due to the more consistent participation of pyramidal cells in ripples.

Importantly, when the frequency was increased to 200 Hz, just 3 to 5 stimuli were sufficient to achieve

charge transfer comparable or even stronger check details than in the control (AAV-EGFP) neurons, although the onset of the response was delayed by several milliseconds. Thus, while the temporal precision of transmission suffered, downstream neurons still responded to high-frequency spikes. Even long-term potentiation was retained in Syt1-infected animals. When the mice were tested in a contextual fear conditioning paradigm, the results with TetTox injections largely confirmed previous investigations using more traditional methods. Recent memory was impaired in animals with the virus injected in the hippocampus and entorhinal cortex, whereas remote memory (tested Selleck BMS907351 several weeks after fear conditioning and the virus injection) was affected only in the prefrontal group. However, the results with Syt1-infected mice were surprising. While recent fear memory was seriously impaired after entorhinal

Syt1 knockdown, Syt1 hippocampal mice performed just like the controls. Animals with Syt1 infections in the prefrontal cortex were comparable to their TetTox peers. In summary, high-pass frequency filtering of spikes by Syt-1 did not matter much in the hippocampus but was devastating in both the entorhinal cortex and prefrontal cortex. On the basis of these spectacular findings, Xu and colleagues (2012) suggest that different spike coding mechanisms are at work in the three different brain Oxygenase regions. Hippocampal circuits can rely on bursts of spikes only, whereas the paleo- and neocortex networks need high temporal precision of single

spikes for coding, at least for the mediation of contextual fear memory. The authors’ account of their findings may indeed be right. Yet, one might also consider the possibility that it is not necessarily the precision of spikes that matters, but rather the extent to which each structure is able to communicate via high frequency bursts, and thus overcome the genetic manipulation. As the authors point out, cortical neurons can fire both single spikes and complex spike bursts and the bursts may be critical for spike transmission under certain conditions (Lisman, 1997). Unfortunately, there is no natural frequency border between single spikes and spike bursts and the interspike interval statistic reflects a renewal process where spiking history is critical (Harris et al., 2001). Traditionally, a spike burst is defined as three or more spikes with < 8 ms intervals (Ranck, 1973). In the hippocampus, spike doublets and triplets of pyramidal cells at such short intervals occur 14% and 3% of all spikes during exploration. A burst of 4 spikes is rare (0.4%) and 5 or more spikes is super rare (0.06%) although these fractions can increase several-fold during sleep.

, 2010) in a side-by-side comparison. The comparison indicates a vertical motion of the S4 that is about 7–10 Å, as measured at the Cα atom of R1, though it should be noted that the estimate of vertical motion selleck screening library of the S4 helix depends on how it is defined and aligned with respect to the rest of the structure (see Experimental Procedures). Without additional information, it is prudent to envision a range of values for the vertical motion in order to reflect the uncertainty among the different models depicted in Figure 2 (see also Figure S1) and the thermal fluctuations estimated by MD simulations (see Figure S2). The displacement of S4 is considerably

larger than the 1–2 Å initially proposed on the basis of lanthanide resonance energy transfer (LRET) measurements (Cha et al., 1999). Nonetheless, the displacement of S4 in the model is consistent with the LRET results, once the molecular structures of the donor Luminespib chemical structure and acceptors with their linker are taken into account. Similarly, the motion is considerably smaller than the vertical motion typically associated

with the paddle model, originally proposed to be 20–25 Å on the basis of biotin-avidin trapping data obtained with the KvAP bacterial channel (Jiang et al., 2003). However, further analysis shows that the movement displayed by the S4 helix in the consensus model is actually consistent with the biotin-avidin trapping data (Figure 4). It is also important to keep in mind that those conformational states are dynamic Thymidine kinase and undergo significant fluctuations (Figure S2). Although the three idealized models proposed to explain voltage sensing are often contrasted by the magnitude of the S4 movements, this is clearly an oversimplification. For example, the helix-screw/sliding-helix model pictures predominantly a rigid body motion of S4. However, available X-ray structures and several independent MD simulations provide support

for the intriguing possibility that voltage sensing might be accompanied by a transformation of S4 from an α helix to a 3-10 helix in the resting state (Long et al., 2007, Clayton et al., 2008, Villalba-Galea et al., 2008, Bjelkmar et al., 2009, Khalili-Araghi et al., 2010 and Vieira-Pires and Morais-Cabral, 2010). Indeed, in our own consensus model, the S4 segment retains a portion of the 3-10 helix that was exhibited in Khalili-Araghi’s model. Similarly, one implication of the paddle model is that the helix-turn-helix S3-S4 moves together as a rigid body. Such concerted motion is observed neither in the consensus model nor in experiments. Based on the disulfide-bond pattern of cysteine pairs substituted between S3 and S4 in Shaker, Broomand and Elinder (2008) concluded that the two helices can move relative to each other. Therefore, although the consensus model recapitulates many of the suggestions embodied by the three idealized models, some specific details from those models are not supported.

A more sophisticated strategy

that is evolving, is to target several different but key proteins in the chlamydial repertoire. Chlamydia has evolved over its long history to have multiple mechanisms of infecting and controlling its host and hence a vaccine that does not rely on a single target has the best chance of success. To this end, the concept of targeting several surface proteins (such as MOMP, Pmps, Incs) as well as some internal or secreted regulatory proteins (such as CPAF, NrdB) has significant merit ( Fig. 1 (a) summarizes the antigens related to each stage of the chlamydial developmental cycle, and Table 2 shows how these might be combined effectively in mTOR inhibitor multi-antigen vaccines). learn more In addition, specifically targeting antigens that are more highly expressed in the persistent or chronic

phase of infection/disease, has considerable merit. While the major goal of a chlamydial vaccine is to prevent infection in naive individuals, it may not be possible to screen all vaccinees to ensure they are negative prior to vaccination. In addition, if sterilizing immunity is difficult or impossible to achieve, then including persistence phase antigens in a vaccine would have significant merit. Such multi-target vaccines are well within the reach of current technologies and clearly are successful with other infectious disease vaccines, such as meningococcal disease vaccines. All candidate antigens though require effective adjuvants and the optimal delivery mechanism to be an effective vaccine. The challenge with a C. trachomatis STI vaccine is that the vaccine-adjuvant combination must elicit Phosphoprotein phosphatase the correct balance of Th2 (neutralizing antibodies) and Th1 (IFN-g and Th17 cytokines) responses and it must do this at the required mucosal sites (female genital tract). Thanks to recent progress

in vaccinology and immunology more broadly, the range of adjuvants that are now available, and well advanced in human safety trials [89] is rapidly increasing and some promising results with C. trachomatis vaccines are emerging. The range of adjuvants and delivery systems that have been evaluated with C. trachomatis vaccines include immunostimulating complexes [88] and [90], detergent/surfactant-based adjuvants [91], live viral vectors [92], Vibrio cholerae ghosts [93], liposomes [ [94], CpG and their more recently developed, safe derivatives [88] and cytokines. One challenge for chlamydial vaccine development is whether it should (i) primarily aim to significantly reduce or even eliminate the infection, or (ii) should also, or perhaps only, aim to reduce or eliminate the adverse pathology, in particular upper genital tract pathology in females.

, 2009), and irradiation ( Mexis et al., 2009, Narvaiz et al., 1992 and Prakash et al., 2010). Compared Protease Inhibitor Library supplier to hydro, thermal, and chemical methods, ionizing radiation has the advantage of retaining the functional quality of nuts. However, radiation efficacy varies among studies due to different sample conditions during treatment. Although low water activity (aw) is one key to controlling microbial growth, it actually presents a significant impediment to microbial inactivation. According to Laroche et al. (2005), the thermal inactivation rates for Saccharomyces cerevisiae and Lactobacillus plantarum are not monotonically dependent on initial aw (0.10–0.70).

Additionally, the effect of aw on thermal resistance of Salmonella Typhimurium varied with solute type (glycerol, sucrose, glucose, or polyethylene glycol) ( O’Donovan-Vaughan and Upton, 1999). Given that the surrounding humidity can alter the surface aw of nuts during processing and storage, it is important to quantify inactivation rates as a function of this critical variable. Therefore, the objectives of this study were to: (1) quantify the relationship between aw and the D10-value for low-energy X-ray inactivation of Salmonella on almonds and walnuts, (2) quantify post-irradiation survival of Salmonella on nuts during storage, and (3) determine the impact of X-ray irradiation on the sensory quality. Shelled

raw whole almonds (Nonpareil) and walnuts (Juglans regia) from the 2009 crop were purchased in a single lot of each from retail sources located in California. Upon acquisition, 200 g of each nut type were vacuum-packaged and stored at 4 °C until testing. UMI-77 mw Kernel and bulk density were measured in a graduated cylinder Calpain using the platform scale method (AOAC 971.25) ( AOAC, 2000). For moisture content determination, samples were ground using a IDS55 coffee bean grinder (Mr. Coffee, Cleveland, OH) for ~ 30 s, and ~ 2 g (5 replications) was dried in an oven at 102 °C to constant weight (~ 48 h). An FP-200 Nitrogen Analyzer (Leco Corp., St. Joseph, MI: compliance with AOAC 990.03) was used for protein

analysis, and a Soxhlet fat extractor was used according to AOAC 948.22 ( AOAC, 2000) for total fat content. Salmonella Enteriditis PT30 (SE PT30), originally isolated from raw almonds implicated in the 2000 and 2001 outbreaks, was previously obtained from Dr. Linda Harris (University of California — Davis) and preserved at − 80 °C in Tryptic Soy Broth (TSB) (Difco, Becton Dickinson, Sparks, MD) containing 20% glycerol. To compare serovars, three strains of Salmonella Tennessee (S13952, S13972, and S13999) were obtained from the Food Science and Human Nutrition culture collection at Washington State University (Pullman, WS) and preserved under the same condition as SE PT30. The inoculation procedure of Danyluk et al. (Danyluk et al., 2005) was followed with slight modifications.

elegans Unc-79 and Unc-80, respectively). First, mutations in Unc79 and Unc80 in Drosophila, C. elegans, and the mouse have essentially identical phenotypes to those of Nalcn mutants. Second, mutation in one of these genes also affects the protein levels and/or localization of the others ( Humphrey et al., 2007, Jospin et al., 2007, Nakayama et al., 2006, Pierce-Shimomura et al., 2008 and Yeh et al., 2008). Defining evidence that UNC79 and UNC80 form a physical complex with NALCN came from coimmunoprecipitation experiments in mouse brain showing that antibody

against any one of the three could bring BKM120 cell line down the others ( Lu et al., 2009 and Lu et al., 2010). Whether there are other core subunits in this complex awaits the purification of the protein complex and a determination of the subunit composition. UNC79 and UNC80 are well conserved among animals but share no obvious sequence similarity with any other protein with known function (Humphrey et al., 2007, Jospin et al., 2007,

Lu et al., 2009 and Lu et al., 2010). Both are large proteins (2654 aa and 3326 aa, respectively, in humans), larger than any known auxiliary subunit in the 24-TM channel family. Despite their size, there are no obviously identifiable Selleck Pfizer Licensed Compound Library protein domains in UNC79 and UNC80. The lack of sequence similarity among the auxiliary subunits of the NALCN, NaVs, CaVs, and CatSper suggest that, unlike the pore-forming subunits, the auxiliary subunits evolved independently. In heterologous expression systems, UNC80 and NALCN have been shown to interact (Lu et al., 2009 and Lu et al., 2010). UNC79 and UNC80 also associate with each other, and UNC79 requires the presence of UNC80 to associate with NALCN. These data suggest a NALCN complex model whereby UNC80 serves as a bridge between UNC79 and NALCN (Figure 4). In Unc79 knockout mouse brain, UNC80 protein is also undetectable, but NALCN is present. Unc79 mutant neurons also retain the NALCN-dependent basal Na+ leak current ( Lu et al., 2010). Unlike in wild-type neurons, however the current in the mutant is not regulated by G protein-coupled receptors (GPCRs; see

below). However, the GPCR regulation of NALCN can be restored by overexpression of UNC80 in the Unc79 knockout, suggesting that UNC80 is not required for the basal function of NALCN, but is required for the regulation of the channel ( Lu et al., 2010). Consistent with this idea, NALCN alone forms a leak channel when transfected into HEK293 cells, but its regulation by several GPCRs requires the cotransfection of UNC80 ( Lu et al., 2010). The function of UNC79 is less clear. The whole-cell basal Na+ leak is of a similar size in neurons cultured from the wild-type and in neurons from the Unc79 knockout mouse, and overexpression of UNC80 in an Unc79 null background restores the regulation of NALCN, suggesting that this channel’s biophysical properties in mouse brain, as they are currently understood, do not require UNC79 ( Lu et al., 2010).

(1995). Zebrafish-handling procedures were approved by Institute of Neuroscience, Chinese Academy of Sciences. For in vivo electrophysiological recording and time-lapse two-photon imaging, learn more zebrafish aged at 3–6 or 15–20 dpf were immobilized with the neuromuscular junction blocker α-bungarotoxin (100 μg/ml), mechanically fixed by 1.0% low melting point agarose, and incubated in HEPES-buffered saline containing 134 mM NaCl, 2.9 mM KCl, 2.1 mM

CaCl2, 1.2 mM MgCl2, 10 mM HEPES, and 10 mM glucose (pH 7.4). The lens of the eye was removed to expose the surface of the RGC layer (Figure 1B). In vivo perforated whole-cell recording of cells at the ganglion cell layer was made under visual control at room temperature (Zhang et al., 2010). In zebrafish larvae, displaced amacrine cells in the ganglion cell layer are rarely observed (Connaughton et al., 1999; Kay et al., 2001; Zhang et al., 2010), and most Selleckchem SP600125 of the cells at the ganglion cell layer are RGCs. The recording pipette was made from borosilicate glass capillaries (WPI), had a resistance in the range of 10–12 MΩ, and was tip filled with internal solution and then backfilled with internal solution containing amphotericin

B (200 μg/ml). The internal solution contained 110 mM K-gluconate, 6 mM NaCl, 2 mM MgCl2, 2 mM CaCl2, 10 mM HEPES, and 10 mM EGTA (pH 7.3). Therefore, the ECl− was about −59.8mV. Recording was made with patch-clamp amplifiers (MultiClamp 700B; Axon Instruments). The whole-cell capacitance was fully compensated, and the series resistance (10–20 MΩ) was compensated at 70%–80% (lag 60 μs) and monitored during the experiment. The data were discarded if the series resistance varied by >20% during recording. Signals were filtered at 2 kHz

and sampled at 10 kHz using AxoScope software 10.0 (Axon Instruments). To electrically activate synapses formed by BCs on RGCs, BCs at the INL were extracellularly stimulated by focal extracellular stimulation (duration, 0.1 ms; intensity, 2–40 μA) with theta glass electrodes Adenosine (tip opening, 1–2 μm). Data obtained from RGCs, which only exhibited e-EPSCs, were collected and analyzed. In order to record mEPSCs of RGCs, TTX (1 μM) was included in the bath solution. For whole-field flash and MBSs, a mini LCD (Sony; PLM-A35) was mounted on the camera port of the microscope (BX51WI; Olympus), allowing projection of computer-generated images onto the retina of zebrafish larvae (Figure 1A). Visual responses were evoked by whole-field flashes with a step increase (2 s duration) in the light illumination or moving bars with rightward direction (width, 6 μm; speed, 0.1 μm/ms). All drugs were from Sigma-Aldrich. For in vivo two-photon calcium imaging of BC axon terminals, we used double-transgenic zebrafish Tg(Gal4-VP16xfz43,UAS:GCaMP1.6) larvae that were obtained by crossing Tg(UAS:GCaMP1.6) (gift from Dr.

For all three neuronal populations, 95% of the amplitude of the signal recorded in the soma layer came from neurons within a radius smaller than 200 μm ( Figure 3D). By plotting the LFP amplitude as a function of cortical depth, we further found the largest LFP amplitudes at the soma level ( Figure 3E). We therefore conclude that when the synaptic activity is

uncorrelated, the LFP is rather local, both in terms of horizontal reach and amplitude variation in the vertical direction. Changing the synaptic distributions to either only apical or only basal dendrites for the pyramidal cells gave a different depth dependence for both the reach and the amplitude of the LFP for the L5 population, whereas the results for the L3 population

were largely unaffected (Figures 3D and 3E). For the apically activated L5 population both the LFP amplitude and the spatial reach are similar for the electrode see more contacts positioned in L2/3 and the L5 soma layer (Figures 3D3–3E3). This demonstrates that these qualitative features of the LFP are determined both by the spatial distributions of the synaptic inputs and the neuronal morphology, in particular the depth profile of the total dendritic area (Lindén et al., 2010). We next compared the numerical simulations with predictions of the simplified model: by using the detailed single-cell decay functions f(r) obtained Cyclopamine concentration above ( Figure 2), we numerically integrated the simplified model ( Equation 1).

As seen in Figure 3, the predictions of the simplified model agree excellently with the results of the comprehensive numerical simulations, suggesting that our simplified model indeed captures the salient features of LFP generation from neuronal populations. How do these results change when the synaptic inputs to different cells in the population are correlated? We used the same simulation setup as above with the difference that spike trains to different cells were drawn from a finite pool of presynaptic spike trains (Figure 4A). This induced a mean correlation cξcξ between the synaptic input currents to different cells due to common input. By varying Terminal deoxynucleotidyl transferase the size of the pool of presynaptic spike trains n  pool we could vary the input correlation cξcξ (see Experimental Procedures). As predicted by the simplified model (Equation 1), inducing correlations between single cell LFP contributions changed the total LFP amplitude in three respects: (1) the LFP amplitude σ becomes considerably higher (Figures 4C1–4C3 and 4F1), (2) the reach R∗ of the LFP (as before defined as the population radius where the amplitude had reached 95 % of the value for R = 1,000 μm) generally increases ( Figures 4D1–4D3 and 4E1), and (3) the LFP amplitude σ no longer appears to converge to a fixed value with increasing population radius.

To determine which PCs are significantly different from chance, we compared the semantic PCs to the PCs of the category stimulus matrix (see Experimental Procedures for details of why the stimulus PCs are an appropriate null hypothesis). First, we tested the significance of each subject’s own category model weight PCs. If there is a semantic space underlying category representation in the subject’s brain, then we should find that some of the subject’s selleck chemicals llc model weight PCs explain more of the variance in the

subject’s category model weights than is explained by the stimulus PCs. However, if there is no semantic space underlying category representation in the subject’s selleck chemical brain, then the stimulus PCs should explain the same amount of variance in the category model weights as do the subject’s PCs. The results of this analysis are shown in Figure 3. Six to eight PCs from individual subjects explain significantly more variance in category model weights than do the stimulus PCs (p < 0.001, bootstrap test). These individual subject PCs explain a total of 30%–35% of the variance in category model weights. Thus, our fMRI data are sufficient

to recover semantic spaces for individual subjects that consist of six to eight dimensions. Second, we used the same procedure to test the significance of group PCs constructed using data combined across subjects. To avoid overfitting, we constructed a separate group semantic space for each subject using combined data from the other four subjects. If the subjects share a common semantic space, then some of the group PCs should explain more of the variance in the selected subject’s category

model weights than do the stimulus PCs. However, if the subjects do not share a common semantic space, then the stimulus PCs should explain the same amount of variance in the category model weights as do the group PCs. The results of this analysis are also shown in Figure 3. The first four group PCs explain significantly more variance (p < 0.001, bootstrap test) than do the stimulus PCs in four out of five subjects. These four group PCs explain on average 19% of the total variance, 72% as much as do the first four individual Thalidomide subject PCs. In contrast, the first four stimulus PCs only explain 10% of the total variance, 38% as much variance as the individual subject PCs. This result suggests that the first four group PCs describe a semantic space that is shared across individuals. Third, we determined how much stimulus-related information is captured by the group PCs and full category model. For each model, we quantified stimulus-related information by testing whether the model could distinguish among BOLD responses to different movie segments (Kay et al., 2008; Nishimoto et al., 2011; see Experimental Procedures for details).

The PPL1 cluster contains five distinct DAN types with stereotyped innervation zones within the MB lobes, the neuropil housing the axon fibers of MB intrinsic neurons (Mao and Davis, 2009). DAN output has been shown to be necessary for the acquisition

of aversive olfactory memories (Schwaerzel et al., 2003), and artificial stimulation of the PPL1 DANs in the presence of an odor is sufficient to form aversive olfactory memory (Claridge-Chang et al., 2009). These studies provide evidence that the PPL1 DANs convey the unconditioned stimulus (US) to the MBs, where it converges with the olfactory conditioned stimulus (CS) for the acquisition of aversive olfactory memories. Two distinct dopamine receptors, dDA1 and DAMB, are highly expressed within the MB intrinsic neurons and are coupled to the cAMP signaling pathway, and thus are likely mediators of dopaminergic effects on olfactory memory (Sugamori

et al., 1995, Han et al., 1996 and Kim Regorafenib research buy et al., 2003). Indeed, the dDA1 receptor is required for both aversive and appetitive olfactory memory formation in adult flies (Kim et al., 2007). While the DAMB receptor mutant was reported to produce aversive olfactory memory defects in larvae (Selcho et al., 2009), these results were confounded by odor preference defects and leave the role of DAMB in adult olfactory learning and memory largely unknown. Here we utilize bidirectional modulation of DAN activity Y 27632 with temporal precision, in vivo

functional imaging of DAN activity, and dopamine receptor mutant analysis to address the role that dopamine plays in memory. Our results indicate that dopamine has a dual role in both the acquisition of olfactory memories and the forgetting of these memories. We used the GAL4 > UAS system (Brand and Perrimon, 1993) to acutely modulate the activity of Drosophila’s DANs during the period of memory retention after olfactory classical conditioning. Rutecarpine Our initial studies employed a tyrosine-hydroxylase (TH) gal4 line (TH-gal4) to drive UAS-transgene expression in the DANs in the fly brain ( Mao and Davis, 2009 and Friggi-Grelin et al., 2003). We drove expression of a UAS-shits1 transgene encoding a temperature-sensitive Dynamin protein that blocks synaptic output at restrictive temperatures ( Kitamoto, 2001) or a UAS-trpA1 transgene encoding a temperature-sensitive cation channel to stimulate DANs at elevated temperatures ( Hamada et al., 2008). Both of these transgenes provide for normal neuronal function below 25°C but modulate activity at temperatures above 29°C. Thus, these two tools allow for the control of neuronal activity in a bidirectional way. Remarkably, we discovered that blocking synaptic output from DANs with UAS-shits1 for 40 min or more after learning significantly enhanced memory measured at 3 hr ( Figure 1A), whereas there was no significant increase in memory with control +/UAS-shits1 flies.