Since “RTEBC consumers” are “breakfast consumers”, it is possible that just eating breakfast (but not necessarily RTEBC) may partly explain the reported health benefits of RTEBC consumption.20 However, differences between RTEBC and other breakfast consumers imply the beneficial effect of breakfast consumption is enhanced with the inclusion of RTEBC. The nutrient fortification and low fat content of cereals may explain relationships between RTEBC consumption and nutrient intake. Compared with other breakfasts, RTEBC consumption Sunitinib cost is associated with greater nutritional benefits in young

people, including higher intakes of total CHO, dietary fibre and several micronutrients and lower total fat and cholesterol intakes.19 and 32 Lower fat intakes are associated with lower BMI in young people47 and may prevent weight gain in adults.59 Increased dairy calcium consumption that often accompanies RTEBC is also related to lower BMI in children60 and interventions in adults have shown that increased calcium consumption may accelerate weight loss.61

In more recent years, it has been suggested that the association between RTEBC consumption and health may be attributed to the consumption of whole-grain and not refined-grain cereals, particularly regarding diabetes.25 and 26 In young people, plasma total cholesterol was lower in those habitually consuming RTEBC with fibre compared Epigenetic Reader Domain inhibitor with traditional breakfast, crisps (“chips”) or sweets, other RTEBC and mixed breakfasts.35 Indeed, the nutritional content of RTEBC varies considerably

and there are concerns that the majority of RTEBC marketed to children fail to meet national nutrition standards. These cereals are typically denser in energy, sugar and sodium, but sparser in fibre and protein compared with cereals that are not marketed specifically for children.62 Conversely, it is possible that the health benefits of RTEBC offset potential Phosphatidylethanolamine N-methyltransferase increases in added sugars and, in practice, the convenience and cost of RTEBC as a breakfast food may facilitate the promotion of breakfast consumption.63 Breakfasts containing LGI rather than high glycaemic index (HGI) CHO typically have a lower energy density and contain higher amounts of dietary fibre.64 and 65 However, evidence on the nutrient intakes of young people regularly consuming LGI compared with HGI breakfasts does not appear to be available. The consumption of RTEBC containing LGI CHO may provide an optimal balance of ensuring that breakfasts are nutritious, healthy and convenient for the consumer. Much of the research on the health benefits of breakfasts containing LGI CHO comes from experimental work investigating the acute effect of manipulations in GI on metabolism. The following section reviews this evidence, following an introduction on GI.

Quantification of the proportion of GFP-labeled cells (CreGFP-electroporated cells in RhoAfl/+ animals or GFP-electroporated TSA HDAC datasheet cells in RhoAfl/fl embryos were used as a control; Figure 4D, yellow bars) confirmed not only the efficient neuronal migration but even revealed an apparently faster migration of RhoA-depleted neurons as a significantly higher proportion of GFP+ cells was located in the upper most bin of five equally bins, corresponding to the cortical plate (CP), compared to controls ( Figure 4D, blue bars). These experiments therefore suggest that RhoA-depleted neurons did not fail to migrate but rather migrated faster than control cells. Some RhoA-depleted cells had migrated even beyond the CP forming ectopic

clusters within and beyond layer 1 in brains analyzed at E19, i.e., 5 days after electroporation ( Figures 4E and 4F), reminiscent of the type II cobblestone lissencephaly observed in the cerebral cortex of cKO mice and described previously. To test the possibility that RhoA may indeed Venetoclax ic50 slow down migration and release this break in its absence, we electroporated a spontaneously activated (“fast-cycling”) mutant of RhoA (RhoA∗) and quantified the position of GFP+ cells in the cerebral cortex. Indeed, consistent with this scenario, we detected a significant increase of GFP-labeled cells in the lower layers 3 days after electroporating

the fast-cycling RhoA mutant construct ( Figures 4C, 4C′, and 4D, pink bars), suggesting a delay in migration in the condition of activated RhoA. Even though these migrating neurons Glucocerebrosidase also had a normal polarized morphology, consistent with a normal migration, it would still be possible that RhoA-deficient neurons reach their final position but in a very different or disturbed migration compared to normal. We therefore directly monitored the migration

of electroporated cells by live imaging in slices. E13 Cre-electroporated cortices were sliced and sections were imaged 2 days after transfection for approximately 9 hr to examine the movement of migrating cells (Movie S1; Figures 5A–5D). All cells imaged performed a normal radial migration moving basally and directed by a single leading process, as shown by the traces of tracked cells in Figures 5A–5D. However, despite the early reduction of RhoA protein in electroporated regions, the remnant levels may still be sufficient to allow for migration of these neurons. To examine migration of neurons lacking RhoA protein entirely, we employed transplantation experiments with cells from E14 cKO cortices which had completely lost RhoA protein by E12. E14 cells were dissociated and labeled with cell tracker green prior to transplantation into isochronic WT cortices (Figures 6A and 6B). Similar to control cells also RhoA−/− cells had often reached the IZ or CP 3 days after transplantation ( Figures 6C–6E). Thus, neurons still migrate fairly normal and reach the CP also in the complete absence of RhoA.

Systemic treatment with either U-50,488 or nalfurafine significantly reduced the amount of time Bhlhb5−/− mice selleckchem spent biting and/or licking the site of lesion by 33% ± 14% and 40% ± 22%, respectively ( Figures S4B and S4C), suggesting that kappa opioids have therapeutic potential for neuropathic itch conditions. Because of the key role of mu opioids in inhibition of pain, numerous groups have assessed the potential role of KOR agonists as analgesics

(Kivell and Prisinzano, 2010 and Vanderah, 2010). While KOR agonists were found to be analgesic in some acute, inflammatory, and neuropathic pain tests, their analgesic efficacy at doses that do not affect motor coordination remains unclear (Leighton et al., 1988 and Stevens and Yaksh, 1986). We therefore wondered whether a concentration sufficient to inhibit itch (i.e., 20 μg/kg of nalfurafine) selleck compound is selective for pruritoception rather than nociception. To address this question, we used the cheek model (Figures 5A and 5B), in which pruritic agents elicit scratching with the hindlimb, whereas nociceptive substances cause wiping with the forepaw (Shimada and LaMotte, 2008 and Akiyama et al., 2010a). As expected, intradermal injection of chloroquine into the cheek induced robust hindlimb-mediated

scratching with minimal wiping behavior, indicative of itch. Systemic pretreatment with nalfurafine led to an almost complete suppression of scratching, with no significant effect on wiping behavior (Figures 5C and 5D), in accordance with the idea that kappa agonists inhibit itch. Next, to investigate the effect of kappa agonists on nociception, we injected capsaicin into the cheek. This treatment evoked intense site-directed wiping with little scratching, in keeping with the idea that pain is the predominant sensation elicited by capsaicin. Importantly, capsaicin-induced wiping was not affected by pretreatment with nalfurafine (Figure 5F), suggesting that nociceptive responses were

Carnitine dehydrogenase unaffected by kappa opioid signaling. In contrast, the modest scratching in response to capsaicin was almost completely abolished following treatment with nalfurafine (Figure 5E). These results suggest that kappa opioid agonists, at least at low doses, can selectively inhibit itch with no effect on pain. The finding that systemic kappa opioids inhibit itch, together with our discovery that Bhlhb5−/− mice lack dynorphin-expressing spinal interneurons, raised the possibility that endogenous dynorphin and exogenous kappa opioids modulate itch through common neural circuits in the spinal cord. To test the idea that the inhibition of itch by kappa opioids is due, at least in part, to activation of spinal KORs, we manipulated KOR signaling in the spinal cord through intrathecal delivery of KOR agonists.

No rat dropped below 85% of initial ad libitum body weight at any time. Three naive rats were trained on the 1DR task (Figure 2C). Each session began with 400 trials where both sides were rewarded and then reward were provided only for one choice direction (when

correct) and this rewarded direction changed across blocks of 100 correctly performed trials (∼120–140 trials total). Reward were delayed for 1 s after entry into the water-port. We provided auditory feedback for both correct and error choices for both the rewarded and unrewarded sides. To ensure that rats responded to the nonrewarded direction following incorrect choices we repeated TSA HDAC cell line the same stimulus in the next trial. Repeated trials were removed from the analysis. Go-signal paradigms were similar to reaction time paradigm except rats were required to stay

in the odor sampling port until a 2 kHz, 100 ms pure tone was delivered after delay dtone after odor valve onset ( Figure 3A). Otherwise, the task timing was identical to the low urgency version of the RT task ( Figure 1C). The following three conditions were considered invalid Akt inhibitor trials and were not rewarded and not counted in accuracy or OSD measurements: (1) short odor poke trials (withdrawal from the odor port before the go-signal) resulted a short white noise burst (120 ms) and 4 s increase in dintertrial. (2) Long odor poke trials (withdrawal >1.0 s after the go-signal) triggered a long white noise the burst (3 s) and 4 s increase in dintertrial. (3) Delayed choice trials (failure to enter a choice port within 4 s after a valid odor sampling period) were invalid but not signaled in any way and did not result in any increase in dintertrial. In a first set of go-signal experiments (Figure 3), a single go-signal delay was used in each session and a range of odor mixtures (12% to 90% mixture contrast) were randomly interleaved within the session, as in the RT paradigms. Go-signal delays were changed from session to session while the odor stimuli

remained constant (Figure 3B). The set of rats tested in this paradigm were naive at the beginning of training. In a second set of go-signal experiments (Figure 4), a single odor mixture pair was delivered in each session and go-signal times were randomly varied within a session. In these experiments, a single set of four rats was used in five sequential phases (I–V). (I) A pseudorandom go-signal delay (dgo) for each trial was drawn from a uniform distribution (0.1–1.0 s in 0.1 s increments). Mixture ratio difficulty was increased after stable performance was achieved (8–10 sessions per ratio) ( Figure 4A, phase I). (II) dgo was drawn from an exponential distribution (mean 0.3 s) using the 12% mixture contrast stimuli ( Figure 4A, phase II). (III) Subjects were retested using uniformly distributed go-signal delays while keeping the same stimuli ( Figure 4A, phase III).

Twenty-one groups of 2–4 simultaneous LGN unit recordings were obtained with two to four quartz-coated platinum-tungsten electrodes (impedance 1–3 MΩ) mounted on a 7-electrode microdrive (Thomas Recording GmBH, click here Giessen, Germany). A custom guide tube narrowed the spacing between electrodes to ∼125 μm. Signals were band-pass filtered (300 Hz–5 kHz) and digitized at 10,000 samples/s. Spike sorting was performed offline with custom software written in Matlab, based on window discrimination followed by manual graphical cluster-cutting

of the first three principal components of the spike waveform. We most often sorted only one spike per electrode, but in a few cases, a second spike waveform could be reliably discriminated. Recordings were from the A layers of the LGN and predominantly from X cells; 19 of 71 LGN neurons were OFF center. Analysis was performed offline with custom software written in Matlab. Spikes were detected and removed from the Vm traces by linear interpolation. The mean and SD of the Vm responses to flashing gratings were calculated from at least 15 repetitions of each stimulus condition, after smoothing the responses with a 5 ms boxcar filter. We defined the peak mean response as the highest mean response in

an analysis window between 30 ms and 120 ms of stimulus onset. Peak Vm SD was calculated from a 2.5 ms window centered at the peak location. Baseline Vm SD was calculated in a 2.5 ms window, starting 5 ms after the onset of the visual stimulus or shock to avoid the influence of the shock MI-773 artifact. For display, the shock artifact was removed by subtracting the shock-only trace (no visual stimulus presented). All parameters were measured without baseline subtraction. “Low-contrast,” which refers to the lowest contrast for which we obtained a positive mean peak Vm response, was 4% (lowest contrast tested) for 23/35 cells and 8% for 12/35 cells. Tuning width

was taken to be σ of a least-squares Gaussian fit to the average peak amplitudes with four free parameters: amplitude, preferred orientation, width, and offset. In LGN recordings, each positive half-cycle of the drifting grating was treated as a separate trial. Spike counts Cefprozil were pooled across orientations for calculating response mean and variance as a function of contrast to obtain between 960 and 4,800 stimulus cycles for each contrast. Pairwise correlations between LGN neurons were calculated as the Pearson correlation coefficients of spike counts on a trial-to-trial basis (Kohn and Smith, 2005 and references therein). Since correlation between pairs of neurons depended on relative response phase, we also calculated pairwise correlations separately for in-phase responses, where the cycle post-stimulus time histograms (PSTHs) of the two neurons overlapped by more than 70% (by area), and for out-of-phase responses for which the overlap was less than 30%. The all-way shuffle predictor of the pairwise correlations (<0.

In contrast, no such events were detected in control slices ( Figure 1B; Table 1) or when the mutant CS-Cbln1, which does not bind to GluD2 ( Matsuda et al., 2010), was used. Retrospective analysis of the synaptogenic events revealed that they were associated with prior protrusive changes characterized learn more as SP (57%), CP (43%), or both (36%) ( Table 1). Considering the low sampling frequency of our time-lapse imaging (1 hr intervals), active PF protrusions are most

probably associated with the majority of the synaptogenic events. We also examined the frequency of PF protrusions among the events that led to the formation of transient boutons that lasted less than 4 hr (Table 1). Such transient boutons were observed in all samples, including those treated with WT-Cbln1 or CS-Cbln1 and those that were untreated. SPs were observed selleck chemicals preferentially after the addition of WT-Cbln1, and led to transient PF bouton formation (Table 1). In contrast, CPs were not observed during transient bouton formation. Taken together,

our observations suggest that Cbln1 induces the formation of SPs and CPs at the sites where the contact is formed between PFs and PC spines. Because CPs were specifically associated with stable bouton formation, CPs may play an important role in promoting maturation of developing presynaptic terminals. Synaptic vesicle (SV) accumulation is an essential step during the formation of presynaptic terminals. To clarify whether PF protrusions are formed before or after SV accumulation, we visualized PF morphology and SVs simultaneously by cotransfecting cDNAs encoding GFP and synaptophysin fused with TagRFP-T (SypRFP) (Shaner et al., 2008). Synapse formation was visualized at 1 hr intervals for 6–9 hr after the addition of recombinant WT-Cbln1 to cbln1-null slices. Consistent with our previous findings, the density of SypRFP clusters in PFs was lower in cbln1-null slices (52.7 ± 0.3/mm, n = 4 slices) when compared to wild-type slices (90.7 ± 3.4/mm, n = 4 slices, p < 0.05). By comparing the images obtained before

Mirabegron and after the addition of WT-Cbln1, we extracted all the synaptogenic events that resulted in new SypRFP clusters, which were formed within 5 hr after the addition of WT-Cbln1 and lasted for 4 hr or longer. To confirm that the new SypRFP clusters were associated with the PC dendrites, we performed retrospective immunostaining for calbindin to visualize PCs ( Figures 2A and 2B). In contrast to the structural changes in PFs ( Figures 1D and 1E), accumulation of SypRFP clusters was detected much earlier ( Figures 2B and 2C). Average time from the addition of WT-Cbln1 to the initial observation of SPs and CPs were 4.6 ± 0.4 hr (n = 8) and 5.8 ± 0.7 hr (n = 6), respectively (calculated from the data in Table 1). In contrast, SypRFP clusters were initially observed 1.5 ± 0.2 hr after the addition of WT-Cbln1 (n = 13; Figures 2B and 2C).

Monkeys sat in a primate chair positioned 57 cm in front of a tangent screen. The chair was in a dark room in the center of magnetic field coils used for measuring eye movements. For monkeys OZ and OM, computers running REX (Hays et al., 1982) and associated programs controlled stimulus presentation, administration of reward, the recording of eye movements

and single neuron activity, and the on-line display of results. For monkey RO, eye movements and neuronal data were acquired using a Plexon System. Visual stimuli appeared on a gray background on an LCD Fulvestrant chemical structure monitor or were back-projected by an LCD projector. Monkeys were rewarded with drops of fruit juice or water. See Supplemental Experimental Procedures for further details. We are grateful to Altah Nichols and Tom Ruffner for machine shop support and to Kirk Thompson for his efforts in the

initial stages of the experiments. “
“Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by two hallmark pathologies, extracellular amyloid plaques and intracellular neurofibrillary tangles. Strong genetic and biochemical evidence highlights a central role of the amyloid pathway in the pathogenesis of AD (Hardy and Selkoe, 2002). The central theme of the “amyloid hypothesis” is that amyloid deposition is the causative factor for the initiation of the neurodegeneration cascade, which includes inflammation, GDC-0449 in vivo Histone demethylase gliosis, neuronal damage, synaptic loss, and cell loss. Although the exact neurotoxic moiety remains speculative (monomer, soluble oligomer, or fibril), the neuropathological findings indicate that neurodegeneration of the AD type occur after initial amyloid deposition.

Since monomer Aβ and fibrils are in equilibrium (DeMattos et al., 2002; Tseng et al., 1999), the deposited plaque probably acts as a reservoir for soluble Aβ, and thus eliminating the deposits would have a multifold benefit through the reduced levels of all possible toxic forms of Aβ (monomer, oligomer, and fibril). Although most of the early onset familial forms of AD arise due to mutations that alter the synthesis of Aβ to favor increased levels of the Aβ42 peptide, the vast majority of cases of idiopathic AD (>95%) are thought to be due to faulty clearance of the peptide and/or deposit (Saido, 1998). Immunotherapy is a promising therapeutic approach focused on using antibodies to facilitate clearance of the Aβ peptide. Three main mechanisms of action for Aβ immunotherapy have been postulated: soluble equilibrium, phagocytosis, or blockade of amyloid seeding. The soluble equilibrium mechanism is based upon antibodies neutralizing soluble Aβ and shifting the equilibrium to favor dissolution (DeMattos et al., 2001).This mechanism of action is proposed to take place in both the periphery and central compartments (DeMattos et al., 2001; Yamada et al., 2009).

In fact, distinct classes of GABAergic interneurons inhibit particular compartments of principal neurons; “basket” cells, that target the somatic and perisomatic compartment, “chandelier” cells that selectively inhibit the axon initial segment, or “Martinotti” cells that preferentially target the apical dendritic tuft are just a few classic examples of this

compartmentalization of inhibition. Morphological differences are however not the only properties that contribute to the diversity of cortical inhibitory neurons. Interneurons can be also subdivided based on intrinsic electrophysiological learn more properties, synaptic characteristics, and protein expression patterns. Probably because of the many dimensions that can be used to describe an interneuron, no consensus yet exists with regard to their categorization. Strikingly, in contrast to the large amount of information that exists on the properties of the various types of cortical inhibitory neurons, knowledge of the specific role that each one plays in orchestrating cortical activity is still extremely limited. Thus, in

this review, unless explicitly mentioned, we remain agnostic as to the specific interneuron subtypes see more mediating inhibition. The specific contribution of different subtypes of interneurons to cortical inhibition is still largely unknown, and is likely to strongly depend on the activity pattern of the network. An important open question is

whether specific subtypes of interneurons have unique functional roles in cortical processing. Through the recruitment of interneurons via feedforward and/or feedback excitatory projections, inhibition generated in cortical networks is somehow proportional to local and/or C1GALT1 incoming excitation. This proportionality has been observed in several sensory cortical regions where changes in the intensity or other features of a sensory stimulus lead to concomitant changes in the strength of both cortical excitation and inhibition (Figure 2A; Anderson et al., 2000, Poo and Isaacson, 2009, Wehr and Zador, 2003, Wilent and Contreras, 2004 and Zhang et al., 2003). In addition, during spontaneous cortical activity, increases in excitation are invariably accompanied by increases in inhibition (Figure 2B; Atallah and Scanziani, 2009, Haider et al., 2006 and Okun and Lampl, 2008). Furthermore, acute experimental manipulations selectively decreasing either inhibition or excitation shift cortical activity to a hyperexcitable (epileptiform) or silent (comatose) state (Dudek and Sutula, 2007). Thus, not only does excitation and inhibition increase and decrease together during physiological cortical activity (van Vreeswijk and Sompolinsky, 1996), but interference of this relationship appears to be highly disruptive.

Funding was provided by NINDS R01 NS069679, the Klingenstein Fund, the Rita Allen Foundation (R.M.B.), http://www.selleckchem.com/products/SRT1720.html NIGMS T32 GM07367-35 (A.R.), and the Max Planck Society (M.O.). “
“Time and space have such a close relationship in human perception that, according to Piaget, “time and space form an inseparable whole” (Piaget, 1927, p. 1). Temporal and spatial perceptions interfere with each other in both humans and monkeys (Casasanto and Boroditsky, 2008, Merritt et al., 2010 and Xuan

et al., 2007), saccadic eye movements compress magnitude judgments of both space and time (Morrone et al., 2005), and spatial manipulations such as prism adaptation cause misperceptions of time intervals (Magnani et al., 2011). These and other psychophysical interactions have led to the idea that the brain encodes magnitude in domain-general representations that include space and

time, as well as number, size, and speed (Gallistel and Gelman, 2000 and Walsh, 2003). According to this theory, some neural networks encode a greater or lesser quantity in the abstract, independent of metrics such as distance, duration, speed, numerosity, etc. Although some findings support an abstract neural representation of magnitude, such as the effect of cortical damage on both space and time perception (Basso et al., Afatinib manufacturer 1996, Mitchell and Davis, 1987 and Zorzi et al., 2002), other results seem to contradict this idea. For example, some patients with lesions of different frontal and parietal areas have deficits in perceiving either numbers or durations, but not both (Cappelletti et al., 2009 and Cappelletti et al., 2011). Likewise, an asymmetry in the interference between temporal and spatial perceptions indicates separate, domain-specific mechanisms that interact with each other, rather than a common representation of magnitude (Casasanto et al., 2010). Neurons in the prefrontal (PF) and parietal cortex

encode space, time, and number (Nieder and Miller, 2004a, Nieder and Miller, 2004b, Tudusciuc and Nieder, 2007 and Tudusciuc and Nieder, 2009), including categories of these Linifanib (ABT-869) metrics (Merchant et al., 2011), and several contemporary theories of the PF cortex have stressed domain generality and cross-domain information processing (Baars et al., 2003, Duncan, 2010 and Wilson et al., 2010). In two previous reports, we have described PF activity during the discrimination of relative durations (Genovesio et al., 2009) and relative distances (Genovesio et al., 2011) recorded in the same PF areas. These reports do not, however, address whether individual neurons in these areas encode relative magnitude in both cognitive domains. In order to search for a representation of common magnitude, we analyzed the activity of cells that were recorded in both of these discrimination tasks, along with a control task that we used to identify goal coding.

Bonferroni/Dunn post hoc comparisons were used for individual comparisons after ANOVA. We Epacadostat thank S. Ozawa, the late T. Tsujimoto, and the late Y. Kidokoro for comments on the preliminary draft of manuscript, as well as J. B. Thomas, A.-S. Chiang, T. Tamura, and S. Xia for fly stocks and U. Thomas for antibody. We also thank to A.

Miwa and S. Hirai for assisting in experiments. We are grateful to members of the Saitoe laboratory for technical assistance and discussions. This work was supported by Takeda Science Foundation and the Uehara Memorial Foundation and by the Grant-in-Aid for Scientific Research on Innovative Areas “Systems Molecular Ethology” from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT). “
“Converging evidence from neurophysiology and from functional and metabolic neuroimaging demonstrate that the brain is continually active in the absence of sensory inputs or motor tasks (Kennedy et al., 1978, Arieli et al., 1995, Biswal et al., 1995 and Raichle

et al., 2001). There has recently been considerable interest in whether this spontaneous activity reflects and can be used to investigate, Dabrafenib the underlying architecture of the functional networks within the brain. Neurophysiological evidence for this prospect comes, for example, from experiments using optical imaging of voltage sensitive dyes, which have shown that the correlation structure of spontaneous activity in visual cortex reflects the spatial structure of an orientation map derived from sensory stimulation (Kenet et al., 2003). A recent fMRI study has similarly shown correspondence between spontaneous signals and the functional organization of the somatosensory cortex (Chen et al., 2011). Moreover, studies with single unit electrophysiology have demonstrated that there is a higher

level of correlation in spontaneous spiking activity between pairs of neurons that have similar tuning properties (Lee et al., 1998 and Crowe et al., 2010). Furthermore, the spontaneous activity of single neurons can reflect the global state of the network in which they are heptaminol embedded (Arieli et al., 1996, Tsodyks et al., 1999 and Luczak et al., 2009). In summary, the brain’s endogenous activity can exhibit significant spatial and temporal structure, and this structure can be related to the underlying functional properties of the network. At the same time, it is unclear to what extent previous observations reflect a general principle of cortical function. Most of the imaging studies in visual and somatosensory cortex mentioned above were conducted in anesthetized rodents and cat, but the anesthesia or behavioral state can influence spontaneous neural activity. In humans, the correlation structure of gamma-band spontaneous activity in the awake state is different from that in slow-wave sleep (He et al., 2008).