, 2012) to test whether amplitude stability is expected, given the rates of R∗ and G∗-E∗ deactivation for each of the mouse lines. Using parameters

optimized within 10% of the canonical values of Table 2, the predictions of the tightly constrained model were found to be in excellent agreement with Selleckchem GS-7340 the experimental SPRs of each genotype in both the wild-type and the GCAPs−/− backgrounds (Figures 4A and 4B). Thus, amplitude stability is an inherent feature of a model of phototransduction that incorporates measured lifetimes of R∗ and G∗-E∗, an experimentally determined diffusion coefficient for cGMP (Gross et al., 2012), and parameters of calcium feedback determined by biochemical measurements. To better understand the specific mechanisms

contributing to stability, we used the model to calculate SPR amplitudes for theoretical effective R∗ lifetimes (τReff) ranging from a few milliseconds to several seconds, which is adequately long to approximate a step selleck inhibitor of R∗ activity and for the SPR to achieve steady state (Figure 4C). The model (solid curves) accurately predicts the average SPR amplitudes of rods of both GCAPs−/− (green symbols) and GCAPs+/+ backgrounds (blue symbols), including the steady-state amplitudes of SPRs produced by R∗s that remain fully active for several seconds (Gross et al., 2012). Notably, both data and theory differ strongly from the intuitive notion that the SPR amplitude would increase in proportion to R∗ lifetime, except for τReff < 20 ms. Our results establish that GCAPs-mediated feedback makes a distinct contribution to SPR amplitude stability. To characterize this contribution, we plotted the SPR amplitudes for GCAPs+/+

and GCAPs−/− backgrounds (blue and green symbols in Figure 4C) for each value of τReff against each other (Figure 4D). For τReff > 40 ms, the amplitudes of the SPRs of the GCAPs+/+ and GCAPs−/− backgrounds significantly deviate from proportionality (dashed gray line). For longer R∗ lifetimes, the relative increase in SPR amplitude is systematically greater for rods of the GCAPs−/− much background than for rods of GCAPs+/+ background. This reveals that GCAPs-mediated feedback reduces the amplitudes of SPRs driven by longer R∗ lifetimes to a greater extent than those driven by shorter R∗ lifetimes. To understand how SPR amplitude stability is conferred by GCAPs-mediated feedback, it is instructive to separately consider the time courses of light-driven cGMP hydrolysis and synthesis, integrated over the length of the outer segment. The spatially integrated rates of cGMP hydrolysis are illustrated for SPRs corresponding to three different values of τReff (15, 40, and 76 ms) in the GCAPs+/+ (Figure 5A, orange, black, and blue traces) and GCAPs−/− (gray dotted lines) backgrounds. All six hydrolysis rate functions follow a common initial trajectory (pink area) but peel off at times that depend on τReff.

This is the first report of a motor protein that plays a key SB203580 datasheet role in enrichment-induced structural and behavioral changes. Our data demonstrate a new molecular motor-mediated presynaptic mechanism underlying experience-dependent neuroplasticity. Considering that enrichment is beneficial to ameliorate symptoms of brain disorders (van Praag et al., 2000 and Nithianantharajah and Hannan, 2006), KIF1A is a potentially important therapeutic target that merits further investigation. Three- to four-week-old male mice were housed in standard (nonenriched) cages without

special equipment (3 mice per cage) or in enriched cages (15 mice per cage) equipped with running wheels, tunnels, igloos, huts, retreats, and wooden toys. All mice received standard lab chow and water ad libitum. Bdnf mutant mice have been previously described ( Ernfors et al., Selleck Entinostat 1994) and were obtained from The Jackson Laboratory (Bar Harbor, ME). Kif1a mutant mice have been produced and described (Niwa et al., unpublished). Kif1a+/− mice are generally healthy and do not

exhibit any sensory or motor neurological abnormalities up to 3 months old. These mutant mice had been backcrossed at least seven generations with C57BL/6J mice. Male mice were used in all experiments. Detailed information is provided in the Supplemental Experimental Procedures. Mouse hippocampi, cultured hippocampal neurons, and cultured astrocytes were lysed in Nonidet P-40 (NP-40) buffer (10 mM HEPES [pH 7.4], 150 mM NaCl, 1% NP-40). The lysates were subjected to SDS-PAGE followed by immunoblotting as previously described (Yin et al., 2011). Quantification

analyses were performed using ImageJ (National Institutes of Health) software. The respective protein levels in nonenriched wild-type mice, nontreated cultured hippocampal neurons, or nontreated cultured astrocytes were set as 1 at each time point. Detailed information is and provided in the Supplemental Experimental Procedures. The sources of antibodies used were as follows: anti-KIF1A (rabbit polyclonal, Niwa et al., 2008), anti-KIF1Bβ (rabbit polyclonal, Niwa et al., 2008), anti-KIF5A (rabbit polyclonal, Kanai et al., 2000), anti-KIF5B (rabbit polyclonal, Kanai et al., 2000), anti-KIF17 (rabbit polyclonal, Yin et al., 2011), anti-dynein (mouse monoclonal, Millipore), anti-synaptophysin (mouse monoclonal, Chemicon), anti-BDNF (rabbit polyclonal, Santa Cruz Biotechnology), anti-αTubulin (mouse monoclonal, Sigma), and anti-GAPDH (mouse monoclonal, Abcam). Total RNA was isolated from mouse hippocampi and cultured hippocampal neurons using ISOGEN II (Nippon gene) according to the manufacturer’s instructions, and semiquantitative RT-PCR analysis was performed as previously described (Yin et al., 2011). The respective mRNA levels in nonenriched mice or nontreated cultured hippocampal neurons were set as 1 at each time point.

To further confirm these gene ontology categories, we create a custom network via Ingenuity, in which the top network is significant for cell death (Figure S4). Notably, within the GO analysis there was only one KEGG signaling pathway whose members were overrepresented with GRN knockdown, the Wnt signaling pathway. Members of the Wnt signaling pathway with significant alterations in expression included: CD24, WNT1, SFRP1, NKD2, and the Wnt receptor FZD2. Other Wnt genes buy BIBW2992 that were nominally significant include GSK3B, PPP2R2B, APC2, and CER1. To provide independent validation,

gene expression changes of key Wnt signaling pathway members are additionally validated by qRT-PCR ( Figure 3B). These changes follow a clear pattern: genes that typically activate canonical Wnt signaling are upregulated (WNT1, FZD2, APC2), whereas genes that normally inhibit Wnt signaling are downregulated (GSK3B, SFRP1, NKD2, CER1) ( Figure 3C). This indicates that an selleck kinase inhibitor early consequence of GRN loss in vitro in human neural cells is an increase in Wnt signaling components that increases pathway activity. To test this

prediction, we performed a direct experimental assay of Wnt activity in this model using the canonical LEF/TCF reporter ( Experimental Procedures). LEF/TCF signaling was increased in the GRN knockdown condition ( Figure S5), confirming that indeed Wnt signaling is altered. Moreover, noncanonical Wnt signaling pathways

AP1, cJun, and NFAT assayed by the same reporter system ( Experimental Procedures; Figure S5) show no significant changes, indicating that the alterations in Wnt signaling converge on the canonical ADP ribosylation factor pathway. Although the GO analysis points to several potential key alterations in biological and molecular functions coupled with GRN deficiency, GO analysis is considered only a first step, since the function of many genes is not well-annotated. Recently, we have shown that WGCNA (Zhang and Horvath, 2005) provides a system level framework for the understanding of transcriptional profiles in many distinct cellular and tissue contexts (Geschwind and Konopka, 2009, Miller et al., 2008, Oldham et al., 2008, Winden et al., 2009 and Voineagu et al., 2011). WGCNA has the power to reveal the underlying organization of the transcriptome of a system under study based on the degree of gene neighborhood sharing, which is defined based on coexpression relationships. This facilitates the identification of modules of functionally related, highly coexpressed genes, as well as the most central or hub genes that are of prime importance to module function (Geschwind and Konopka, 2009, Miller et al., 2008, Oldham et al., 2008 and Winden et al., 2009). We condense the gene expression pattern within a module to a “module eigengene” (ME) which is a weighted summary of gene expression in the module (Oldham et al., 2008).

Next we checked whether the suppression occurs at the end of the cascade, at the level of AMPA receptor trafficking in and out of the synapse. To that end we exploited the facts that LTP and LTD can be both reversed by activity. The

reversal of LTP (termed de-potentiation) and LTD (termed de-depression) share common downstream mechanism of expression with LTD and LTP, as they involve changes in AMPA receptor function; yet they differ in induction mechanisms, as they involve different kinase and phosphatase pathways (Hardingham et al., 2008 and Lee and Huganir, 2008). We reasoned that if the GPCR-mediated suppression occurs at the expression level (AMPAR trafficking), de-potentiation and de-depression should also be affected. The experiments were carried out in a two independent inputs setting, to allow internal controls, and using pairing Selleck AC220 conditioning (to 0mV or –40mV) to induce LTP and LTD as well as to reverse them (Figure 5). First LTD was induced in both inputs, and 20 min later one input was de-depressed by pairing with 0mV while the other input was not stimulated. The second pairing effectively reversed LTD in either control learn more conditions (de-depressed versus nonstimulated; paired t test: p = 0.0086) (Figure 5A), and in the presence of methoxamine (paired

t test: p = 0.0368. Figure 5B), indicating that α1 adrenergic receptors do not suppress de-depression. A similar strategy was used to test the role of β-adrenergic receptors on de-potentiation: LTP induction in both pathways, followed by pairing with –40mV in one input (Figures 5E and 5F). The second pairing reversed LTP either in control conditions (p = 0.0343. Figure 5E) or in the presence

of isoproterenol (p = 0.0007) (Figure 5F). Next we compared the effects of methoxamine Ketanserin on LTD and de-potentiation simultaneously by first inducing LTD in one input and then applying the 0mV pairing to both inputs. In control experiments (Figure 5C) the second pairing potentiated both the depressed input (p = 0.0008) and the naive (p = 0.0038); in the presence of methoxamine (Figure 5D) the depressed inputs potentiated (p = 0.0236), but not the naive inputs (p = 0.2054), confirming that α1-adrenergic receptors prevent LTP but they do not affect de-potentiation. The effects of β-adrenergic receptors on LTD and depotentiation were compared with a similar strategy: first LTP induction of one input, followed by simultaneous pairing with −40mV of both potentiated and naive inputs. Under normal conditions both inputs became depressed (potentiated inputs: p = 0.001; naive inputs: p = 0.0006) (Figure 5G). In contrast, in the presence of isoproterenol only the previously potentiated input became depressed (potentiated inputs: p = 0.048; naive inputs: p = 0.604) (Figure 5H). These results confirmed that β-adrenergic receptors prevent LTD but do not affect de-depression.

, 2010, Webster et al., 1994 and Yeterian et al., 2012), also reflect subjective visual perception in a manner close to all-or-none. In particular,

we observed that the magnitude of SUA and MUA perceptual modulation in the macaque LPFC is significantly higher than the respective magnitude in lower visual cortical areas during BFS/BR (Gail et al., 2004, Keliris et al., 2010, Leopold and Logothetis, 1996 and Logothetis and Schall, 1989) and largely follow phenomenal perception. Therefore, the results presented http://www.selleckchem.com/products/bmn-673.html in this study suggest that the LPFC and temporal cortex could consist a corticocortical network critically involved in explicit processing of stimulus awareness. Assuming a feed-forward scheme, it is possible that perceptually related activity is transferred from the STS/IT to the LPFC through the well-described anatomical connections between these two areas. However, these connections are reciprocal, indicating that the direction of perceptual modulation flow could as well follow the opposite direction (i.e., from the LPFC to the IT/STS cortex). Our results did not allow us to draw any solid conclusions regarding

the flow of perceptual information. We observed, however, that the mean SUA and MUA perceptual latencies started to become significant at approximately 220 ms following the stimulus flash, thus looking very similar to the latency reported by Sheinberg see more and Logothetis (1997) for STS/IT cortex. Perceptual information flow between STS/IT and LPFC could also follow a transthalamic pathway, since both cortical areas connect to the pulvinar nucleus of the thalamus

(Barbas et al., 1991, Contini et al., 2010, Romanski et al., 1997 and Webster et al., 1993). Endonuclease Interestingly, perceptual modulation of spiking activity is surprisingly high in the dorsal pulvinar (which receives mostly afferents from the frontal cortex), where MUA activity in 60% of the recorded sites is modulated during generalized flash suppression but absent in the lateral geniculate nucleus during BR (Lehky and Maunsell, 1996 and Wilke et al., 2009). Future experiments employing simultaneous electrophysiological recordings in the temporal cortex and LPFC during BR of BFS could (a) allow monitoring of perceptual latencies and directed functional connectivity and thus hint at the direction of interareal perceptual information flow and (b) elucidate which features of functional connectivity between these two cortical areas are related to the emergence of conscious visual perception. Interestingly, we observed some weak traces of nonconscious stimulus processing in the pattern of the mean MUA responses during the perceptual dominance of a nonpreferred stimulus.

Hence, our data allow us to state that 3D-structure selective clusters exist in IT but do not allow us to characterize the 3D-structure selectivity in IT in an unbiased way. For the spike-density functions in Figures 2B and 2C, the preferred structure for each 3D-structure-selective site was defined as the structure with the highest average MUA in the stimulus interval selleck kinase inhibitor ([100 ms, 800 ms]; 0 = stimulus onset). Averaging was performed

on 50% of the trials randomly chosen from the Fix-position-in-depth presentations (i.e., stimuli presented at the fixation plane). The remaining 50% of the trials were used to calculate the spike-density function for the Fix-position-in-depth stimuli. This procedure avoids spurious 3D-structure selectivities due to MUA variability unrelated to the stimulus. Importantly, the preferred structure thus defined was used to sort the MUA of the Far- and Near-trials into preferred- and nonpreferred PD0332991 categories. Virtually identical results were obtained when the preferred structure was determined using the MUA from the Far- or Near-positions-in-depth. The

averaged spike trains of each 3D-structure selective site were first convolved with a Gaussian kernel (σ = 10 ms) before being averaged across sites. We used the d′ as a measure of the 3D-structure selectivity of a site. The signed d′ is defined as d′=(X¯convex−X¯concave)/Sconvex2+Sconcave2/2, where X¯convex and X¯concave are the mean multiunit responses to convex and concave stimuli, respectively, and Sconvex2 and Sconcave2 are the variances of the neural responses to convex and concave stimuli, respectively. Positive and negative values

indicate convex and concave tuning respectively. The unsigned d′ is given by the absolute value of the signed d′, |d′| and indicates the magnitude of the 3D-structure selectivity. We estimated the RT for each trial as follows: The horizontal eye-traces of no each trial were first low-pass filtered (cutoff = 40 Hz) to remove high-frequency noise (Bosman et al., 2009). The resulting time series x→t was transformed into velocities using the transformation v→n=(x→n+2+x→n+1−x→n−1−x→n−2)/6Δt (Δt = sampling period) which represents a moving average of velocities to suppress noise. The reaction time was defined as the time point relative to stimulus onset of the first of five consecutive velocities for which the speed exceeded 50 deg/s in the same direction. Reaction times were square-root transformed before being entered into an ANOVA. We used logistic regression to model the behavioral data as a function of stereo-coherence and the occurrence of microstimulation on a trial (Afraz et al., 2006, DeAngelis et al., 1998 and Salzman et al.

While both are essential components of the control system, the EVC theory ascribes to dACC a role in specification but not regulation, as we discuss below. Monitoring. In order to specify the appropriate control signal and deploy regulative functions in an adaptive manner, the system must have access to information about current

circumstances and how well it is serving task demands. Detecting and evaluating these requires a monitoring mechanism. The conflict-detection component in the Stroop model provides one example of such a monitoring function and how it can guide specification: the occurrence of response conflict indicates that insufficient control is being allocated to the current task (see Botvinick, 2007, Botvinick et al., 2001 and Botvinick et al., Everolimus mouse 2004). check details In this instance, conflict indicates the need to re-specify control signal intensity. However, conflict is just one among many signals that can indicate the need to adjust intensity. Others include response delays, errors, negative feedback, and the sensation of pain. These signals all carry information about performance within

a task and how to specify control signal intensity. Monitoring must also consider information relevant to the specification of control signal identity; that is, to task choice. Such information can come from external sources (e.g., explicit instructions, cues indicating new opportunities for reward, or the sudden appearance of a threat) or internal ones (e.g., diminishing payoffs from the current task indicating

it is no longer worth performing, recollection of another task that needs to be performed, etc.). In all of these cases, monitoring must be responsive to, but should be distinguished MycoClean Mycoplasma Removal Kit from, the sensory and valuative processes that represent the actual information relevant to specification. Thus, just as we distinguish between specification and regulation on the efferent side of control, we distinguish between monitoring and valuation on the afferent side. In each case, the EVC theory ascribes to dACC a role in the former, but not the latter. Early research on control focused on regulative and monitoring mechanisms, but growing attention is being paid to the problem of control-signal specification. Work in this area has been driven increasingly by ideas from research on reward-based decision making and reinforcement learning. One emerging trend has involved reframing control-signal specification as an optimization problem, shaped by learning or planning mechanisms that serve to maximize long-term expected reward (Bogacz et al., 2006, Dayan, 2012, Hazy et al., 2007, O’Reilly and Frank, 2006, Todd et al., 2008 and Yu et al., 2009). Under this view, cognitive control can be defined as the set of mechanisms responsible for configuring behavior in order to maximize the attainment of reward.

[K+]o and [Ca2+]o reached a steady-state level during a stable locomotor episode. learn more The steady state of [K+]o reflects an equilibrium between the neuronal K+ efflux and its clearance from the extracellular space with neuronal Na+/K+ pump (Syková, 1987) and glial cells (Jendelová and Syková, 1991). The decrease in [Ca2+]o mainly involves an uptake into postsynaptic somata and/or dendrites (Heinemann and Pumain, 1981). Lowering [Ca2+]o has been reported to switch the firing mode of various CNS neurons from spiking to bursting (Brocard et al., 2006; Heinemann et al., 1977; Johnson et al., 1994; Su et al., 2001; Tazerart et al.,

2008). In our experiments, the reduction OSI744 of [Ca2+]o requires a concomitant raise in [K+]o to trigger bursts. This synergistic effect probably results from a joint regulation of INaP and IK, respectively. An increase in INaP appears to be the major link between the reduction in [Ca2+]o and the bursting ability because a decrease of [Ca2+]o shifts the threshold of INaP activation toward more negative values and enhances its amplitude. In agreement with this, our simulation showed that the shift of the threshold of INaP activation toward more negative values plays a major role in the emergence of bursts, and even a subtle shift of activation by −3 mV produces the same effect as increasing INaP conductance by 50%. This is supported by the

sensitivity of pacemaker activity to riluzole and TTX. Changes in pore occupancy of sodium channels by calcium may be responsible for these modifications of INaP ( Armstrong, 1999). Although [K+]o increase does not upregulate INaP, as shown experimentally, our model demonstrates that it facilitates the emergence of INaP-dependent bursts by reducing IK as a result of reduction of EK (see also Rybak et al., 2003). The increased [K+]o also provides an additional depolarization of pacemaker cells via the reduction of the voltage-gated potassium and leak

currents, which also increases the frequency of oscillations. In summary, the regulation of INaP and IK by [Ca2+]o and [K+]o, respectively, may represent a fundamental PD184352 (CI-1040) mechanism in generating and regulating the pacemaker activities in other brain areas. Taking into account that changes in [K+]o and [Ca2+]o (1) precede the onset of locomotion, (2) promote INaP-dependent pacemaker properties in putative locomotor CPG cells, and (3) trigger a locomotor episode, a conceptual scheme can be proposed for rhythmogenesis in the mammalian spinal cord. A moderate spiking activity of CPG components causes a reduction in [Ca2+]o and increase in [K+]o. Changes in these concentrations cause the simultaneous regulation of INaP and IK, which together produce at a threshold level a switch from spiking to bursting representing the locomotor oscillations.

, 2012). We therefore tested SC boutons for local and global spatial correlations. RFs of directly neighboring boutons were not correlated (Figure 2A) (R2 = 0.06 ± 0.04, n = 7 cells, not different from the correlation between random bouton pairs: R2 = 0.01 ± 0.01, p = 0.46). Imaging conditions were near identical for adjacent boutons; we therefore could gain additional information by comparing absolute fluorescence

signals of the two-color channels independently. Total vesicle content (integrated red fluorescence, corrected for surface-stranded protein) and the absolute number of released selleck chemicals llc vesicles (change of integrated green fluorescence) were also not correlated between neighbors (Figure 2B). None of the individual axons showed significant neighborhood correlations (data not shown). Furthermore, RF did not change as a function of distance along the axons (Figures 2C and 2D; average axon length studied: 338 ± 78 μm, range: 107–729 μm; n = 7 cells, 14–89 boutons each). Therefore, in contrast to dissociated culture (Branco et al., SP600125 2008; Murthy et al., 1997; Peng et al., 2012), mature SC boutons do not display

systematic modulation of presynaptic parameters along the axon, locally or globally. Given that multiple synaptic connections between one axon and one dendritic branch are frequently formed in dissociated culture, but not in organotypic culture (Figure S2) or in vivo (Sorra and Harris, 1993), the lack of neighborhood correlations in organotypic culture is not surprising. SC axons traverse CA1 dendrites perpendicular (Figure S2), an arrangement that prevents retrograde comodulation of neighboring boutons by the same target cell (Branco et al., 2008). Synaptic Pr scales linearly with the number of vesicles docked to the active zone (Branco et al., 2010; Holderith et al., 2012; Murthy et al., 2001). How release scales with the total vesicle number is less clear, given that not all vesicles are thought to be

functional (Branco et al., 2010). Taking the integrated red fluorescence of ratio-sypHy as an estimate of total vesicle content (corrected for surface fraction), we were able to compare total vesicle pool size and RF at individual boutons along SC axons (Figure 3). We confirmed (Shepherd and Harris, 1998) that total vesicle content is highly variable (average QCV: 0.49 ± 0.02, n = 12 cells). Adenosine The average RF in response to 200 APs at 30 Hz was nearly constant for the largest quartile (Q75%) of boutons (Figure 3B). The smallest quartile (Q25%), however, released on average an almost 2-fold larger fraction of their vesicles (Q25%/Q75% = 1.8 ± 0.12, p < 0.0001, 74 boutons per quartile, n = 12 cells). Our data suggest that release scales with total vesicle content like bouton surface with bouton volume (i.e., 2/3 power; Figure 3B; also see the Experimental Procedures). Thus, during periods of high activity, small synapses are more prone to deplete their vesicles than large synapses.

, 2008 and Slachevsky et al., 2001). fMRI revealed that this nonlinearity related to a bilateral distributed network involving AG and PFC cortices ( Farrer et al., 2008). Perhaps the clearest evidence for a two-stage process in action awareness JQ1 price comes from studies of error awareness ( Nieuwenhuis et al., 2001). In an antisaccade paradigm, participants were instructed to move their eyes in the direction opposite to a visual target. This instruction generated frequent

errors, where the eyes first moved toward the stimulus and then away from it. Many of these erroneous eye movements remained undetected. Remarkably, immediately after such undetected errors, a strong and early (∼80 ms) ERP component called the error-related negativity arose from midline frontal cortices (anterior cingulate or pre-SMA). Only when the error was consciously detected was this early waveform amplified and followed by a massive P3-like waveform, which fMRI associated with the expansion of activation into a broader network including left inferior frontal/anterior insula activity ( Klein et al., 2007). The experiments reviewed so far considered primarily subliminal paradigms where access to conscious reportability was modulated by reducing the incoming sensory information. However, similar

findings arise from preconscious paradigms learn more where withdrawal of attentional selection is used to modulate conscious access ( Dehaene et al., 2006), resulting in either failed (attentional blink, AB) or delayed (psychological refractory period or PRP) conscious access.

In such states, initial visual processing, indexed by P1 and N1 waves, can be largely or even entirely unaffected ( Sergent et al., 2005, Sigman and Dehaene, 2008 and Vogel et al., 1998). However, only perceived stimuli exhibit an amplification of activation in task-related sensory areas (e.g., parahippocampal place area for pictures of places) as well as the unique emergence of lateral and midline prefrontal and parietal all areas (see also Asplund et al., 2010, Marois et al., 2004, Slagter et al., 2010 and Williams et al., 2008). Temporally resolved fMRI studies indicate that, during the dual-task bottleneck, PFC activity evoked by the second task is delayed ( Dux et al., 2006 and Sigman and Dehaene, 2008). With electrophysiology, the P3b waveform again appears as a major correlate of conscious processing that is both delayed during the PRP ( Dell’acqua et al., 2005 and Sigman and Dehaene, 2008) and absent during AB ( Kranczioch et al., 2007 and Sergent et al., 2005). Seen versus blinked trials are also distinguished by another marker, the synchronization of distant frontoparietal areas in the beta band ( Gross et al., 2004).