Excised apical or middle cochlear turns were viewed through a water-immersion objective (Zeiss 40× or 63×) on a Zeiss Axioskop FS microscope. The chamber was perfused with artificial perilymph of composition (in mM): 150 NaCl, 6 KCl, 1.5 CaCl2, 2 Na-pyruvate, 8 D-glucose, and 10 Na-HEPES (pH 7.4), osmolarity 315 mOsm/kg−1. The effect of endolymph was examined by changing the solution around

the hair bundle using a nearby puffer pipette to one containing (mM): 155 KCl, 0.02 CaCl2 (buffered with 4 HEDTA), 2 Na-pyruvate, 8 D-glucose, and 10 K-HEPES selleck products (pH 7.4). Endolymph Ca2+ has been reported to be between 0.02 Proteases inhibitor and 0.04 mM (Bosher and Warren, 1978 and Salt et al., 1989). The puffer pipette was positioned about 30 μm from the target and aimed approximately along the cochlear

axis so the flow did not directly stimulate the bundle. The flow was also away from the small hole in the reticular lamina through which the recording electrode was introduced so it is unlikely that the solution gained access to the OHC’s basolateral membrane. To ensure that the solution was fully replaced, the flow was continued until the holding current had increased to a steady state (usually taking 10–20 s) prior to running the stimulation protocol. Recordings were made from first or second row OHCs using borosilicate patch either electrodes connected to an

Axopatch 200A amplifier and currents were low-pass filtered at the amplifier output at 10 kHz and digitized at 100 kHz. Patch electrodes were filled with an intracellular solution containing (mM): 125 KCl, 3.5 MgCl2, 5 Na2ATP, 0.5 GTP, 10 Tris phosphocreatine, 1 BAPTA, 10 K-HEPES (pH 7.2), osmolarity 295 mOsm/kg−1. BAPTA (1 mM) was used as the intracellular Ca2+ buffer as it most closely approximates the native buffer (Beurg et al., 2010). No significant apex to base gradient in the Ca2+ buffer concentration has been reported (Hackney et al., 2005) so the same BAPTA concentration was used for all CFs. In recording from older (P15–P19) animals, intracellular chloride was reduced to minimize OHC contractions by replacing the 140 KCl with 130 K-aspartate plus 10 KCl. The locations of the apical, middle and a few basal turn recordings (Figure S1) correspond in vivo to mean CFs of 4, 10, and 20 kHz respectively for P21 animals (Müller, 1991). Because there is a continued expansion of the high frequency range into the adult for both rat and gerbil (Müller, 1991; 1996), CFs were taken from frequency maps at P21.

Researchers argue that metabolic dysfunction, including reduced mitochondrial energy status in

the brain with increased metabolic demands but decreased energy stores with a low ATP/DTP ratio and increased lactate/pyruvate ratio, may play a role (Jenkins et al., 1989; Vespa et al., 2005; Vagnozzi et al., 2008). Yuen et al. (2009) suggested that mild trauma AZD6738 clinical trial stimulates a type of sodium channelopathy on axons, which, in turn, intensifies pathophysiological responses to succeeding minor injuries. Longhi et al. (2005) reported that increased brain vulnerability after repeated concussions occurs via axonal damage, which is significantly amplified. In the next section, we more closely consider some of the molecular mechanisms underlying traumatic brain injury. There are two main categories of brain damage due to trauma: focal damage and diffuse injury. Focal injury includes cortical or subcortical contusions and lacerations, as well as intracranial bleedings (subarachnoid hemorrhage and subdural hematoma). Focal injury is due to severe direct impact on the brain and is thus mainly seen in severe cases of TBI. Diffuse injury is caused by stretching and tearing of the brain tissue and does not need any skull fracture or direct impact or crush injury to the brain

surface and is therefore also Metformin mouse seen in cases with mild TBI. The main form of diffuse injury is called diffuse axonal injury (DAI), which is due to acceleration/deceleration forces that lead to shearing of axons. In the following subsections, we discuss the neurobiology of acute mild TBI or concussion, considering how accurate this may be examined in different forms of animal models. We also review the chronic degenerative brain disorder CTE, which is found in contact sports athletes, and its similarity to other neurodegenerative disorders, especially Alzheimer’s disease and Parkinson’s disease (PD). Animal models have been used in numerous studies to examine the neurobiology and mechanisms of TBI. Many studies exploring the neurobiology and neurochemistry

of acute TBI are based on invasive animal TBI models in which the brain is exposed by craniotomy, and the cortex is subjected first to injury by crush or compression, for example, by a rigid impactor (controlled cortical impact), weight drop, vacuum deformation, or by fluid percussion (for review, see O’Connor et al., 2011). These direct crush animal TBI models have been found to have a high variability in outcome, ranging from minor symptoms to fatal outcome, from a minor change in impact (Nilsson et al., 1990), which might limit their utility as models of human mild TBI. Animal TBI models based on acceleration/deceleration of the skull and brain that replicate the dynamics of damage due to rotational forces leading to diffuse brain injury have been difficult to develop, due to the lower mass of the animal brain (O’Connor et al., 2011; Johnson et al., 2012).

With this model, inactivation is coupled in an allosteric manner to activation but it is not obligatory

for channels to open for inactivation to occur (Armstrong, 2006). Parameters were adjusted by trial and error to match the voltage dependence and kinetics of activation and inactivation and voltage dependence of steady-state current, using the data from our experimental recordings of current from acutely dissociated hippocampal CA1 neurons at 37°C. Data are summarized as mean ± SEM. Thanks to Zayd Khaliq for discussion and helpful suggestions. Supported by the National Institute of Neurological Disorders and Stroke (R01-NS036855 to B.P.B., R01-NS046579 to B.L.S., F31-NS064630 to B.C.C., and F31-NS065647 to A.J.G.) and the Howard Hughes Medical Institute (B.L.S.). A.J.G. was also supported by a Quan Predoctoral Fellowship. “
“Astrocytes AZD9291 solubility dmso alone make and store glycogen in the mammalian adult brain (Cataldo and Broadwell, 1986). By recruiting this energy store, astrocytes can deliver lactate (and possibly pyruvate) to neurons for fuel, helping maintain axonal and synaptic function (Izumi et al., 1997; Magistretti

and Pellerin, 1999; Wender et al., 2000), particularly during brief periods of aglycemia (Wender et al., 2000) or during intense neuronal activation (Brown et al., 2003; Magistretti et al., 1993; Wyss et al., 2011). The importance of astrocyte-to-neuron lactate transport has been demonstrated by the recent report demonstrating that IOX1 it is required for long-term memory formation in vivo (Suzuki et al., during 2011). Although astrocytes can release lactate in response to glutamate uptake (Magistretti, 2006; Magistretti et al., 1999; Wender et al., 2000), here we describe another molecular pathway that leads to glycogen metabolism and lactate efflux as a result of metabolic

or neuronal activity. Soluble adenylyl cyclase (sAC) is sensitive to bicarbonate (HCO3−) and is posited to be a metabolic sensor (Zippin et al., 2001); however, its cellular distribution and function in the brain have not been identified. Due to their relationship to pH, HCO3− and HCO3−-sensitive enzymes represent a potentially effective way by which cells can initiate cellular cascades to meet metabolic demands that are often accompanied by changes in acid/base homeostasis. HCO3−-mediated sAC activation increases the production of the second messenger cAMP (Chen et al., 2000). In astrocytes, high levels of cAMP lead to the breakdown of glycogen (Sorg and Magistretti, 1992) and the production of lactate that can serve as an alternative energy source to neurons. Thus, new enzymes that lead to cAMP generation in astrocytes may be critical for mobilizing metabolic support for neurons during periods of intense neural activity or glucose deprivation.

We refer to this procedure as the “bootstrap test. Relative power spectra for the odor period were constructed by normalizing the raw power per frequency to the total power in the [2, 200] Hz interval (Figures 5A and 5B). Spike-LFP phase-locking was computed using the pairwise phase consistency method (see Supplemental Experimental Procedures; Vinck et al., 2012). As above, group averages were constructed using the stratified bootstrap

procedure, and their significance was assessed by comparing the T-statistic of the bootstrap distribution to the normal distribution. The authors would like to acknowledge the software tools or assistance provided PFT�� supplier by Prof. Kenneth Harris (Imperial College London, UK) for the use of KlustaKwik, A. David Redish (University of Minnesota, Minneapolis, MN) for the use of MClust, and Ruud Joosten and Laura Donga (Netherlands Institute for Neuroscience & University of Amsterdam, the Netherlands) for help with rat surgeries. This work was supported by the Netherlands Organization

for Scientific Research–VICI Grant 918.46.609 (to C.M.A.P.) and the EU FP7-ICT grant 270108 (to C.M.A.P.). M.v.W. and C.M.A.P. designed Venetoclax experiments; M.v.W. carried out experiments; V.T., I.R.S.F., and A.J.J. provided major technical assistance. M.v.W. and M.V. analyzed the data; M.v.W., M.V., and C.M.A.P. wrote the paper. “
“Animals and humans make an action quickly and preferentially if the action is expected to provide a valuable reward. This is the hallmark of “goal-directed behavior based on motivational values” (Dickinson and Balleine, 1994). Goal-directedness requires that the outcome of the action should be represented as a goal at the time of performance, and motivation is a mental state that embodies the goal-directedness. These considerations suggest that somewhere inside the brain there are neurons that represent motivation (or the value of an upcoming action) and that the motivational signal facilitates or inhibits the initiation/execution of the action. A prominent candidate for such a

goal-directed motivator is the basal ganglia. It has been shown that activity of STK38 many neurons in the basal ganglia is heavily influenced by expected reward values (Ding and Hikosaka, 2006; Hollerman and Schultz, 1998; Hong and Hikosaka, 2008; Joshua et al., 2009; Kawagoe et al., 1998; Lauwereyns et al., 2002; Pasquereau et al., 2007; Samejima et al., 2005; Sato and Hikosaka, 2002; Shidara et al., 1998). Anatomical studies have suggested that the basal ganglia act as an interface between nonmotor processes and motor processes (Haber, 2003). In particular, the limbic part of the basal ganglia, which includes the ventral striatum (VS) and ventral pallidum (VP) (Heimer and Wilson, 1975), may convert motivation signals to motor signals (Mogenson et al., 1980).

However, there was no constraint forcing the envelope adjustment to remain consistent with the subband fine structure (Ghitza, 2001), or to produce new subbands that were mutually consistent (in the sense that combining them would produce a signal that would yield the same subbands when decomposed again). It was thus generally the case that during the first few iterations, the envelopes measured at the beginning of cycle n + 1 did not completely retain the adjustment imposed www.selleckchem.com/products/Everolimus(RAD001).html at

cycle n, because combining envelope and fine structure, and summing up the subbands, tended to change the envelopes in ways that altered their statistics. However, we found that with iteration, the envelopes generally converged to a state with the desired statistics. The fine structure was not directly constrained, and relaxed to a state consistent with the envelope constraints. Convergence was monitored by computing the error in each statistic at the start of each iteration and measuring the signal-to-noise ratio (SNR) as the ratio of the squared error of a statistic class, summed across all statistics in the class, to the sum of the squared statistic values of that class. The procedure was halted once all MEK inhibitor classes of statistics were imposed with an SNR of 30 dB or higher or when 60 iterations were reached. The procedure was considered to have converged if the

average SNR of all statistic classes was 20 dB second or higher. Occasionally the synthesis process converged to a local minimum in which it failed to produce a signal matching

the statistics of an original sound according to our criterion. This was relatively rare, and such failures of convergence were not used in experiments. Although the statistics in our model constrain the distribution of the sound signal, we have no explicit probabilistic formulation and as such are not guaranteed to be drawing samples from an explicit distribution. Instead, we qualitatively mimic the effect of sampling by initializing the synthesis with different samples of noise (as in some visual texture synthesis methods) (Heeger and Bergen, 1995 and Portilla and Simoncelli, 2000). An explicit probabilistic model could be developed via maximum entropy formulations (Zhu et al., 1997), but sampling from such a distribution is generally computationally prohibitive. We thank Dan Ellis for helpful discussions and Mark Bee, Mike Landy, Gary Marcus, and Sam Norman-Haignere for comments on drafts of the manuscript. “
“During successful reading, the visual system efficiently transforms a complex input of contrast-defined strokes of ink into phonological and semantic word representations. After entering primary visual cortex (V1), visual information about words undergoes several transformations in extrastriate cortex, including regions localized to ventral occipitotemporal (VOT) cortex (Dehaene et al., 2005 and DiCarlo and Cox, 2007).

, 2005). These early patterning functions of Shh and

other Hedgehog family members are mediated by the transmembrane receptor Patched (Ptch) and the seven-pass transmembrane protein Smoothened (Smo) which signals through a “canonical” signaling pathway involving transcriptional regulators of the Gli family (Lum and Beachy, 2004). Surprisingly, Shh was more recently involved in axon guidance independently of its patterning functions (Charron et al., 2003). This axon guidance function is mediated by activation of a “noncanonical” receptor called Brother Of CDO (Boc), a Robo-related Ig/fibronectin superfamily member that can bind with high affinity to Shh and other Hedgehog family members (Okada et al., 2006). Callosal projections represents a great illustration of the precise, layer-specific, synaptic organization of cortical circuits (Fame et al., 2011; Figures 1A and

1B): neurons from the superficial layers 2/3 have their axon projecting medially Docetaxel supplier through the corpus callosum to establish topographically organized connections with the equivalent areal position in the controlateral hemisphere. These projections are layer selleck specific, making glutamatergic excitatory synaptic connections mainly with layer 5 pyramidal neurons projecting subcortically and with other layer 2/3 pyramidal neurons (Figures 1A and 1B). The axons of layer 2/3 callosally projecting neurons also make excitatory synaptic contacts with layer 5 neurons ipsilaterally (Figures 1A and 1B). The molecular mechanisms underlying mafosfamide the establishment of these layer-specific patterns of synaptic connectivity are largely unknown. In the present study, Harwell et al. (2012) observed that Shh expression persists in the postnatal mouse neocortex long after its “patterning” function during embryonic development is over. Interestingly, Shh expression is largely restricted to pyramidal neurons in layer 5. Using combination of retrograde

axon tracing, layer-specific marker expression and lineage tracing using a Shh-Cre;Rosa26-LoxP-STOP-LoxP-YFP reporter mouse line, the authors identified that Shh expression is largely restricted to CTIP2-positive, corticospinal-projecting neurons of layer 5b. Conditional deletion of Shh from most pyramidal glutamatergic neocortical neurons by crossing conditional Shh knockout mice with the dorsal telencephalon-specific driver Emx1Cre (Gorski et al., 2002) has no major consequence on brain patterning, most likely because it does not disrupt Shh expression at the ventral midline where it plays its patterning function in the embryonic telencephalon. Conditional deletion of Shh seemed to have little or no effect on the number, survival, and axon guidance of corticospinal projecting neurons. However, the authors observed layer-specific dendritic defects: in Shh cKO brains, neurons displayed reduced dendritic arborization and a reduced spine density specifically in layer 5, whereas neurons from superficial layers 2/3 appeared unaffected.

Transplantation of interneuron precursors into the postnatal cortex reopens the critical period of ocular

dominance plasticity when transplanted interneurons reach a cellular age equivalent to that of endogenous inhibitory neurons during the normal critical period (Southwell et al., 2010). Recent efforts to derive cortical interneurons from human pluripotent stem cells (hPSCs) or human-induced pluripotent stem cells (hiPSCs) have also emphasized the ability of these cells to differentiation according to an intrinsic program of maturation. Both in vitro and after transplantation into the rodent cortex, human GABAergic interneurons Sunitinib supplier derived from hPSCs or hiPSCs mature following a protracted timeline of several months, thereby mimicking the endogenous human neural development (Maroof et al., 2013 and Nicholas et al., 2013). Altogether, these findings suggest that multiple aspects of the selleck screening library integration of interneurons in cortical networks are regulated by the execution of a maturational

program intrinsic to inhibitory neurons. Several mechanisms dynamically adjust the balance between excitation and inhibition in the adult brain (Haider et al., 2006 and Turrigiano, 2011). However, it is likely that developmental programs are also coordinated to play an important role in this process. Indeed, the relative density of pyramidal cells and interneurons remains relatively constant from early stages of corticogenesis, when both classes of neurons are still migrating to their final destination

(Sahara et al., 2012). One possibility is that the generation of both classes of neurons is coordinated through some kind of feedback mechanism that balances proliferation in the pallium and subpallium. Alternatively, the production of factors controlling Calpain the migration of GABAergic interneurons to the cortex might be proportional to the number of pyramidal cells generated. For example, it has been shown that cortical intermediate progenitor cells (IPCs) produce molecules that are required for the normal migration of interneurons (Tiveron et al., 2006), and mutants with reduced numbers of IPCs have a deficit in cortical interneurons (Sessa et al., 2010). Cell death is another prominent factor regulating neuronal incorporation during development (Katz and Shatz, 1996 and Voyvodic, 1996). It has long been appreciated that a sizable proportion of inhibitory neurons is eliminated from the cerebral cortex through apoptosis during the period of synaptogenesis (Miller, 1995), and recent work estimated that approximately 40% of the interneurons in the cortex perish around this time (Southwell et al., 2012). Similarly, only about half of the adult-born granule cells survive more than a few days after reaching the olfactory bulb (Petreanu and Alvarez-Buylla, 2002). The mechanisms controlling the death of newborn olfactory bulb interneurons have been studied with some detail.

The mean FR in the analysis epoch did not significantly differ between the two conditions, i.e., whether the current Go trial was preceded by a Stop or a Go trial. In contrast, VarCE displayed a strong modulation by the task history and was significantly higher in case the preceding trial was a Stop as opposed to a Go trial (Figure 2B). Single-unit analyses showed a consistent effect across the whole population (Figure S2A). We also tested the correlation of task history with VarCE during

Stop trials in two different contexts: when a Stop trial was preceded by Go (t −1) and Stop (t − 2) trials or by two consecutive Go trials. We observed the same modulation in VarCE by task history (Figure S2B). Interestingly, the difference in VarCE between both conditions disappeared about 70 ms after the presentation of the Stop signal. This latency is consistent with the average processing delay of visual information in PMd (Cisek and Kalaska, 2005). In a next analysis, we assessed Fluorouracil nmr the relationship between task history, VarCE, and performance (Figures 2C, 2D, and S2C). This analysis revealed that mean and SD of RT closely mirrors the effect of task history on VarCE over a wide range of task history conditions. The three factors, mean RT, SD of RT, and VarCE, increased with an increase

in the number of previous Cell Cycle inhibitor Stop trials, while they decreased with an increase in the number of preceding Go trials. Moreover, changes in mean RT over a range of trial history conditions are due to systematic shifts of the entire RT distributions (Figure S2D). We observe that the mean RTs are very well correlated with VarCE (Figure 3A) and that RT and VarCE distributions seem to have similar

shape (Figure 3B). The mean FR for the same conditions did not show any variation (Figure S2E). Interestingly, the modulation of VarCE also depends on the difficulty of the previous trial (Figure S2F), so that its value increased as the SSD in the Stop trial preceding the Go trial increased. Thus, these results suggest that the influence of task history is reflected in the variance of neuronal activity in PMd and Idoxuridine that both variables, VarCE and trial history, are linearly correlated with performance. In order to understand the neural mechanisms causing the observed behavioral and across-trial neuronal response variability differences due to varying trial history conditions, we used a mean-field approximation (Wilson and Cowan, 1972) of a biophysically based binary decision-making model (Figure 4A). The model receives two segregated inputs: perceptual evidence provided by the visual cues (Stop and Go signals) and a task history signal provided by a monitoring system. The model has two populations of excitatory neurons: one population is sensitive to the appearance of the Go signal (λgo; Go pool), while the other population is sensitive to the appearance of the Stop signal (λstop; Stop pool). The two populations mutually inhibit each other.

Subsequent studies confirmed this result in different DNA Damage inhibitor neurons (Wu et al., 2005 and Yao et al., 2006) and revealed that local protein synthesis underlies growth-cone adaptation, gradient sensing, and directional turning

in growing axons (Leung et al., 2006, Ming et al., 2002, Piper et al., 2005 and Yao et al., 2006). In addition, axonal protein synthesis is elicited in response to injury and plays key roles in axon regeneration and maintenance (Jung et al., 2012, Perry et al., 2012, Verma et al., 2005, Yoon et al., 2012 and Zheng et al., 2001). Neuronal function is highly dependent on spatially precise signaling. Increasing evidence indicates that the complex morphology of neurons has created biological compartments that subdivide the neuron into spatially distinct signaling domains important for neuronal function LY294002 in vitro (Hanus and Schuman, 2013). Dendritic spines represent a specialized (“classical”) cellular compartment in which subsets of specific proteins (e.g. receptors, channels, signaling molecules, and scaffolds) are collected together with a common function for receiving and processing electrical and chemical input. Spines have a

distinct structural morphology and, as such, are easy to classify as a compartment. Although spines are small (∼1 μm3), they can still be subdivided into further functional compartments (see Chen and Sabatini, 2012 for review) with multiple microdomains, raising the question of how a compartment is defined. next For example, a recent superresolution imaging study demonstrated that, within synapses, AMPA receptors are clustered into small nanodomains (∼70 nm in diameter) that contain on average ∼20 receptors (Nair et al., 2013). These nanodomains are dynamic in both their shape and position and may have a limited lifetime. Anatomically and functionally distinct compartments also exist in axons, such as the growth cone, the axon initial segment, and terminal arbor. Equally, there are examples

of compartments that exhibit no obvious “anatomical” specializations. In axons, for example, some membrane proteins are localized to restricted segments of the axon (Fasciclins, Tag1/L1, Robo) (Bastiani et al., 1987, Dodd et al., 1988, Katsuki et al., 2009 and Rajagopalan et al., 2000) indicative of plasma-membrane compartmentalization. In addition, second-messenger signaling molecules such as calcium and cyclic nucleotides, once thought to signal extensively throughout a cell, are now known to be highly regulated such that increases in concentration can be confined to a small space, creating a signaling compartment. Selective activation of a single spine on a dendrite, for example, can provide the receiving neuron with information about a specific stimulus (Varga et al., 2011).

5 nm. The settled nanoparticles in centrifuge tube were redispersed in 5 ml fresh phosphate buffer saline (pH 7.4) and returned to the dissolution media.8 and 9 The dissolution data of each batch was fitted to various kinetic equations and mechanism of drug Libraries release investigated. Eqs (5), (6) and (7) are Zero order, First order, Higuchi

model and Korsmeyer–Peppas model respectively. equation(4) Qt=K0tQt=K0t equation(5) InQt=InQ0−K1t equation(6) Qt=Kht1/2Qt=Kht1/2 equation(7) Mt/Mα=KptnMt/Mα=Kptnwhere, Qt is the percentage of drug released at time t, Q0 is initial amount of drug present in the formulation and K0, K1, Kh are the constants of equations. Regression coefficient (R2) was determined from slope of the following plots: Cumulative Selumetinib mouse % drug release vs Time (Zero order kinetic model), Log cumulative of % drug remaining vs Time (First order kinetic model), Cumulative % of drug release vs Square CX-5461 manufacturer root of Time (Higuchi model), Log cumulative % drug release vs Log time (Korsmeyer–Peppas model). 8 and 10 In Korsmeyer–Peppas model, first 60% of drug release was fitted and release exponent “n” was calculated

which is indicative of drug release mechanism. According to Korsmeyer theory, if ‘n’ is 0.45 then drug release will follows Fickian diffusion mechanism, for 0.45 < n < 0.89 follows Anomalous (non-Fickian) diffusion, for n = 0.89 case II transport and for n > 0.89 diffusion mechanism will super case II transport. 11 Results were evaluated by one-way analysis of variance (ANOVA) using Graphpad Instat® Version 3.06 software, where p < 0.05 was taken to represent a statistically significant difference. REPA-EC NPs were prepared by solvent diffusion technique using ethyl acetate as internal organic phase. Both REPA and EC are completely soluble in ethyl acetate therefore there was no possibility of drug loss from polymer due to homogenous matrix. In this study

we used EC of 300 cps viscosity range as drug carrying polymer. Due to high viscosity range it formed a saturated solution with ethyl acetate organic solvent. Both REPA and EC were hydrophobic in nature, thus hydrophobic polymer encapsulate larger amount of hydrophobic drug. When organic phase added in external water phase containing surfactant, REPA-EC matrix immediately found start to precipitate because of insoluble in water and fast diffusion of ethyl acetate. Subsequently REPA-EC matrix was disrupted in nano size by high pressure homogenizer. Polyvinyl alcohol is a better surfactant in terms of encapsulation efficiency, drug content and particle size. PVA has greater propensity to migrate toward the surface of EC nanoparticles and stabilizes its surface more effectively and hence accomplish a lower particle size.9 Ethyl acetate is high soluble in water (8.7% w/v) and having less interfacial tension (6.78) with water due to which fast diffused out in external water phase at the time of solidification of nanoparticles.