The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of  cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a  model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level. Dopamine is a neurotransmitter that has been implicated in a wide variety of cognitive and motor functions; it is depleted in Parkinson's disease, disrupted in schizophrenia and plays a central role in working memory, reinforcement learning and other cognitive functions. In this paper, we present a straightforward and neurophysiologically grounded explanation for the diversity of functions and pathologies that implicate dopamine. This explanation rests on a principled approach to the nature of action and perception called active inference. This approach suggests that (Bayes) optimal perception and consequent behaviour depends on representing uncertainty about states of the world in terms of the precision (inverse amplitude) of their random fluctuations. Crucially, this uncertainly can be encoded by the same postsynaptic gain of neurons that is modulated by dopamine. This means that changing the levels of dopamine changes the level of uncertainty about different representations. To substantiate this idea, we simulate dopamine depletion in a hierarchical sensorimotor network to show that a single function of dopamine (encoding precision in terms of postsynaptic gain) is not only sufficient to account for commonly observed behaviours following dopamine depletion but also provides a unifying perspective on many existing theories about dopamine. It offers a straightforward account of dopamine in optimising behaviour in the context of  predictive coding. Our focus is on the consequences of depleting dopamine and simulating perseveration that is characteristic of Parkinson's disease, using synthetic neuronal models based upon Dopamine has been implicated in a bewildering variety of processes and pathologies in the human brain; ranging from cortical excitability to attentional deficits active inference in a series of previous papers This article takes a different view and considers that behaviour is Bayes-optimal in the sense that it maximises the Bayesian evidence for an actor's model of the world or, equivalently, minimises surprise. We have formulated this as The Bayesian perspective suggests that there are only two sorts of things that need to be inferred about the world; namely, the state of the world and uncertainty about that state. We have suggested that predicted states of the world are encoded in terms of synaptic activity, while uncertainty is encoded by synaptic gain that encodes the precision (inverse amplitude or variance) of random fluctuations about predicted states incentive salience in psychology aberrant salience in psychopathology The motivation and mechanisms behind the role of dopamine considered in this paper are exactly the same as we have proposed for the attentional modulation of postsynaptic gain in sensory processing per se and will try to reproduce the empirical findings of behavioural reinforcement paradigms, using phasic dopamine discharges that shift striatial neurons into an up state to increase their gain or precision In this paper, the hypothesis that dopamine release reports precision or uncertainty is based purely on its synaptic physiology. However, there is definitive neurophysiological evidence for this role of dopamine The models and methods section reviews the theory on which subsequent simulations are based. This section presents a brief review of the free energy principle, with a special focus on active inference and the role of synaptic gain in encoding precision. The basic theory and ensuing differential equations used to simulate neuronal responses are exactly the same as those used to illustrate perceptual inference, learning, attention and action in a series of previous papers . This foIn this section, we briefly overview the free energy principle and active inference to frame the role of dopamine examined later. The free energy principle proposes that the states and infrastructure of a self organising system, such as the brain, should minimise the free energy of the sensory states it samples Crucially, high-level sensorimotor representations can be dynamic in nature, with itinerant dynamics (on attractor manifolds) that embody prior beliefs about the sequence of sensorimotor events or trajectories that will unfold in the near future. These can be regarded as central pattern generators or attractors that provide proprioceptive and sensory predictions for sensorimotor integration; in other words, representations of affordance ad hoc; however, they are based on just three assumptions:The equations and simulations used in this paper may appear a bit complicated and The brain minimises the free energy of sensory inputs defined by a generative model.The generative model used by the brain is hierarchical, nonlinear and dynamic.Neuronal firing rates encode the most likely state of the world, under this model.slaving principle from statistical physics Laplace code and is arguably the simplest and most flexible of all neural codes The first assumption is the free energy principle, which leads to active inference when considering both representations and action. This principle has to be true, in the sense that a failure to minimise free energy means that the brain will entertain increasingly surprising sensations and, at some point, will cease to exist in an ergodic sense. The second assumption is motivated easily by noting that the world is both dynamic and nonlinear. Hierarchical causal structure emerges inevitably from a separation of temporal scales. This can be seen most clearly in the Given these three assumptions, one can simulate a whole variety of situations and processes by simply specifying the particular equations that constitute the generative model. The resulting perception and action is specified completely by the above assumptions and can be implemented in a biologically plausible way as described in previous applications listed in To perform neuronal simulations under this framework it is only necessary to integrate or solve Equation 1 to simulate neuronal dynamics that encode the conditional predictions and ensuing action. Conditional predictions depend upon the brain's generative model of the world, which we assume has the following  formGiven the form of the generative model (Equation 2) we can now write down the differential equations (Equation 1) describing neuronal dynamics in terms of (precision-weighted) prediction errors on the hidden causes and states generalised filtering or generalised predictive coding It is difficult to overstate the generality and importance of Equation 3: its solutions grandfather nearly every known statistical estimation scheme, under parametric assumptions about additive or multiplicative noise Equation 3 shows that precision modulates the responses of prediction error units to their presynaptic inputs. Here, we associate precision with dopaminergic neuromodulation of these responses. The action of dopamine is mediated by a family of transmembrane G protein-coupled receptors Although dopamine appears to be a natural candidate to modulate principal cells reporting prediction error, Equation 3 does not tell us whether prediction errors are modulated before or after they are computed. Both mechanisms are biologically plausible: for example, superficial pyramidal cells encoding prediction error could be modulated by D1 receptors on the soma or initial segment, after the integration of signals subtending prediction errors in the dendritic tree. Conversely, precision dependent modulation could be applied at a synaptic level to all presynaptic inputs. For dopamine, the balance of evidence points to the latter mechanism Dopamine innervation in the human prefrontal cortex exhibits a distinct bilaminar distribution with dense bands of fibres in the superficial and deep layers empirical priors, because they are optimised in relation to sensory data. Furthermore, precision or neuromodulation of synaptic gain will affect action and motor control, because action is driven by (proprioceptive) prediction errors at the sensory level that have their own gain or precision:Clearly, in sensorimotor hierarchies Affordance is generally conceived of as the opportunities for action offered by the environment to an agent In summary, we have derived equations for the dynamics of perception and action using a free energy formulation of adaptive  exchanges with the world and a generative model that is both generic and biologically plausible. In what follows, we use Equations 3 and 4 to simulate neuronal and behavioural responses. A technical treatment of the material above will be found in The preceding scheme allows one to simulate  responses in terms of neuronal activity and motor behaviour, under any plausible generative model. Here, we consider a particular model, described in terms of the functions in Equation 2 that leads to a sequence of pointing movements, elicited by a sequence of visual cues. This model of sensorimotor integration provides the basis for simple simulated lesion experiments, in which we can deplete levels of simulated dopamine (precision) in different parts of the brain, and examine the consequences.Because the differential equations governing perception and action are coupled (Equation 1), we need to specify two mappings: the generative model used by the brain, whose inversion maps from sensations to action, and the process by which action produces sensations. To distinguish between real states generating sensory information and the hidden states assumed by the generative model, we will use bold and italic variables respectively. Sensory input was generated using the following equations, which constitute real world dynamics that are hidden from the agent:The second differential equation describes how changes in arm position and target salience depend upon action and exogenous causes respectively: the direction of the arm changes as a sigmoid (hyperbolic tangent) function of action and decays back to its resting position in the absence of action. The salience of each location is increased by exogenous causes Clearly, to integrate Equation 5 we need not only the exogenous causes  but also action that depends upon the perceptual inversion of a generative model. The generative model here was chosen to include several features of sensorimotor integration and hierarchical dynamics in the simplest way possible. It considers the brain to model the sensory world as a succession of unstable fixed points in some abstract state space. In other words, the agent expects the world to change continuously, with an itinerant (wandering) trajectory, visiting different states in succession. These reflect prior beliefs about forthcoming sensory events, and are encoded by differential equations that embody metastable dynamics; e.g., winnerless competition; The generative model used by our simulated agent had two levels and the following form (a particular case of Equation 2):However, we will consider a more sophisticated and realistic model, in which the agent has prior beliefs about the sequence in which targets will appear. This sort of prior belief could be instantiated by repeated exposure to the same sequence. These prior beliefs are encoded by the function self see . The speThe final part of the model endows the agent with the concept that cues may or may not be ordered. We model this in terms of the hidden cause that controls the speed of the sequence. Crucially, this hidden cause is itself a softmax function of a hidden state that is part of a slower itinerant cycle (by factor of eight), governed by the same winnerless competition among the hidden states; In summary, the agent believes that it will point towards salient cues when they appear. Furthermore, it believes that these cues would appear one at a time; either in a fixed (clockwise) sequence or with no sequential contingencies. Although this is a very simple model of the world, it allows us to demonstrate sensorimotor integration in the context of cued motor actions, biasing of action selection in terms of sequential anticipations and set switching that depends upon recognising the context  in which cues appear. Our particular interest here is in how manipulating the precision (dopamine) at various levels in this hierarchical model will impact on cued responses. The interesting behaviour depends entirely upon the prior beliefs entailed by the form of the generative model and its equations of motion. These are shown schematically in If we substitute the generative model in Equation 6 into the message passing  scheme in Equation 3, we arrived at the network architecture shown in The model above is sufficient to engender cued reaching movements, which are anticipatory if the agent correctly infers that the cues are presented in a fixed (clockwise) sequence. However, if we reverse the order of the stimuli, there should be accuracy and reaction time costs, due to the fact that the sequence cannot be predicted under clockwise beliefs about the sequence. Furthermore, there should be a set switching cost as the hidden states at the second (context) level are inferred and the itinerant dynamics at the first (affordance) level are suppressed. When we integrated Equation 1, this is precisely what was found:The same simulated neuronal responses are shown in In summary, we have created a generative model that illustrates the itinerant and dynamic sensorimotor constructs that might be used by the brain to predict cued sequential behaviours and set switching in response to changing contingencies. It is worth noting, that this relatively simple model has implicitly modelled (and integrated) a number of apparently disparate processes in cognitive neuroscience: for example, Bayes-optimal sensorimotor integration, evidence accumulation, anticipation, short term (working) memory, action selection, set switching and a simple form of reversal learning (in terms of switching to a new contingency). We mean this in the straightforward sense that to perform accurately, the simulated agent has to remember the sequence of cues in terms of delay period activity in the premotor and prefrontal cortex In this section, we repeat the above simulations under different levels of precision in the putative targets of dopaminergic projections. This is meant to simulate depleted levels of dopamine; acting at postsynaptic D1 receptors to reduce postsynaptic gain see . First, The effects on conditional expectations of hidden states and causes are shown in This means that it takes longer before the second level expectations accumulate sufficient evidence to make them switch, following the reversal of stimulus order. At the lowest level of simulated dopamine, this switch fails completely and the agent always expects the next target to appear in the wrong (clockwise) location. The behavioural consequences of this are shown graphically in the lower panels of First, irrespective of the level of dopamine, reaction times are faster when the next cue can be anticipated. Furthermore, there is a price to be paid for this anticipatory speeding, when sequential anticipations are violated. This is reflected in the increased reaction times at the point of sequence reversal for a couple of trials. It is these transient decreases in performance that index the switching costs hypothesised earlier. Crucially, the effect of dopamine depletion is to exacerbate both the switching costs and the behavioural slowing when sequential predictions no longer hold. In the limit of very low dopamine, and a complete failure to switch sets (infer a context change), there is a marked impairment in performance that persists following reversal. Perhaps the most important result in On the basis of these results, one might predict that the greatest difference between Parkinson's patients on and off dopaminergic medication would be expressed most acutely in trials that violated expectations established during sequential cueing. Conversely, there should be relatively small differences in reaction times when stimuli are presented in the correct sequence: cf., decreases reaction times. This is perfectly sensible because increasing the precision at higher levels of a hierarchical model has the opposite effect to increasing the precision of lower level prediction errors. In other words, as dopamine levels in the premotor cortex increase, the agent becomes overly confident about its top-down  prior expectations, even in the face of precise sensory information. This overconfidence is manifest in terms of a slight impairment in reactions elicited by sensory cues. However, the overconfidence about affordance enables the second level of the generative model to recognise the change in context more efficiently (quickly). This induces a trade-off between the efficiency with which cues elicit movements and the efficiency of set switching.Finally, we repeated the simulated lesion experiments above by reducing the precision in other cortical and subcortical structures in receipt of SN/VTA projections. The most interesting results are shown in In this section, we have seen how simulated dopamine depletion is manifest in terms of neuronal responses encoding Bayes-optimal inferences about sensorimotor contingencies and in terms of behavior. The key point made by the simulations is that although dopamine may have a singular mechanism of action and computational function  the physiological and behavioral correlates of dopamine changes depend on where in the brain they are expressed. This reiterates the point made in the introduction that understanding the role of dopamine may call for a multilateral perspective that accommodates the delicate balance among distributed responses that underlie the functional anatomy of behavior. The simulations in this section can be regarded as a proof of principle that a single mechanism can lead to the diverse functional consequences seen empirically In summary, contrary to what is often assumed, dopamine may not report the prediction error on value but the value (precision) of prediction errors. If this is the case, one would anticipate different behavioral deficits following dopamine depletion, depending on which prediction errors were affected. Strategically, it may be better to ask not what the function of dopamine tells you about a model but what a model tells you about the function of dopamine. In this paper, the function of dopamine is to modulate postsynaptic gain, while the model mediates between this  function and its behavioral and neurophysiologic consequences. See also ad hoc model of dopaminergic function but use exactly the same principles, equations and numerics used previously to address a wide variety of processes in cognitive neuroscience: http://www.fil.ion.ucl.ac.uk/spm/).In this paper, we have presented a simple model of cued reaching movements and set switching that is consistent with the notions of salience and affordance. Furthermore, we have simulated some latent symptoms of Parkinsonism by reducing the precision of cues that have affordance. Reducing this precision (dopamine) delays and can even preclude set switching, with associated costs in behavioral accuracy. When the precision of sensory cues is removed completely, we obtain autonomous behavior that is prescribed by the itinerant expectations of the agent (results not shown). Crucially, these simulations are not based on an In short, we have focused on the role of dopamine in balancing the influence of bottom-up sensory information and top-down  prior expectations during perceptual inference and consequent behavior. This role is consistent with many other theoretical treatments of dopamine in modulating top-down effects and signal-to-noise The simulations presented in this paper do not attempt to cover all the physiological and computational processes that dopamine may mediate. Our results pertain to classical neuromodulatory postsynaptic consequences of changes in tonic dopamine release  on principal cells. There are many aspects of dopaminergic function that are not addressed by these simulations: for example, modulation of long-term synaptic plasticity, or the differential roles of various pre- and postsynaptic dopamine receptors. Furthermore, we have only considered the effect of tonic or baseline dopamine levels  and have ignored dopamine fluctuations on a shorter time scale: for example, the phasic release of dopamine in response to specific cues or its rapid clearance in the striatum by the dopamine active transporter. However, the aim of the present work was to highlight the computational, neurophysiologic and anatomic considerations that suggest a unitary role for dopamine in the encoding of precision.Bayes optimality (implicit in free energy minimization) replaces optimal control .There is a large literature on modeling dopamine in the context of choice and motor behavior The ideas and simulations in this paper just show that dopamine fits comfortably into a principled if somewhat abstract formulation of action and perception. Specifically, it has the physiological characteristics that would be required to fulfill a central role in Bayes-optimal inference; namely, to encode precision or uncertainty. This is why we have focused on the neuromodulation of postsynaptic gain in cortical cells, mediated by D1 receptors. The theoretical constraints we have considered do not really allow us to say very much about the functional anatomy of the cortico-basal ganglia-thalamic loops. Having said this, the dynamics necessary for active inference are not inconsistent with many aspects of known functional anatomy. For example, the basal ganglia are believed to act on cortex through a process of focused disinhibition Although the computations that minimize free energy do not predict the details of functional anatomy (in the same way that maximizing adaptive fitness does not predict the details of a phenotype), it does provide some interesting insights into large-scale functional anatomy. These insights rest upon the notion that the brain is a generative model of its environment. This means that causal regularities in the environment should be recapitulated in its neuroanatomy. The trick here is to think of the brain as generating predictions of sensory input using its backward (top-down) connections; in other words, to imagine the brain without the forward connections that are used for perceptual inversion of its generative model. The picture that emerges is a fine lacework of cortical projections from high-order associative cortex that radiate to sensory cortical areas and then send top-down predictions of sensory input to thalamic nuclei. A nice example here is the distinction between what and where pathways in visual processing First, the fact that the outputs of the basal ganglia are to thalamic nuclei suggests that the basal ganglia are at a hierarchically lower level than prefrontal cortex. For example, the ventral lateral nucleus receives inputs from the basal ganglia  and sends outputs to the primary motor cortex. The ventral lateral nucleus is juxtaposed to the ventral posterolateral nucleus in receipt of proprioceptive and somatosensory afferents via the posterior column-medial lemniscus pathway. The implicit hierarchical level of the basal ganglia suggests that the prefrontal cortical projections to the striatum should be of the backwards type . Indeed, the cortical pyramidal neurons projecting to the striatum are located in layers II–VI, but the densest projections come from layer V Second, in active inference, the balance between top-down prior beliefs and bottom-up sensory information is controlled by the relative precision of prediction errors at higher and lower levels of cortico-basal ganglia-thalamic hierarchy. This suggests that there will be an antagonistic control of dopaminergic projections to the prefrontal cortex (higher level) and equivalent projections to the striatal (lower-level). In anatomical terms, this may be reflected in the segregation of the cells of origin of the nigrostriatal dopamine projections (from the substantia nigra) and the cortical projections . Clearly, there are several important functional associations in dopaminergic systems that may or may not be understandable at this level of analysis. Key examples here include the opposing effects of the direct and indirect pathways Finally, it is interesting to note that the number of parameters of statistical models is generally much greater than the degrees of freedom of their variance or precision estimates. For example, a classical statistical model with both between and within subject factors can have hundreds of parameters yet can be inverted using just two precisions (modeling within and between subject variability). This might be a simple explanation for the fact that the number of dopaminergic cells (encoding precision or context) is much smaller than the myriad of cells (encoding hidden causes or content) needed to parameterize the world.per se. The alternative and simpler approach, provided by active inference, replaces rewards with prior beliefs about how the world should unfold and motivates these priors in evolutionary and ethological terms. Put simply, survival does not depend on seeking out rewards; it depends upon avoiding surprising encounters and physiological states that are uncharacteristic of a given phenotype. It is therefore sufficient to minimize surprise (free energy), which is a problem of (active) inference, not reinforcement learning.The current work presents a covert challenge to standard theories of decision-making and motor control based upon the concept of reward or value. As hinted at in the introduction, there is an inherent circularity in reward-based accounts of behavior that may obscure deeper questions about behavior. This is because  reward is defined as a process that reinforces or modifies behavior. As such, it is circular to use reward to explain behavior. Although the concept of reward may provide beautifully self-consistent descriptions of behavior in terms of optimizing expected reward; e.g., At no point in the formulation above do we refer to reward or value; the only optimization was to minimize surprise about expected outcomes. In the context of hierarchical inference, the potentially potent role of dopamine is obvious. This is because the delicate mixing of bottom-up sensory information, with top-down priors, to produce conditional beliefs (and action) is exquisitely sensitive to the precision or certainty ascribed to representations at different levels of a hierarchy. This is a generic and ubiquitous aspect of hierarchical inference; for example, inference about treatment effects in group studies rests on veridical estimates of within-subject, relative to between-subject variability (precision). The idea here is that dopamine reports the precision (variability) in hierarchical models used by the brain to infer the causes of sensory data. In this view, the task instructions and cues provided to subjects in decision-making experiments do not, in themselves, constitute rewards but are used to instill prior beliefs about how they should behave.Although we have questioned the epistemological status of reward on the grounds of circularity, one cannot deny that certain cues are inherently rewarding; for example, those induced by appetitive stimuli. In this sense, one might associate reward with the affordance of (hidden) causes in the environment that elicit obligatory volitional and autonomic responses. In this context, the tautology of reward is resolved by noting that reward is a perceptual attribute of a cue not a cause of behavior: in active inference, behavior is caused by prior beliefs and ensuing exchange with environment. These beliefs ensure certain states are occupied frequently, where these states are, by definition, valuable. In this view, value and reward are consequences not causes of behavior. Behavior is caused by the environment and prior beliefs that are established epigenetically or through experience dependent learning. This means that reward is a perceptual (hedonic) consequence of behavior, not a cause.per se. This account may explain why dopaminergic responses do not behave as reward prediction errors generally. For example, a significant proportion of dopamine neurons increase their firing to aversive stimuli, and cues which predict them The argument in this paper is that the information (prediction error) conveyed by rewarding cues cannot be encoded by dopamine, because dopamine cannot excite postsynaptic the responses that would be needed to mediate the influence of that information: Dopamine can only modulate the postsynaptic responses to glutamatergic or other neurotransmitter release. This is not to say that rewarding cues will not excite dopamine cells. Indeed, this is the hypothesis put forward here – namely that rewards or cues with particular affordance can select exteroceptive, interoceptive and proprioceptive processing channels that mediate behavioral responses. In short, dopamine may report the precision or confidence about reward, not reward The simulations in this paper focused on the depletion of dopamine. However, increasing levels of, or sensitivity to, dopamine in the basal ganglia has been implicated in several movement disorders, including tardive dyskinesia Although we have not dealt with the phasic release of dopamine and the implicit optimization of precision, the current formulation does make some strong predictions about the modulation of stimulus bound or event related responses. If tonic dopaminergic levels modulate postsynaptic gain, one would expect to see amplified responses in the post synaptic targets of dopamine projections that report (sensory) prediction errors. This means that one might expect greater evoked responses to the same stimuli when presented in a predictable, as opposed to an unpredictable context. Indeed, this is the basis of an explanation for the mismatch negativity: an increase in the event related potential elicited by a novel or oddball stimulus, presented in a predictable train of stimuli. The proposed mechanism rests on prediction errors that are afforded too much precision The central role of precision-weighted prediction errors also emerges from other computational perspectives. For example, one can formulate dynamic inference about the causes of sensory cues without reference to neurobiology but as a generic hierarchical Bayesian model per se, because we have assumed that the prior beliefs used to generate behavior were already known in the simulations. In future work, we will use the simulations above to look at the acquisition of prior beliefs in terms of sequence learning and the effect that dopamine has on this learning. The key issue here is that any effects of dopamine on learning and activity-dependent plasticity (using free energy minimization) are mediated vicariously through its effects on neuronal activity. This means that the only thing specific about dopamine, in the context of learning, is due to the restriction of its projections to the (extended) motor system. This suggests that sensory learning will be largely unaffected by dopaminergic manipulations  to the extent that it is independent of motor learning. We will explore this in future work, in which we will treat (phasic) dopamine release as encoding state-dependent precision, given contextual cues like conditioned stimuli: see We have not touched upon learning vice versa, because distinct exteroceptive and motor predictions only arise at lower (sensory) levels of the hierarchy. In other words, we could regard the simulations above as simulations of actions that disclose salient cues . This perspective may be useful when trying to understand the central role of dopamine in action selection based upon conditioned stimuli on the one hand, and optimizing behavior to access conditioned reinforcers on the other.One interesting aspect of the simulations in this paper is that they lend themselves nicely to higher-order (operant) learning paradigms: The behavior generated by high-level sensorimotor constructs does not distinguish between sensations caused by motor acts or


Caenorhabditis elegans. We found that the fat reducing effects of dopamine were dependent on dopaminergic receptors and a set of fat oxidation enzymes. Our findings reveal an ancient role for dopaminergic regulation of fat and suggest that dopamine signaling elicits this outcome through cascades that ultimately mobilize peripheral fat depots.The regulation of energy balance involves an intricate interplay between neural mechanisms that respond to internal and external cues of energy demand and food availability. Compelling data have implicated the neurotransmitter dopamine as an important part of body weight regulation. However, the precise mechanisms through which dopamine regulates energy homeostasis remain poorly understood. Here, we investigate mechanisms through which dopamine modulates energy storage. We showed that dopamine signaling regulates fat reservoirs in  Obesity has emerged as a major health problem that is implicated in several human diseases A number of findings point to the neurotransmitter dopamine as a central regulator of energy balance. Knock-out mice lacking dopamine exhibit dramatically reduced food intake and die of starvation a few weeks after birth Caenorhabditis elegans provides a genetically tractable system for rapid and functional analysis of fat regulatory pathways in the context of an intact animal C. elegans neural sensory mechanisms gauge environmental conditions and coordinate myriad behavioral and physiological responses C. elegans have been best characterized in the context of food-seeking behaviors. In hermaphroditic worms dopamine production and release are confined to the ADE, PDE and CEP neurons, which are primarily mechanosensory C. elegans responses to food cues C. elegans to investigate the effects of dopamine signaling on fat content. To this end, we measured fat reservoirs of animals exposed to different concentrations of dopamine and carried out genetic analyses of potential molecular targets implicated by fat deposition.In this study we used the nematode dop-1(vs101), LX702 dop-2(vs105), LX703 dop-3(vs106), RB1254 dop-4(ok1321), CB1112 cat-2(e1112), LC33 bas-1(tm351), RM2702 dat-1(ok157), LX706 dop-1(vs100);dop-2(vs105), LX705 dop-1(vs100);dop-3(vs106), CF2808 dop-1(vs100);dop-4(ok1321), LX704 dop-2(vs105);dop-3(vs106), LX734 dop-1(vs100);dop-2(vs105);dop-3(vs106), CF2805 dop-1(vs100);dop-2(vs105);dop-3(vs106);dop-4(ok1321), RB1700 f59f4.1(ok2119), MT9668 mod-1(ok103), VS24 kat-1(tm1037), VS20 hjIs67[atgl-1p::atgl-1::GFP]. All strains were outcrossed at least 4×. Strains were synchronized by hypochlorite treatment of gravid adults. Eggs were allowed to hatch overnight in M9 and used next morning. For all experiments strains were kept at 20°C.Nematodes were maintained using standard procedures as described previously Dopamine hydrochloride  was freshly dissolved in 0.1 M HCl and added to the top of NGM plates seeded with either OP50 or RNAi clone (HT115) to the desired concentration. For vehicle-control plates, a 0.1 M HCl solution was used. For experiments, L4-stage worms were transferred to vehicle or dopamine containing plates and the desired parameter measured 24 hours later unless otherwise noted. For biochemical and fatty acid oxidation assays L3-stage animals were used to avoid interference of yolk from eggs.Pharyngeal pumping was measured in healthy animals on food at room temperature. The number of contractions of the pharyngeal bulb was counted over 10 s, in triplicate, and the average multiplied by 6 to give pumps/min. All experiments were repeated at least twice.Animals were transferred one by one to fresh plates seeded with 100 µL of OP50. After a 10 minute adaptation period, 1 minute digital videos were captured at ∼12 frames/s using a dissecting microscope outfitted with a CCD camera. Worm velocity was measured by the “worm tracking” function of the Nikon analysis software. Experiments were performed at room temperature and repeated twice.Animals on food from vehicle or dopamine treatment plates were monitored. For each worm an average of three intervals between two defecation cycles was measured. The defecation cycle was identified as a peristaltic contraction beginning at the posterior body of the animal and propagating anteriorly followed by feces expulsion. Experiments were done at room temperature and repeated at least twice.Well-fed animals from vehicle or dopamine treatment plates were used. To measure the number of eggs inside the uterus, individual worms were lysed using a bleach solution and the number of eggs released was counted. To measure the egg-laying rate, individual worms were transferred to fresh plates and allowed to lay eggs for one hour. The next day the number of progeny, including crawling worms and dead eggs, on each plate was counted. Noteworthy, the number of dead eggs was negligible. Experiments were performed at room temperature and repeated at least twice.Standard Nile Red and BODIPY lipid staining were conducted as described previously with minor modifications Fixative-based stainings were performed as described previously For standard Nile Red and BODIPY, fluorescent images were acquired on a Zeiss Axioplan II inverted microscope, outfitted with a digital CCD camera, using identical settings, suitable fluorescence filters and the same exposure times. For LipidTOX and acute Nile Red, a Leica TCS SP5 confocal microscope fitted with an argon excitation laser was used. Images were acquired using identical settings including PMT's reading spectrum and gain. Images used for quantification had pixel intensities below saturation levels. The fluorescence in the first half of the distance between the beginning of the intestine and the vulva was quantified. Usually this region encompasses the first two to three segments of intestinal cells. For each condition or genotype worms were randomly selected under a dissection microscope and mounted for analysis. Each experiment was repeated at least twice.E. coli strain HT115 carrying each of the selected RNAi clones. Plates were induced overnight and used the next day. Dopamine and Nile Red were added on top of each plate and allowed to equilibrate overnight. Plasmid L4440 without inserts was used as a control. Synchronized L1 animals were transferred to plates containing RNAi/bacteria. They were fed on RNAi plates throughout their growth and treatment. The identity of each RNAi clone was blinded during experiments. Knockdowns that resulted in a suppression of the dopamine induced fat phenotype of more than 80% of the control animal's reduction were considered for further analyses. For the metabolic screen, data were acquired with Acumen  a microplate fluorescence citometer, and double checked in a visual-based assay. For missing values or values with SD bigger than 20% from the automatic assay, only the visual-based assay data were considered. Data from both assays had the same trend with the former being more sensitive for variations in intensity.RNAi feeding on agar plates was performed as described previously −1For the coherent anti-stokes Raman imaging 10,000 late L4-stage animals were collected. Total lipids were extracted at room temperature as described previously Image quantification and densitometry were performed using ImageJ software (NIH).Worms were exposed to dopamine from L3 to late L4 larval stages (24 hours) as described previously. The animals were collected and washed three times with S-Basal to remove bacteria. For each sample, a BSA-oleic acid solution (0.5 mM cold oleic acid/65 mCi/L hot oleic acid/0.55 mM NaOH/8.25 g/L BSA) was added sequentially. Samples were incubated for 60 minutes at room temperature in contact with hot oleic acid solution (BSA-oleic acid solution). This interval was sufficient to allow no dopamine and dopamine exposed worms to absorb and metabolize radiolabeled oleic acid. Subsequently, trichloroacetic acid (10% w/v) was used to stop the reaction. After spin down, supernatants were transferred to new tubes and 1× PBS and 5 M NaOH added in a proportion of 0.1/0.25, respectively. Samples were loaded onto Dowex resin  columns and eluents collected into scintillation vials. Scintillation fluid was added to each vial and radioactivity was measured. Protein content was used to normalize radioactivity. Experiment was repeated at least twice.Gene expression measurement through qPCR was performed as describes previously −Δ Ct, where −Δ Ct = −  and Ct =  cycle threshold calculated by the qPCR software (ABI 7500). 2−Δ Ct dopamine/2−Δ Ct no dopamine (control) ratio indicates fold changes in gene expression after exposure to dopamine. qPCR data were normalized by using a set of control genes previously reported Differences in genes expression were assessed through the comparative Ct method that uses the following equation: 2Student's t-test or one-way Analysis of Variance (ANOVA) followed by Bonferroni post-test was used to check for significant differences between means.C. elegans to exogenous dopamine reduced fluorescence signals  and aromatic amino acid decarboxylase (BAS-1) cat-2, bas-1 and dat-1 mutants  Dopamine induced fat reduction requires continues exposure to the neurotransmitter. (A) Diagram of the experiment with representative images taken for each condition. (B) Fluorescence quantification shows that transfer of dopamine exposed wild type animals to plates without dopamine brings fat stores up to non-treated animal levels. Data are expressed as percentage of 24 h vehicle-treated animals average ± SEM. . *** p<0.001 compared to vehicle-treated animals for each time point. (C) Fat reduction induced by dopamine and serotonin (5-HT). Quantification of fluorescence of animals exposed to dopamine and/or serotonin. Data are expressed as percentage of 24 h vehicle-treated animals average ± SEM. . *** p<0.001 compared to vehicle-treated animals, ### p<0.001 compared to dopamine-treated animals, ns means “not significant”. (D) Increasing dopamine's concentrations reduce Nile Red fluorescence similarly in wild type and mod-1 serotonin receptor mutant animals. Black and white bars represent wild type and mod-1 animals respectively. Data are expressed as percentage of vehicle-treated worms mean for each genotype ± SEM. . * p<0.05, ** p<0.01, *** p<0.001 compared to vehicle-treated animals of the same genotype.(TIF)Click here for additional data file.Figure S2Dopamine fat reducing effect is dose dependent. (A–B) Fat stores are reduced in dopamine-exposed animals as visualized by vital dye Nile Red. (A) Fluorescence quantification of animals exposed to a smaller concentration range of dopamine. Box and whiskers represent 25–75th percentile with mean and minimum to maximum measured values, respectively. Data are shown as percentage of “No dopamine” control. . (B) Dopamine inhibits accumulation of fat over time. (•) No dopamine, (Δ) Dopamine 5 mM. Data are shown as percentage of 0 hour “No dopamine” animals average ± SEM.  ** p<0.01 and *** p<0.001 compared to “No dopamine” control animals. (C) Pharyngeal pumping of wild type and dopamine receptor loss-of-function mutants is similar. Black and white bars represent vehicle and 5 mM dopamine treated animals, respectively. Data are presented as percentage of vehicle-treated wild-type average ± SEM. .(TIF)Click here for additional data file.Figure S3A new protocol for use of vital dye Nile Red highlights different tissues lipid droplets. (A) LipidTOX is a triglyceride fixative-based dye. The figure shows worm's diverse lipid storage tissues. White arrow highlights yolk stained in gonads. These are representative images from three different experiments. DIC means differential interference contrast microscopy. (B) Acute instead of chronic exposure to Nile Red reveals lipid droplets outside intestinal cells. The right top panel shows Nile Red staining of gonad (white arrow), intestine and hypodermis. To confirm that stained structures are mainly lipid droplets, a transgenic strain (VS20) carrying adipose triglyceride lipase-1 tagged with green fluorescent protein (ATGL-1::GFP) was used. The bottom panel shows Nile Red staining of VS20. ATGL-1::GFP colocalizes with Nile Red. Representative images of different animals from the same experiment.(TIF)Click here for additional data file.Figure S4Worm's developmental stage affects lipid profile as assessed by thin-layer chromatography (TLC) from whole animal homogenates. (A) Representative images of TLC plates loaded with extracts from different developmental stages animals. Seq1 to Seq4 are distinct chromatograms. Some extracts were loaded on more than one plate with similar results. (B) Quantification of lipid fractions from each population. Graph on the left shows absolute quantification. Graph on the right shows corresponding percentage of each lipid fraction in total lipid extract for each group. Bar colors in (B) correspond to the bands in (A). Data are presented as a mean of three biological replicates extracted from 10000 worms each ± SD. L2 – second larval molt. L2/L3 – L2 to L3 molt. L4 – fourth larval molt. L4/YA – L4 to young adults (YA) molt. GA – Gravid adults.(TIF)Click here for additional data file.Figure S5Dopamine does not change expression of serotonin regulated metabolic genes. (A–B) Quantitative real-time polymerase chain reaction measurement of transcriptional levels of specified metabolic genes after exposure to different dopamine concentrations. (A) Graphs show mean transcriptional levels of three independent populations ± SD. Data points were calculated using the follow equation: 2 −Δ Ct, where −Δ Ct = − and Ct =  cycle threshold calculated by the qPCR software (ABI 7500). Black and white bars represent vehicle and dopamine treated animals, respectively. (B) 2−Δ Ctd/2−Δ Ctc ratio indicates fold changes in gene expression after exposure to dopamine. Ctd =  cycle threshold of dopamine exposed animals and Ctc =  cycle threshold of “No dopamine” control animals.(TIF)Click here for additional data file.Figure S6Sudan Black staining of dopamine receptor loss-of-function mutants. (A–C) dop-1 and dop-3 mutants have increased and decreased fat stores, respectively. (A) dop-1 and (B) dop-3 representative images of fixed-animals stained with Sudan Black. Images of wild type animals stained in the same tube are shown. (C) quantitation of Sudan Black stain intensities. Data are expressed as percentage of wild type animals average ± SD. . * p<0.05 compared to wild type animals.(TIF)Click here for additional data file.Figure S7Compilation of the dopamine metabolic screen. Nile Red fluorescence quantifications of worms fed on a series of RNAi clones are shown. Black and white bars represent vehicle and 5 mM dopamine-treated animals, respectively. Clones that suppressed dopamine fat reduction phenotype more than 80% of the “No dopamine” control's reduction are highlighted in red and were picked for further analyses. Data are presented as fluorescence arbitrary units (a.u.) ± SEM .(TIF)Click here for additional data file.Table S1List of primers and cycle thresholds (Ct) used for gene transcriptional level measurements.(XLSX)Click here for additional data file.


The major problem during the detection is the presence of a high concentration of ascorbic acid that is oxidized at nearly the same potential as dopamine on bare electrodes. Furthermore, the oxidation product of dopamine reacts with ascorbic acid present in samples and regenerates dopamine again, which severely limits the accuracy of the detection. Meanwhile, the product could also form a melanin-like insulating film on the electrode surface, which decreases the sensitivity of the electrode. Various surface modifications on the electrode, new materials for making the electrodes, and new electrochemical techniques have been exploited to solve these problems. Recently we developed a new electrochemical detection method that did not rely on direct oxidation of dopamine on electrodes, which may naturally solve these problems. This approach takes advantage of the high performance of our newly developed poly(anilineboronic acid)/carbon nanotube composite and the excellent permselectivity of the ion-exchange polymer Nafion. The high affinity binding of dopamine to the boronic acid groups of the polymer affects the electrochemical properties of the polyaniline backbone, which act as the basis for the transduction mechanism of this non-oxidative dopamine sensor. The unique reduction capability and high conductivity of single-stranded DNA functionalized single-walled carbon nanotubes greatly improved the electrochemical activity of the polymer in a physiologically-relevant buffer, and the large surface area of the carbon nanotubes increased the density of the boronic acid receptors. The high sensitivity and selectivity of the sensor show excellent promise toward molecular diagnosis of Parkinson's disease. In this review, we will focus on the discussion of this novel detection approach, the new interferences in this detection approach, and how to eliminate these interferences toward in vivo and in vitro detection of the neurotransmitter dopamine.Most of the current techniques for  They were allowed to react for 5 minutes at room temperature before performance of the fluorescence experiments. The solutions were excited at 468 nm and the fluorescence intensities were monitored at the emission wavelength of 588-590 nm. Equations use to calculate the binding constants are shown below :(1)Q=[Aexed ARS , and P i-1 (mean ± SEM). Wang et al. determined the binding constant between PBA and catechol to be 830 M-1. As catechol is very structurally similar to dopamine , the small difference in their Ka values is understandable. However, it is necessary to mention the possibility that the existence of free amine group in dopamine may quench the fluorescence of the ARS-PBA complex due to the lone electron pair on the amine nitrogen [nitrogen . This wonitrogen .para position. We utilized the same fluorescence binding assay to study how the amine group interacts with the PBA-ARS complex by monitoring the fluorescence signal upon addition of different concentrations of tyramine into the PBA-ARS complex solution. We found that addition of tyramine barely changed the fluorescence signal of the PBA-ARS complex, suggesting that possible quenching of the ARS-PBA complex by the free amine groups of dopamine, resulting in an overestimate of the calculated PBA-DA binding constant, is negligible.To elucidate the influence of the amine group in dopamine on the fluorescence signal during the binding constant measurement, a control experiment was performed with tyramine. Tyramine is also a neurotransmitter and it has a very similar molecular structure as dopamine, but without a diol group to bind boronic acid: rather, it possesses a single alcohol group and an ethylamine group in the -1 (mean ± SEM), which is approximately 40-fold lower than the DA association constant (890 ± 42 M-1). Considering that the concentration of AA is three or four orders of magnitude higher than the concentration of DA in physiological samples, large amounts of AA can therefore bind to the boronic acid groups along the polyaniline backbone under physiological conditions. Therefore, the interference by AA toward the detection of dopamine is a two-pronged problem in this non-oxidative approach. On one hand, the electrocatalytic reductive ability of AA caused a large increase of the oxidation current of the polyaniline backbone, and on the other hand AA chemically bonded to the boronic acid groups, which induced a decrease of the oxidation current and a positive shift of the oxidation potential. The net effects of these two divergent factors determine the degree of AA interference on the detection of DA. The chemical and electrochemical interactions between PABA and AA are summarized in The aforementioned fluorescence-based binding assay was also employed to calculate the binding affinity of ascorbic acid to PBA, the value of whose binding constant is 21 ± 1.8 MIn vivo AA is protected by chemical interactions with physiological proteins but in vitro AA is susceptible to oxidation, which is not surprising considering that the foremost chemical role of Vitamin C is as a reducing agent. It is reported that measurable oxidation of AA occurs within hours [Although we still do not understand why the current approach is contradictory to the previous reports about AA interference, we speculate that one of the most important reasons is the extremely high sensitivity provided in the current sensing approach, which “detected” the previously undetectable AA, leading to the observed interference. Finally it is important to mention that a freshly prepared ascorbic acid solution is required to study the interference effect of AA. We noticed that the AA solutions that were used approximately one day after preparation did not demonstrate interference. We understand that this is because AA is not stable in solution . In vivoin hours . The oxiin hours , which iin hours .2.5.a 8.9) and reject the negatively charged ascorbate (pKa 4.2) at the electrode surfaces in physiological pH. The widely used perfluorosulfonated polymer Nafion, a cation-exchange polymer, repels ascorbate and other anions and can provide a transport channel solely for cations. Due to their biocompatibility, Nafion films have been extensively employed for the modification of electrode surfaces and for the construction of amperometric biosensors [in vivo detection of DA, we deposited a thin layer of Nafion on top of the PABA/SWNT composite to diminish the ascorbate interference.Several methods have been reported to eliminate the interference of AA towards the determination of dopamine, such as use of the enzyme ascorbate oxidase (AOx). Selective oxidation of AA by AOx has been widely used in the development of selective biosensors, including those for glucose, lactose, and dopamine -107. Theosensors , 109. ToDeposition of a layer of Nafion does not alter the redox activity of the ss-DNA/SWNT/PABA film in neutral pH solutions. The CV curves are very similar to the curves shown in The correlation coefficient for the reproducible detection of dopamine is 0.9924, and the detection limit (1.5 nM) is slightly higher than that without Nafion (0.6 nM). This is perhaps due to Nafion behaving as a diffusive barrier, i.e. the Nafion film decreased the amount and/or the speed of dopamine diffusing to the ss-DNA/SWNT/PABA film. With 0.15 mM ascorbic acid in the dopamine solutions, the data points in the calibration curve of dopamine reside in the positive error region of the calibration curve for dopamine without ascorbate present. These results demonstrate the near-complete elimination of interference by ascorbic acid. The strong cation exchange of Nafion leads to an uptake of the positively changed dopamine and the anionic ascorbate interferent is electrostatically rejected from the surface because of the negatively charged Nafion. Since the linear correlation is between 1 to 10 nM dopamine, which is an appropriate concentration range for Parkinson's disease patients , 14, thi3.in-situ polymerized poly(anilineboronic acid)/carbon nanotube composite and a thin layer of the highly permselective Nafion film. Since direct oxidation of dopamine is avoided in this approach, the associated problems with direct oxidation, such as electrode fouling and dopamine regeneration, were prevented. Furthermore, the DNA-wrapped single-walled carbon nanotubes in the composite not only greatly improved the electrochemical activity of the composite in physiologically relevant solutions, they also increased the effective electrode surface area, and therefore the density of boronic acid groups available for binding dopamine. These features significantly enhanced the sensitivity for dopamine detection. Dopamine concentrations as low as 1 nM were detected with cyclic voltammetry, and the electrochemical current change was linear in the range of 1–10 nM. Optimization of the detection technique resulted in a detection limit of 16 pM, which is six magnitudes lower than that of the sensor in which PABA alone was used for the detection. In this work, we also found that ascorbic acid interfered with the detection if Nafion was not deposited on the electrode, which is contrary to previous reports of nonoxidative PABA-based sensors. We studied the mechanism of interference by ascorbic acid and the results show that the interference mechanism is very different from the approaches relying on direct oxidation of dopamine at the electrode. The ascorbic acid was able to electrocatalytically reduce the fully oxidized polyaniline backbone during the electrochemical oxidation process. The ascorbic acid was also able to bind to the boronic acid groups through its planar diol as dopamine does, although its binding affinity is lower. Coating a thin layer of Nafion on top of the composite eliminated the interference from ascorbic acid. The strong cation exchange of Nafion leads to an uptake of positively charged DA and the negative charges on the Nafion film electrostatically rejected the ingress of the negatively charged ascorbate to the PABA/carbon nanotube composite. The sensitivity of the sensor along with its improved selectivity might allow for its potential use in the diagnosis of Parkinson's disease. A clear understanding of the dopamine transduction mechanism and AA interference mechanism in this non-oxidative approach is essential to eliminate other interferences toward in vivo and in vitro detection of dopamine, which is the long term goal of our continuing efforts.In this review we have summarized our efforts in electrochemical detection of dopamine with high sensitivity and selectivity by modifying the electrode surface with a thin layer of


Room temperature PL measurements of the annealed nanofibers present a wide feature band at ~1.4–1.6 eV. The room temperature PL properties of the irradiated samples presents a large blue shift compared to bulk GaSb. Annealed nanofibers and annealed nanofibers with Au nanodots present two different PL peaks (400 and 540 nm), both of which may originate from Ga or O vacancies in GaO. The enhanced PL and new band characteristics in nanostructured GaSb suggest that the nanostructured fibers may have unique applications in optoelectronic devices.Amorphous GaSb nanofibers were obtained by ion beam irradiation of bulk GaSb single-crystal wafers, resulting in fibers with diameters of ~20 nm. The Raman spectra and photoluminescence (PL) of the ion irradiation-induced nanofibers before and after annealing were studied. Results show that the Raman intensity of the GaSb LO phonon mode decreased after ion beam irradiation as a result of the formation of the amorphous nanofibers. A new mode is observed at ~155 cm III–V semiconductors are increasingly important for electronic and optoelectronic devices due to their high electron mobility and direct bandgap. And nanostructured semiconductors have been attracting widespread attention for their unique quantum-confined nanoscale properties. In particular, the optical properties of nanoscale semiconductors are seen as a key to the future of optoelectronic device fabrication ,2. One m+ ion beams (FIB) [+ and Kr+ ion beams [+ and high-energy Au+ and Kr+ ion beam irradiation. And thermal annealing was conducted to crystallize the GaSb nanofibers. We analyze the optical properties of the GaSb nanofiber semiconductors by means of Raman scattering and photoluminescence (PL). It shows that the substrate signal of the GaSb LO mode appears at 237 cm-1, and the nanostructured GaSb samples show peaks around ~155 cm-1, which can be assigned to the A1g peak of crystalline Sb. The visible room temperature PL spectrum of the annealed nanofibers demonstrates an increase in luminescent intensity, and the low temperature (15 K) PL spectrum presents two new PL peaks (400 and 540 nm) when compared to bulk GaSb.Some studies using ion accelerators , low-enems (FIB) -12 and hon beams  have shoon beams ,15. In t+ ion beam at room temperature. The evolution of the surface morphology of GaSb was monitored in situ in an FEI Nova 200 Nanolab FIB/SEM dual beam system. Conventional broad ion beam irradiation of GaSb using 150 keV Kr+ ions (with a beam diameter of ~25 mm) and 1 MeV Au+ ions was also conducted.GaSb single-crystal wafers with (100) orientation were irradiated with a 30 keV focused GaFor the annealing study, parts of the irradiated samples were annealed at 250 and 350°C for 10 min in a conventional open tube furnace. Irradiated samples, as well as irradiated samples coated with Au thin film, were annealed at 600°C for 10 min.-1. Room temperature PL characteristics of the GaSb nanofibers and bulk GaSb were also obtained using the 633 nm He–Ne laser. Low temperature PL characteristics of the bulk GaSb, annealed GaSb nano fibers and GaSb nano fibers coated with Au were conducted using a 325-nm He–Cd laser with output power 50 mW.Raman scattering experiments were demonstrated in backscattering geometry from the (100) sample surface at room temperature using a 633 nm He–Ne laser as an excitation source coupled to the commercial Raman spectrometry system. For III–V compound semiconductors of the zinc-blende crystal structure, Raman spectra generally show two peaks. The lower-frequency peak corresponds to TO phonons, and the higher frequency peak corresponds to LO phonons. In backscattering, only LO phonons appear in the (100) direction . The las+ ion beam bombardment with incident angles of 0° and 70°, respectively. Figure + irradiation at 150 keV. It was found that the surface quickly evolved into a high density network of uniformly spaced GaSb nanofibers. Parts of the nanofibers were connected together to form a flake-like structure. The diameters of the relatively uniform nanofibers measured by the SEM is ~20 nm  oriented material, and the TO mode is forbidden [Figure orbidden . Howeverorbidden .+ ion beam. Raman spectroscopy was performed on both unannealed and annealed samples, at temperatures of 350 and 250°C for 10 min. From the spectra, we can see that the intensity of the LO modes becomes weaker after Ga+ irradiation. Figure -1 and its intensity almost approaches zero. This means that the GaSb nanofibers become amorphous by Ga+ ion irradiation. Figure -1 and the full widths at half maximum (FWHM) were broadened in comparison with bulk GaSb spectrum. The stronger intensity of the LO modes means the amorphous nanofibers became crystalline through the annealing process. Such behavior of the Raman peak red shift and broadening can be explained by the phonon confinement effect [+ ions, both unannealed and annealed at the temperature of 250°C. From the spectra, we cannot find the mode of GaSb from the two curves. These alterations of the LO mode by ion implantation on the crystalline structure are also attributed to the disordering of the crystalline structure. Because the decay of translational symmetry relaxes the momentum conservation, all photons in the Brillouin zone participate in ordered Raman scattering. These will generally induce the shift of the LO mode to a lower energy and cause asymmetric broadening [The curves in Figure t effect . Figure oadening .-1 for the samples after Ga+ and Kr+ ion beam bombardment as shown in Figure + ions and found that Sb atoms are deficient in the surface region of the irradiated areas. This phenomenon may be caused by the selective sputtering of Sb atoms during the ion bombardment process. However, recent work has shown that the thermal annealing of GaSb nanofibers results in a complete chemical decomposition of the nanofibers into crystalline Sb cores surrounded by amorphous GaOx shells [-1[1g (150 cm-1) phonon of Sb. Carles et al. [1g mode due to Sb–Sb bond vibration. Therefore, we can conclude that the Raman peak is related to Sb–Sb bond vibrations rather than other modes.At the same time, a new strong peak is observed at around 155 cmx shells , which ihells [-1, which is et al.  have als+ bombarded samples annealed at 250 and 350°C for 10 min and Kr+ bombarded samples annealed at 250°C, respectively. For the as-irradiated sample, as shown in Figure -1, as shown in Figure + ion irradiation were more obvious on the GaSb surface, so the anomalous annealing behavior may be attributable to the thicker fiber layer forming underneath the material surface.In order to study the characteristics of the amorphous and crystalline nanofibers, we compare the Raman spectra of Ga-1 as shown in Figure On the other hand, the FWHM of the 250°C annealed sample is wider than that of the 350°C annealed sample, with a peak at 155 cm+ and 150 keV Kr+ ion bombarded samples. Compared with bulk GaSb with a direct bandgap of ~0.72 eV at room temperature, the PL spectrum of the ion bombarded samples shows a blue shift in the bandgap. It seems likely that the PL mechanism of the GaSb nanofibers is similar to that of porous silicon [0 from pure GaSb and a scattered field Es from the nanoscale, inhomogeneous GaSb fiber networks. As a result, there is an extra energy that results in the blue shift observed in our PL measurement. Figure 2O3 3D microstructures [Figure  silicon . Specifi silicon , while nructures . The nanructures .+ ion, broad Kr+ ion and broad Au+ ion beam irradiation were used to fabricate nanofibers on the surface of bulk GaSb. Raman scattering shows that the LO phonon mode of GaSb decreases after ion beam irradiation. A new mode is observed around ~155 cm-1 both from unannealed and annealed nanofiber samples. The mode is related to the A1g mode of Sb–Sb bond vibration. Room temperature PL characteristics present an enhancement from the annealed GaSb nanofiber samples compared with the bulk. Quantum confinement effects are discussed in regard to the blue shift of the bandgap. Low temperature (15 K) PL characteristics of the annealed nanofibers show a blue emission peaking at 420 nm and green emission peaking at 550 nm, which may be attributed to atomic defects in the nanostructures, such as oxygen vacancies, gallium vacancies and gallium–oxygen vacancy pairs. Higher PL intensities were obtained from the annealed GaSb fibers coated with an Au thin film, which may be due to surface plasmon effects. The enhanced PL and new band characteristics in the annealed GaSb nanostructures suggest that the irradiation-induced nanofibers may well have vast applications in optoelectronic devices for their unique optical properties.In summary, focused Ga


Guar Gum is a natural polysaccharide that, due to its physicochemical properties, is extensively investigated for biomedical applications as a matrix for modified drug delivery, but it is also used in the food industry as well as in cosmetics. A commercial sample of Guar Gum was sonicated for different periods of time, and the reduction in the average molecular weight was monitored by means of viscometric measurements. At the same time, the rheological behaviour was also followed, in terms of viscoelasticity range, flow curves, and mechanical spectra. Sonicated samples were used for the preparation of gels in the presence of borate ions. The effect of borax on the new samples was investigated by recording mechanical spectra, flow curves, and visible absorption spectra of complexes with Congo Red. The anisotropic elongation, observed in previous studies with tablets of Guar Gum and borax, was remarkably reduced when the sonicated samples were used for the preparation of the gels. For flexible polymer coils, a is 0.5 in a θ solvent and 0.8 in a good solvent  = K solvent .μM CR, in order to obtain the desired pH values . The capability of sonicated GG to retain the helix conformation when linked with borax  was examμM CR solution. The absorption spectra were recorded with a Cary 100 spectrophotometer  using a quartz cuvette with a path length of 1 cm. The solutions were left to equilibrate until their UV-Vis spectra did not change with time. For a comparison, similar experiments were carried out on a 10 The swelling of GG/borax tablets, prepared with the different types of GG , was carried out by soaking the tablets in distilled water at 37°C. At fixed time intervals, the tablets were withdrawn, the excess of water was removed with soft filter paper for 5 s, and then the corresponding weights and dimensional variations along the longitudinal axis were determined by means of a screw gauge with an accuracy of ±0.1 mm. No remarkable variations of cross-section dimensions were detected during the swelling process. All experiments were carried out in triplicate, and the obtained values always lay within 10% of the mean.Mw only. In Mw/Mn). It should be kept in mind that the sonicated samples are also centrifuged , and this procedure removes not only the tip dust but also the possible aggregates present in the samples. On the other hand, in the dialysed sample (GG), only the polymeric fractions of very small molecular weights  and impurities, if present, are removed. Thus, the dialysed samples lack the smallest molecular weight fractions while the sonicated ones lack also molecular aggregates, if present. The high polydispersity index detected for GG 0 sample may be due to the effect of centrifugation that breaks the aggregates leading to the dissolution of lower molecular weight fractions (though higher than 14.000); such effect obviously does not occur in the sample that was only dialysed. From the table of Mw/Mn = 2).Molecular weight distributions of GG and sonicated GG samples by GPC are shown in dγ/dt = 10 (s−1)) which drops of one decade, from 0.3 (for GG and GG 1) to 0.02 (Pa s). Furthermore, the interval of the Newtonian plateau is remarkably shifted to a higher shear rates indicating that, by reducing the chain length, the viscosity continues to decrease while, for the native polymer or the polymer sonicated for 1 min, the viscosity values remain almost constant up to dγ/dt = 100 (s−1)). Thus, ten minutes of sonication are sufficient to break significantly the GG chain backbones  mannose protons at 4.73 ppm and the anomeric α (1→6) galactose protons at 5.02 ppm remains constant and equal to 1.56 for the tested samples. This is a clear indication that the M/G ratio is not affected by the sonication process; that is, the repeating units of the polymer keep intact their chemical compositions.NMR was carried out in DThus, the sonication process can be proposed as a suitable method for GG molecular weight reduction. Actually, by means of sonication, it is possible to prepare GG within a quite wide range of molecular weights without damaging the molecular structure. This is a crucial point, especially with reference to possible industrial applications of GG, because the rheological properties of the polymeric solutions prepared with the sonicated samples that show remarkable variations in comparison with the native polymer, can be appropriately tailored according to specific needs.dγ/dt = 0.1 (s−1). After this value, GG and GG 0 undergo a rapid decrease in viscosity followed by the impossibility to further proceed with the measurements due to the brittle nature of the network. The presence of borax leads to a better discrimination among the different samples even after 1 min of sonication. Going from 1 to 10 min of sonication, the viscosity is strongly reduced and the plateau is extended up to dγ/dt ≈ 2 (s−1). Thus, the reduction in molecular weight induces a dramatic change in the polymeric network. When sonication is carried out for 30 min, the sample with borax starts to show a pseudoplastic behaviour. However, in no case the viscosities of the GG/b samples reach values below those recorded for the samples without borax. It is clear that both GG molecular weight and borax represent key parameters for the flow properties.The effect on the rheological properties of the addition of borax to the various samples was also investigated . AdditioG′ (storage modulus) and G′′ (loss modulus), as a function of the deformation are shown. As expected, the linear viscoelasticity interval is reduced by lowering the sample molecular weight. The addition of borax increases the G′ values from about 1 to approximately 100 Pa and the stress/strain response is linear up to a 100% of deformation. The mechanical spectra were then recorded at γ = 0.01. In In G′′ > G′), and a strong dependence of the two moduli on frequency can be detected. In the case of samples sonicated for a longer period of time, it was not possible to carry out the experiment, due to their predominant liquid-like characteristics. G′′) dominates the solution response over most of the frequency domain, indicating the mainly viscous nature of the GG solution. Borax crosslinking causes an increase in both moduli, with the elastic modulus (G′) being more strongly affected. A well-defined plateau is observed in the elastic modulus at intermediate frequencies (0.05–10 Hz), being G′ higher than G′′ within this interval. These features are characteristic of a network structure formed by the GG-borax crosslinks. Due to the labile nature of these linkages, the GG/b gel displays a terminal region similar to that of polymer solutions with a well-defined crossover between G′ and G′′. The frequency values at which the moduli inversion takes place increase as sonication time increases. Obviously, by reducing the GG molecular weight, the strength of the gel, formed in the presence of borax, correspondingly decreases. This is clearly evidenced by the reduction of the storage modulus values of the frequency interval where G′ is independent of the applied frequencies and of the shift of the crossover towards higher frequency values. All these variations indicate the increasing weakness of the polymeric network as sonication proceeds with a widening of the mesh size  and a corresponding shortening of the longest relaxation time .In G′ and G′′ is appreciably shifted to higher frequencies in comparison to GG/b sample (from 0.002 Hz (GG/b) to 0.012 Hz (GG/b 5). In the case of sonicated samples, the sol-gel transition occurs at higher frequencies due to the reduced influence of the longest relaxation modes, as expected for a looser network. Furthermore, the maximum of G′′ does not match with the crossing point: this discrepancy is reduced by increasing the sonication time. This means that, by reducing the chain length, the data can be fitted by using the generalized Maxwell model applying only a few or only one (in the case of coincidence between the G′′ maximum and the crossing point) relaxation time. This is even more evident when GG undergoes a 10 min sonication. The mechanical spectra are still those of a gel system, but both moduli are lowered by a power of ten, and the crossover is further shifted to higher frequencies (≈0.3 Hz). Of course, in this last case a much weaker gel is obtained, being the G′ plateau significantly reduced in comparison to that of the previous two systems. In Mw = 1.32 × 105) for the formation of a real gel network, and consequently the modulus values become too low to be recorded. Anyhow, in the case of GG, one min is the maximum sonication time that can be applied in order to record the mechanical spectra . On the other side, the presence of borax, leading to the formation of a three-dimensional network, allowed to acquire mechanical spectra of a GG sample sonicated up to 10 min. When the sonication time is further increased to 30 min the linkages with borax are too few, due to the reduction of GG molecular weight  and assumes high values when intermolecular associations exist [kH of GG in water is much higher than the regular value for a good solvent (0.3–0.4), which indicates the importance of polymer-polymer hydrogen bonding interactions among the GG molecules. When sonication is applied, the decrease in molecular weight reduces the number per chain of hydrogen bonding sites leading, at the same time, to a reduction of the intermolecular hydrogen bonding between polymer molecules, that is, the attractions between guar chains [kH is also very sensitive to the formation of molecular aggregates. In other words, the Huggins coefficient can provide additional information about the state of guar gum macromolecules in solution and can be a measure of the goodness of the solvent [It is possible to observe that, by applying the sonication for only 5 min, the GG molecular weight was reduced to more than one half. The reduction was further increased by increasing the sonication process for longer times. From the slope of the linear trend of viscosity data, the values of the Huggins constant, kH, see  were calns exist . Furtherr chains . kH is  solvent .In previous works , it was The tablets prepared with GG and GG 0 increased their weight and elongated for 24 hrs; those prepared with GG sonicated for 1 min follow the same trend while those prepared with GG 10 started to dissolve in the medium already after 8 hours. This phenomenon started even earlier for the sample sonicated for 30 min: the tablets started to loose their weight already after 5 hours, and the anomalous elongation could be detected only up to 5 hours. After 8 hours, the tablets dissolved completely in the swelling medium. Both processes, the uptake of water and the height increase, obey, during the first hours, the Fickian law Figures . Thus, tIn The conformational structures of GG-borax and sonicated GG/borax were investigated by means of the formation of CR-polymer complexes at various alkali concentrations. CR, indeed, tends to bind to polysaccharides in helical structures, and as a consequence, its visible absorption maximum shifts to higher values. By following its shift in the Vis spectra of the polymer-dye complex, it is possible to demonstrate the presence of helix conformation. As reported in Mw of a polysaccharide such as GG without the use of chemicals  thus allowing more easily acceptable applications in the field of pharmaceutics, cosmetics, and food industry. As evidenced in the NMR spectra, sonication does not destroy the structural characteristics of the polymeric chains; furthermore, obtained results showed that depolymerization of GG by sonic irradiation leads to different fractions of lower Mw, each fraction having a narrow molecular weight distribution. As expected, such Mw reduction dramatically affects the rheological properties of the polysaccharide, leading to significantly modified flow curves. In this sense, it must be pointed out that it was possible to acquire the mechanical spectra only for the sample sonicated for 1 min because the moduli of the other samples, obtained after sonication was carried out for a longer period of time, were too low to be detected. As in the case of other polysaccharides (3), the addition of borax changed completely the rheological properties from liquid-like systems to gel-like systems, and this behaviour, although to a less extent, was observed for all sonicated samples. Only when the molecular weight was decreased to Mw = 1.32 × 105, the GG chains were no more able to build up a three-dimensional gelled network, and the rheological moduli were too low to be experimentally detected. Actually, the GG molecular weight, as estimated by viscometric measurements, decreased very rapidly by applying ultrasounds to the polymeric solutions, leading to a reduction to one-half of the initial Mw already after a sonication of 3 minutes.Sonication appears to be a rather easy method suitable to reduce the The swelling behaviour of GG/borax tablets was also investigated. In this respect, the reduced molecular weight of the polymer led to a significant difference in water uptake and anisotropic elongation in comparison to the starting GG sample.Mw, represents an important approach for an appropriate tailoring of the mechanical properties of GG solutions in consideration of a more suitable and rational utilization of such polysaccharide in the various and wide fields of practical uses. In particular, as far as innovative drug formulations are concerned, an optimized reduction of the GG Mw will allow an easy conjugation of the obtained shorter chains with a hydrophobic drug that should then lead to the formation of self-assembled structures, in the form of nanohydrogels, that are suitable for modified drug delivery systems, as already successfully carried out and reported in the case of sonicated gellan gum chemically linked to prednisolone [In conclusion, the use of a simple method, such as sonication, for the reduction of GG nisolone .


Vibrio vulnificus is a virulent, gram-negative, comma-shaped, motile bacterium that belongs to the family Vibrionaceae. In 1976, researchers at the Centers for Disease Control identified it as a Vibrio sp. and possible emerging pathogen. Because of its association with blistering skin infections, the bacterium was named Vibrio vulnificus in 1979. From the Latin PubMed;Hollis DG, Weaver RE, Baker CN, Thornsberry C. Halophilic Vibrio species isolated from blood cultures. J Clin Microbiol. 1976;3:425–31. PubMed;Todar K. Todar’s online textbook of bacteriology. Vibrio vulnificus. [cited 2010 Nov 24]. http://textbookofbacteriology.net/v.vulnificus.html; Dorland’s illustrated medical dictionary. 31st ed. Philadelphia: Saunders Elsevier; 2007.Sources: Farmer JJ III. Vibrio (“Beneckea”) vulnificus, the bacterium associated with sepsis, septicaemia and the sea. Lancet. 1979;2:903.


Amphetamine, a highly addictive drug with therapeutic efficacy, exerts paradoxical effects on the fundamental communication modes employed by dopamine neurons in modulating behavior. While amphetamine elevates tonic dopamine signaling by depleting vesicular stores and driving non-exocytotic release through reverse transport, this psychostimulant also activates phasic dopamine signaling by up-regulating vesicular dopamine release. We hypothesized that these seemingly incongruent effects arise from amphetamine depleting the reserve pool and enhancing the readily releasable pool. This novel hypothesis was tested using in vivo voltammetry and stimulus trains of varying duration to access different vesicular stores. We show that amphetamine actions are stimulus dependent in the dorsal striatum. Specifically, amphetamine up-regulated vesicular dopamine release elicited by a short-duration train, which interrogates the readily releasable pool, but depleted release elicited by a long-duration train, which interrogates the reserve pool. These opposing actions of vesicular dopamine release were associated with concurrent increases in tonic and phasic dopamine responses. A link between vesicular depletion and tonic signaling was supported by results obtained for amphetamine in the ventral striatum and cocaine in both striatal sub-regions, which demonstrated augmented vesicular release and phasic signals only. We submit that amphetamine differentially targeting dopamine stores reconciles the paradoxical activation of tonic and phasic dopamine signaling. Overall, these results further highlight the unique and region-distinct cellular mechanisms of amphetamine and may have important implications for its addictive and therapeutic properties. Amphetamine (AMPH) is both addictive, with several notable episodes of widespread abuse worldwide, and therapeutic, for treating narcolepsy, attention deficit hyperactivity disorder, obesity, and traumatic brain injury in vivo are stimulus-dependent, with increases revealed by short trains and decreases by long trains Presynaptic neurotransmitter vesicles are functionally and anatomically segregated into at least three distinct pools, readily releasable, recycling, and reserve, that are interrogated by electrical stimulation of short, intermediate, and long duration, respectively in vivo voltammetry and vary stimulus duration to test the novel hypothesis that AMPH elicits opposing actions on dopamine stores. In support of this hypothesis, we show in the dorsal striatum that AMPH increased exocytotic dopamine release evoked by a short train, which interrogates the readily releasable pool, but decreased release evoked by a long train, which interrogates the reserve pool. A concurrent augmentation of tonic and phasic dopamine signaling was also observed. Vesicular depletion and enhanced tonic signaling appear to be linked because these effects were specific to AMPH and not cocaine, and to the dorsal but not ventral striatum, whereas activation of vesicular release and phasic signaling generalized across psychostimulants and striatal sub-regions. Our results thus support a model of AMPH differentially targeting vesicular stores to reconcile its paradoxical effects on dopamine neurons and identify regionally distinct actions of this psychostimulant in the striatum that may relate to its addictive and therapeutic properties.Here we use The experimental design is shown in ad libitum. Protocols were approved by the Institutional Animal Care and Use Committee of Illinois State University. Care was in accordance with NIH guidelines (publication 86–23).Adult male Sprague-Dawley rats (∼350–400 g), purchased from Harlan , were housed under standard conditions of lighting and temperature. Food and water were provided Rats were anesthetized with urethane  and immobilized in a stereotaxic frame . Deltaphase Isothermal Pads  maintained core temperature throughout surgery. Burr holes were drilled overlying targeted regions, dura was removed, and electrodes lowered along a vertical trajectory using stereotactic coordinates obtained from a brain atlas based on a flat-skull position FSCV was performed by a Universal Electrochemistry Instrument , which was computer controlled by commercially available software . The potential of the CFM was linearly scanned at 10 Hz from a resting value of −0.4 V to 1.3 V (versus the reference electrode) and back again at a rate of 400 V/s. The peak oxidation current for dopamine recorded during each scan was converted to a concentration based on post-calibration of the CFM using flow-injection analysis in a buffer consisting of 150 mM sodium chloride with 15 mM TRIS and adjusted to a pH of 7.4 Electrical stimulation was computer generated and consisted of biphasic pulses (2 ms each phase). Stimulus trains were applied to a twisted bipolar stimulating electrode  through a constant-current generator and optical isolator .max) and parameters described vesicular dopamine release and dopamine uptake according to p is the concentration of dopamine released per stimulus pulse, f is the frequency of stimulation, and k is the first-order term describing dopamine uptake. Data were best fit to m and Vmax, which is difficult to resolve with in vivo voltammetry p, the focus of the present study, have been reported using both kinetic models p).Dopamine responses electrically evoked by short and medium stimulations were analyzed for maximal concentration  using dopamine, pH and background drift as analytes max and [DA]p were statistically analyzed using a two-way ANOVA with drug treatment and stimulus duration as independent variables, followed by sequential Bonferroni post hoc tests. Effects of drug treatment on k were analyzed using a one-way ANOVA with a Tukey's post hoc test. Tonic dopamine levels were statistically analyzed using a one-way ANOVA with repeated measures. Statistical analysis was performed using SPSS Version 18 for Windows (SPSS). Significance was set at p<0.05.When appropriate, data are presented as the mean ± SEM. [DA]d-amphetamine and cocaine doses were determined by base weight.Urethane, cocaine hydrochloride, and d-amphetamine sulfate were purchased from Sigma . All drugs were dissolved in 150 mM NaCl prior to injection. max, the maximal concentration of the evoked signal, and obtained from these recordings are shown in max evoked by the long train, and this only occurred in the dorsal striatum. Third, AMPH was more proficient in increasing [DA]max evoked by the short train in the ventral striatum, whereas cocaine elicited greater effects in the dorsal striatum. And fourth, the high dose of AMPH was more proficient at increasing [DA]max during short trains in both striatal regions compared to the low dose. Statistical analysis of [DA]max revealed a significant effect of drug treatment in the dorsal  and ventral  striatum, a significant effect of stimulus duration in the dorsal  and ventral  striatum, and a significant interaction in the dorsal  and ventral  striatum. In the dorsal striatum, 10 mg/kg AMPH and 40 mg/kg cocaine significantly (p<0.002) increased [DA]max evoked by the short train, but only cocaine was effective at the intermediate train (p<0.001). Both doses of AMPH (1 and 10 mg/kg) significantly (p<0.001) decreased [DA]max evoked by the long train, whereas cocaine was without effect. In the ventral striatum, both doses of AMPH and cocaine significantly (p<0.01) increased [DA]max evoked by short and intermediate trains, but were without effect with the long train.Individual recordings of electrically evoked dopamine levels collected during the four treatments are shown in max could arise from altered vesicular dopamine release and/or dopamine uptake, because both mechanisms regulating extracellular dopamine in the striatum operate concurrently during the stimulus train max. p). Overall, [DA]p and [DA]max  and ventral  striatum, a significant effect of train duration in the dorsal  and ventral  striatum, and a significant interaction in the dorsal  and ventral  striatum. In the dorsal striatum, 10 mg/kg AMPH and cocaine significantly increased [DA]p for the short train (p<0.02). 1 mg/kg AMPH was without effect, and no treatment had significant effects for the intermediate train. Both doses of AMPH significantly decreased [DA]p for the long train (p<0.01), while cocaine had no effect. All drug treatments significantly increased [DA]p in the ventral striatum for both short and intermediate trains (p<0.03) but were without effect for the long stimulation.Observed psychostimulant-induced changes in [DA] [DA]max  tracked 3,54 = 10.53, p<0.001) and ventral  striatum. Each drug treatment significantly decreased dopamine uptake compared to saline control in both striatal regions (p<0.01). AMPH- and cocaine-mediated uptake inhibition is consistent with our previous work using Michaelis-Menten kinetics max and [DA]p shown in max evoked by the trains used in this study are dominated by changes in vesicular dopamine release. The one overt exception is the intermediate train in the dorsal striatum, where cocaine increased [DA]max without a corresponding change in [DA]p. In this case, reduced dopamine uptake dominates the increase in [DA]max. Overall, these results demonstrate that AMPH and cocaine increase vesicular dopamine release in both striatal regions with the short train but that AMPH decreases vesicular dopamine release in the dorsal striatum with the long train.Psychostimulant effects on dopamine uptake are shown in 3,22 = 3.38, p = 0.04), time , and interaction . A post hoc comparison of the average change across the last two minutes of the time course (INSET) revealed that only 10 mg/kg AMPH significantly increased tonic dopamine levels compared to saline (p<0.01). In the ventral striatum . Overall, these results suggested that AMPH is more effective at increasing tonic dopamine signaling than cocaine and in the dorsal compared to the ventral striatum initially after drug injection.striatum  region, max of phasic-like dopamine responses evoked by the short train  and cocaine representing the DAT “inhibitors” max .The neurobiological implications of these psychostimulant actions are not presently known, but they could be profound. Several drugs of abuse have now been demonstrated to augment dopamine transients, including amphetamine, cocaine, nicotine and ethanol


EHP received nearly 1,500 papers during 2012, and nearly 500 of those papers were sent out for peer review. Each paper is evaluated by at least 2 reviewers for scientific quality and content. EHP is very grateful to the more than 1,000 reviewers who were part of the peer-review process. A list of those reviewers is available on the journal’s website (http://ehp.niehs.nih.gov/list-of-reviewers-2012/). EHP would also like to recognize our top 12 Reviewers of the Year. These individuals completed at least 7 papers during the year and received high ratings from the Associate Editors for their timeliness and the quality of their reviews. The EHP 2012 Reviewers of the Year are Sara Adar, Andrea Baccarelli, Aaron Barchowsky (not pictured), Joe Braun, David Carpenter, Ulrike Gehring, Gerard Hoek, Barbara Hoffmann, Matthew Longnecker, Roger McClellan, Allan Smith, and Leonardo Trasande. EHP is grateful to these and all other reviewers who assisted us during 2012.