Non-D9-tetrahydrocannabinol phytocannabinoids stimulate feeding in rats

Cannabinoid type 1 receptor-mediated appetite stimulation by D9tetrahydrocannabinol (D9THC) is well understood. Recently, it has become apparent that non-D9THC phytocannabinoids could also alter feeding patterns. Here, we show definitively that non-D9THC phytocannabinoids stimulate feeding. Twelve male, Lister-Hooded rats were prefed to satiety prior to administration of a standardized cannabis extract or to either of two mixtures of pure phytocannabinoids (extract analogues) comprising the phytocannabinoids present in the same proportions as the standardized extract (one with and one without D9THC). Hourly intake and meal pattern data were recorded and analysed using two-way analysis of variance followed by one-way analysis of variance and Bonferroni post-hoc tests. Administration of both extract analogues significantly increased feeding behaviours over the period of the test. All three agents increased hour-one intake and meal-one size and decreased the latency to feed, although the zero-D9THC extract analogue did so to a lesser degree than the high-D9THC analogue. Furthermore, only the analogue containing D9THC significantly increased meal duration. The data confirm that at least one non-D9THC phytocannabinoid induces feeding pattern changes in rats, although further trials using individual phytocannabinoids are required to fully understand the observed effects.

Cannabinol and cannabidiol exert opposing effects on rat feeding patterns

Rationale: Increased food consumption following Δ9- tetrahydrocannabinol-induced cannabinoid type 1 receptor agonism is well documented. However, possible non-Δ9- tetrahydrocannabinol phytocannabinoid-induced feeding effects have yet to be fully investigated. Therefore, we have assessed the effects of the individual phytocannabinoids, cannabigerol, cannabidiol and cannabinol, upon feeding behaviors. Methods: Adult male rats were treated (p.o.) with cannabigerol, cannabidiol, cannabinol or cannabinol plus the CB1R antagonist, SR141716A. Prior to treatment, rats were satiated and food intake recorded following drug administration. Data were analyzed for hourly intake and meal microstructure. Results: Cannabinol induced a CB1R-mediated increase in appetitive behaviors via significant reductions in the latency to feed and increases in consummatory behaviors via increases in meal 1 size and duration. Cannabinol also significantly increased the intake during hour 1 and total chow consumed during the test. Conversely, cannabidiol significantly reduced total chow consumption over the test period. Cannabigerol administration induced no changes to feeding behavior. Conclusion: This is the first time cannabinol has been shown to increase feeding. Therefore, cannabinol could, in the future, provide an alternative to the currently used and psychotropic Δ9-tetrahydrocannabinol-based medicines since cannabinol is currently considered to be non-psychotropic. Furthermore, cannabidiol reduced food intake in line with some existing reports, supporting the need for further mechanistic and behavioral work examining possible anti-obesity effects of cannabidiol.

Is prospective memory enhanced by cue-action semantic relatedness and enactment at encoding?

Benefits and costs on prospective memory performance, of enactment at encoding and a semantic association between a cue-action word pair, were investigated in two experiments. Findings revealed superior performance for both younger and older adults following enactment, in contrast to verbal encoding, and when cue-action semantic relatedness was high. Although younger adults outperformed older adults, age did not moderate benefits of cue-action relatedness or enactment. Findings from a second experiment revealed that the inclusion of an instruction to perform a prospective memory task led to increments in response latency to items from the ongoing activity in which that task was embedded, relative to latencies when the ongoing task only was performed. However, this task interference ‘cost’ did not differ as a function of either cue-action relatedness or enactment. We argue that the high number of cue-action pairs employed here influenced meta-cognitive consciousness, hence determining attention allocation, in all experimental conditions.

Evaluation of empirical mode decomposition for event-related potential analysis

Current methods for estimating event-related potentials (ERPs) assume stationarity of the signal. Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity. We evaluated an EMD-based method for estimating the ERP. On simulated data, EMD substantially reduced background EEG while retaining the ERP. EMD-denoised single trials also estimated shape, amplitude, and latency of the ERP better than raw single trials. On experimental data, EMD-denoised trials revealed event-related differences between two conditions (condition A and B) more effectively than trials lowpass filtered at 40 Hz. EMD also revealed event-related differences on both condition A and condition B that were clearer and of longer duration than those revealed by low-pass filtering at 40 Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Protease-activated receptor 2 sensitizes TRPV1 by protein kinase Cepsilon- and A-dependent mechanisms in rats and mice

Proteases that are released during inflammation and injury cleave protease-activated receptor 2 (PAR2) on primary afferent neurons to cause neurogenic inflammation and hyperalgesia. PAR2-induced thermal hyperalgesia depends on sensitization of transient receptor potential vanilloid receptor 1 (TRPV1), which is gated by capsaicin, protons and noxious heat. However, the signalling mechanisms by which PAR2 sensitizes TRPV1 are not fully characterized. Using immunofluorescence and confocal microscopy, we observed that PAR2 was colocalized with protein kinase (PK) Cepsilon and PKA in a subset of dorsal root ganglia neurons in rats, and that PAR2 agonists promoted translocation of PKCepsilon and PKA catalytic subunits from the cytosol to the plasma membrane of cultured neurons and HEK 293 cells. Subcellular fractionation and Western blotting confirmed this redistribution of kinases, which is indicative of activation. Although PAR2 couples to phospholipase Cbeta, leading to stimulation of PKC, we also observed that PAR2 agonists increased cAMP generation in neurons and HEK 293 cells, which would activate PKA. PAR2 agonists enhanced capsaicin-stimulated increases in [Ca2+]i and whole-cell currents in HEK 293 cells, indicating TRPV1 sensitization. The combined intraplantar injection of non-algesic doses of PAR2 agonist and capsaicin decreased the latency of paw withdrawal to radiant heat in mice, indicative of thermal hyperalgesia. Antagonists of PKCepsilon and PKA prevented sensitization of TRPV1 Ca2+ signals and currents in HEK 293 cells, and suppressed thermal hyperalgesia in mice. Thus, PAR2 activates PKCepsilon and PKA in sensory neurons, and thereby sensitizes TRPV1 to cause thermal hyperalgesia. These mechanisms may underlie inflammatory pain, where multiple proteases are generated and released.

The time course of implicit affective picture processing: an eye movement study

Consistent with a negativity bias account, neuroscientific and behavioral evidence demonstrates modulation of even early sensory processes by unpleasant, potentially threat-relevant information. The aim of this research is to assess the extent to which pleasant and unpleasant visual stimuli presented extrafoveally capture attention and impact eye movement control. We report an experiment examining deviations in saccade metrics in the presence of emotional image distractors that are close to a nonemotional target. We additionally manipulate the saccade latency to test when the emotional distractor has its biggest impact on oculomotor control. The results demonstrate that saccade landing position was pulled toward unpleasant distractors, and that this pull was due to the quick saccade responses. Overall, these findings support a negativity bias account of early attentional control and call for the need to consider the time course of motivated attention when affect is implicit

Axonal velocity distributions in neural feld equations

By modelling the average activity of large neuronal populations, continuum mean field models (MFMs) have become an increasingly important theoretical tool for understanding the emergent activity of cortical tissue. In order to be computationally tractable, long-range propagation of activity in MFMs is often approximated with partial differential equations (PDEs). However, PDE approximations in current use correspond to underlying axonal velocity distributions incompatible with experimental measurements. In order to rectify this deficiency, we here introduce novel propagation PDEs that give rise to smooth unimodal distributions of axonal conduction velocities. We also argue that velocities estimated from fibre diameters in slice and from latency measurements, respectively, relate quite differently to such distributions, a significant point for any phenomenological description. Our PDEs are then successfully fit to fibre diameter data from human corpus callosum and rat subcortical white matter. This allows for the first time to simulate long-range conduction in the mammalian brain with realistic, convenient PDEs. Furthermore, the obtained results suggest that the propagation of activity in rat and human differs significantly beyond mere scaling. The dynamical consequences of our new formulation are investigated in the context of a well known neural field model. On the basis of Turing instability analyses, we conclude that pattern formation is more easily initiated using our more realistic propagator. By increasing characteristic conduction velocities, a smooth transition can occur from self-sustaining bulk oscillations to travelling waves of various wavelengths, which may influence axonal growth during development. Our analytic results are also corroborated numerically using simulations on a large spatial grid. Thus we provide here a comprehensive analysis of empirically constrained activity propagation in the context of MFMs, which will allow more realistic studies of mammalian brain activity in the future.

Analysis of the variability of auditory brainstem response components through linear regression

(ABR) is of fundamental importance to the investiga- tion of the auditory system behavior, though its in- terpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analyzing the ABR, clinicians are often interested in the identi- fication of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave la- tency) is a practical tool for the diagnosis of disorders affecting the auditory system. In this context, the aim of this research is to compare ABR manual/visual analysis provided by different examiners. Methods: The ABR data were collected from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). A total of 160 data samples were analyzed and a pair- wise comparison between four distinct examiners was executed. We carried out a statistical study aiming to identify significant differences between assessments provided by the examiners. For this, we used Linear Regression in conjunction with Bootstrap, as a me- thod for evaluating the relation between the responses given by the examiners. Results: The analysis sug- gests agreement among examiners however reveals differences between assessments of the variability of the waves. We quantified the magnitude of the ob- tained wave latency differences and 18% of the inves- tigated waves presented substantial differences (large and moderate) and of these 3.79% were considered not acceptable for the clinical practice. Conclusions: Our results characterize the variability of the manual analysis of ABR data and the necessity of establishing unified standards and protocols for the analysis of these data. These results may also contribute to the validation and development of automatic systems that are employed in the early diagnosis of hearing loss.

Assessment of inter-examiner agreement and variability in the manual classification of auditory brainstem response

Abstract Background: The analysis of the Auditory Brainstem Response (ABR) is of fundamental importance to the investigation of the auditory system behaviour, though its interpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analysing the ABR, clinicians are often interested in the identification of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave latency) is a practical tool for the diagnosis of disorders affecting the auditory system. Significant differences in inter-examiner results may lead to completely distinct clinical interpretations of the state of the auditory system. In this context, the aim of this research was to evaluate the inter-examiner agreement and variability in the manual classification of ABR. Methods: A total of 160 ABR data samples were collected, for four different stimulus intensity (80dBHL, 60dBHL, 40dBHL and 20dBHL), from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). Four examiners with expertise in the manual classification of ABR components participated in the study. The Bland-Altman statistical method was employed for the assessment of inter-examiner agreement and variability. The mean, standard deviation and error for the bias, which is the difference between examiners’ annotations, were estimated for each pair of examiners. Scatter plots and histograms were employed for data visualization and analysis. Results: In most comparisons the differences between examiner’s annotations were below 0.1 ms, which is clinically acceptable. In four cases, it was found a large error and standard deviation (>0.1 ms) that indicate the presence of outliers and thus, discrepancies between examiners. Conclusions: Our results quantify the inter-examiner agreement and variability of the manual analysis of ABR data, and they also allows for the determination of different patterns of manual ABR analysis.

A parallel quantum histogram architecture

A parallel formulation of an algorithm for the histogram computation of n data items using an on-the-fly data decomposition and a novel quantum-like representation (QR) is developed. The QR transformation separates multiple data read operations from multiple bin update operations thereby making it easier to bind data items into their corresponding histogram bins. Under this model the steps required to compute the histogram is n/s + t steps, where s is a speedup factor and t is associated with pipeline latency. Here, we show that an overall speedup factor, s, is available for up to an eightfold acceleration. Our evaluation also shows that each one of these cells requires less area/time complexity compared to similar proposals found in the literature.

The relationship between parent and child dysfunctional beliefs about sleep and child sleep

Cognitive theories emphasise the role of dysfunctional beliefs about sleep in the development and maintenance of sleep-related problems (SRPs). The present research examines how parents' dysfunctional beliefs about children's sleep and child dysfunctional beliefs about sleep are related to each other and to children's subjective and objective sleep. Participants were 45 children aged 11 -12 years and their parents. Self-report measures of dysfunctional beliefs about sleep and child sleep were completed by children, mothers and fathers. Objective measures of child sleep were taken using actigraphy. The results showed that child dysfunctional beliefs about sleep were correlated with father (r=.43, p<.05) and mother (r=.43, p<.05) reported child SRPs, and with Sleep Onset Latency (r=.34, p<.05). Maternal dysfunctional beliefs about child sleep were related to child SRPs as reported by mothers (r=.44, p<.05), and to child dysfunctional beliefs about sleep (r=.37, p<.05). Some initial evidence was found for a mediation pathway in which child dyfunctional beliefs mediate the relationship between parent dysfunctional beliefs and child sleep. The results support the cognitive model of SRPs and contribute to the literature by providing the first evidence of familial aggregation of dysfunctional beliefs about sleep.

Cannabidivarin (CBDV) suppresses pentylenetetrazole (PTZ)-induced increases in epilepsy-related gene expression

To date, anticonvulsant effects of the plant cannabinoid, cannabidivarin (CBDV), have been reported in several animal models of seizure. However, these behaviourally observed anticonvulsant effects have not been confirmed at the molecular level. To examine changes to epilepsy-related gene expression following chemical convulsant treatment and their subsequent control by phytocannabinoid administration, we behaviourally evaluated effects of CBDV (400 mg/kg, p.o.) on acute, pentylenetetra- zole (PTZ: 95 mg/kg, i.p.)-induced seizures, quantified expression levels of several epilepsy-related genes (Fos, Casp 3, Ccl3, Ccl4, Npy, Arc, Penk, Camk2a, Bdnf and Egr1) by qPCR using hippocampal, neocortical and prefrontal cortical tissue samples before examining correlations between expression changes and seizure severity. PTZ treatment alone produced generalised seizures (median: 5.00) and significantly increased expression of Fos, Egr1, Arc, Ccl4 and Bdnf. Consistent with previous findings, CBDV significantly decreased PTZ-induced seizure severity (median: 3.25) and increased latency to the first sign of seizure. Furthermore, there were correlations between reductions of seizure severity and mRNA expression of Fos, Egr1, Arc, Ccl4 and Bdnf in the majority of brain regions in the CBDV+PTZ treated group. When CBDV treated animals were grouped into CBDV responders (criterion: seizure severity ≤ 3.25) and non-responders (criterion: seizure severity >3.25), PTZ-induced increases of Fos, Egr1, Arc, Ccl4 and Bdnf expression were suppressed in CBDV re- sponders. These results provide the first molecular confirmation of behaviourally observed effects of the non-psychoactive, anticonvulsant cannabinoid, CBDV, upon chemically-induced seizures and serve to underscore its suitability for clinical development.

LTE and LTE-A interworking and interoperability with 3GPP and non-3GPP wireless networks

This paper provides a high-level overview of E-UTRAN interworking and interoperability with existing Third Generation Partnership Project (3GPP) and non-3GPP wireless networks. E-UTRAN access networks (LTE and LTE-A) are currently the latest technologies for 3GPP evolution specified in Release 8, 9 and beyond. These technologies promise higher throughputs and lower latency while also reducing the cost of delivering the services to fit with subscriber demands. 3GPP offers a direct transition path from the current 3GPP UTRAN/GERAN networks to LTE including seamless handover. E-UTRAN and other wireless networks interworking is an option that allows operators to maximize the life of their existing network components before a complete transition to truly 4G networks. Network convergence, backward compatibility and interpretability are regarded as the next major challenge in the evolution and the integration of mobile wireless communications. In this paper, interworking and interoperability between the E-UTRAN Evolved Packet Core (EPC) architecture and 3GPP, 3GPP2 and IEEE based networks are clearly explained. How the EPC is designed to deliver multimedia and facilitate interworking is also explained. Moreover, the seamless handover needed to perform this interworking efficiently is described briefly. This study showed that interoperability and interworking between existing networks and E-UTRAN are highly recommended as an interim solution before the transition to full 4G. Furthermore, wireless operators have to consider a clear interoperability and interworking plan for their existing networks before making a decision to migrate completely to LTE. Interworking provides not only communication between different wireless networks; in many scenarios it contributes to add technical enhancements to one or both environments.

Improving transmission reliability of low-power medium access control protocols using average diversity combining

Embedded computer systems equipped with wireless communication transceivers are nowadays used in a vast number of application scenarios. Energy consumption is important in many of these scenarios, as systems are battery operated and long maintenance-free operation is required. To achieve this goal, embedded systems employ low-power communication transceivers and protocols. However, currently used protocols cannot operate efficiently when communication channels are highly erroneous. In this study, we show how average diversity combining (ADC) can be used in state-of-the-art low-power communication protocols. This novel approach improves transmission reliability and in consequence energy consumption and transmission latency in the presence of erroneous channels. Using a testbed, we show that highly erroneous channels are indeed a common occurrence in situations, where low-power systems are used and we demonstrate that ADC improves low-power communication dramatically.