Comparative study of the spatial relationship between nicotinamide adenine dinucleotide phosphate-diaphorase activity, serotonin immunoreactivity, and GYIRFamide immunoreactivity and the musculature of the adult liver fluke, Fasciola hepatica (Digenea, Fasciolidae)

This is the first detailed description of the nitrergic nervous system in a fluke. In this study, the authors analysed the distribution of the nicotinamide adenine dinucleotide phosphate-diaphorase (NADPH-d) reactivity in neuronal and nonneuronal tissues of the adult fluke Fasciola hepatica and compared this with the distribution of the musculature using tetramethylrhodamine isothiocyanate-phalloidin. To assess the correlation between the number of muscle cells in different parts of the fluke and the NADPH-d-stained cells, the nuclei were stained with Hoechst 333 42, which is specific for chromatin. The spatial relation between the NADPH-d-positive nerves and the 5-hydroxytryptamine (serotonin; 5-HT)-immunoreactive (-IR) and GYIRFamide-IR nervous elements was also examined. The methods complement each other. NADPH-d-positive staining occurs in both in neuronal tissue and nonneuronal tissue. Large, NADPH-d-stained neurones were localised in the nervous system. The oral and ventral suckers are innervated with many large NADPH-d-stained neurones. Ln addition, the NADPH-d staining reaction follows closely the muscle fibres in both the suckers, in the body, and in the ducts of the reproductive organs. The presence of NADPH-d activity along muscle fibres in F. hepatica and in other flatworms supports a possible myoinhibitory role for nitric oxide. Neuronal nitric oxide synthase in flatworms may form a novel drug target, which would facilitate the development of a novel anthelminthic. (C) 2001 Wiley-Liss, Inc.

Activation of the cGMP/PKG pathway inhibits electrical activity in rabbit urethral interstitial cells of Cajal by reducing the spatial spread of Ca2+ waves.

In the present study we used a combination of patch clamping and fast confocal Ca2+ imaging to examine the effects of activators of the nitric oxide (NO)/cGMP pathway on pacemaker activity in freshly dispersed ICC from the rabbit urethra, using the amphotericin B perforated patch configuration of the patch-clamp technique. The nitric oxide donor, DEA-NO, the soluble guanylyl cyclase activator YC-1 and the membrane-permeant analogue of cGMP, 8-Br-cGMP inhibited spontaneous transient depolarizations (STDs) and spontaneous transient inward currents (STICs) recorded under current-clamp and voltage-clamp conditions, respectively. Caffeine-evoked Cl- currents were unaltered in the presence of SP-8-Br-PET-cGMPs, suggesting that activation of the cGMP/PKG pathway does not block Cl- channels directly or interfere with Ca2+ release via ryanodine receptors (RyR). However, noradrenaline-evoked Cl- currents were attenuated by SP-8-Br-PET-cGMPs, suggesting that activation of cGMP-dependent protein kinase (PKG) may modulate release of Ca2+ via IP3 receptors (IP3R). When urethral interstitial cells (ICC) were loaded with Fluo4-AM (2 microm), and viewed with a confocal microscope, they fired regular propagating Ca2+ waves, which originated in one or more regions of the cell. Application of DEA-NO or other activators of the cGMP/PKG pathway did not significantly affect the oscillation frequency of these cells, but did significantly reduce their spatial spread. These effects were mimicked by the IP3R blocker, 2-APB (100 microm). These data suggest that NO donors and activators of the cGMP pathway inhibit electrical activity of urethral ICC by reducing the spatial spread of Ca2+ waves, rather than decreasing wave frequency.

Activation of G-protein-coupled receptor 30 is sufficient to enhance spatial recognition memory in ovariectomized rats

In ovariectomized rats, administration of estradiol, or selective estrogen receptor agonists that activate either the alpha or beta isoforms, have been shown to enhance spatial cognition on a variety of learning and memory tasks, including those that capitalize on the preference of rats to seek out novelty. Although the effects of the putative estrogen G-protein-coupled receptor 30 (GPR30) on hippocampus-based tasks have been reported using food-motivated tasks, the effects of activation of GPR30 receptors on tasks that depend on the preference of rats to seek out spatial novelty remain to be determined. Therefore, the aim of the current study was to determine if short-term treatment of ovariectomized rats with G-1, an agonist for GPR30, would mimic the effects on spatial recognition memory observed following short-term estradiol treatment. In Experiment 1, ovariectomized rats treated with a low dose (1mug) of estradiol 48h and 24h prior to the information trial of a Y-maze task exhibited a preference for the arm associated with the novel environment on the retention trial conducted 48h later. In Experiment 2, treatment of ovariectomized rats with G-1 (25mug) 48h and 24h prior to the information trial of a Y-maze task resulted in a greater preference for the arm associated with the novel environment on the retention trial. Collectively, the results indicated that short-term treatment of ovariectomized rats with a GPR30 agonist was sufficient to enhance spatial recognition memory, an effect that also occurred following short-term treatment with a low dose of estradiol.

Investigating Environmental Determinants of Diet, Physical Activity, and Overweight among Adults in Sao Paulo, Brazil

There is worldwide recognition that the burden of noncommunicable diseases (NCDs) and obesity-related health problems is rapidly increasing in low- and middle-income countries. Environmental determinants of obesity are likely to differ between countries, particularly in those undergoing rapid socioeconomic and nutrition transitions such as Brazil. This study aims to describe some built environment and local food environment variables and to explore their association with the overweight rate and diet and physical activity area-level aggregated indicators of adults living in the city of Sao Paulo, the largest city in Brazil. This formative study includes an ecological analysis of environmental factors associated with overweight across 31 submunicipalities of the city of Sao Paulo using statistical and spatial analyses. Average prevalence of overweight was 41.69% (95% confidence interval 38.74, 44.64), ranging from 27.14% to 60.75% across the submunicipalities. There was a wide geographical variation of both individual diet and physical activity, and indicators of food and built environments, favoring wealthier areas. After controlling for area socioeconomic status, there was a positive correlation between regular fruits and vegetables (FV) intake and density of FV specialized food markets (r = 0.497; p < 0.001), but no relationship between fast-food restaurant density and overweight prevalence was found. A negative association between overweight prevalence and density of parks and public sport facilities was seen (r = -0.527; p < 0.05). Understanding the relationship between local neighborhood environments and increasing rates of poor diet, physical activity, and obesity is essential in countries undergoing rapid economic and urban development, such as Brazil, in order to provide insights for policies to reduce increasing rates of NCDs and food access and health inequalities.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Recognition of idle resources in time-driven activity-based costing and resource consumption accounting models

Despite its theoretical superiority over traditional volume-based costing models, the Activity-Based Costing (ABC) model has failed to replace traditional volume-based costing models in most organisations. In response to the problems of the model, Time-Driven Activity-Based Costing (TDABC) and Resource Consumption Accounting (RCA) models have been developed as costing models for next generation cost management systems. A key feature that distinguishes TDABC and RCA models from traditional volume-based costing models and the ABC model is the recognition of idle resources in resource pools.

This paper presents a discussion on implications of recognising idle resources in TDABC and RCA models on developments, maintenance and uses of cost management systems. A hypothetical case is presented to illustrate conversions of an ABC-based costing model to ones that are based on the TDABC and RCA models, and the resulting new allocation of resource costs.

Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.

Recognising and monitoring high-level behaviours in complex spatial environments

The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. This paper presents a system for recognising high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviours. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviours of people from observing their trajectories within a real, complex indoor environment.

Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique. © 2008 IEEE.