Role of the endocannabinoid system in obesity induced by neuropeptide Y overexpression in noradrenergic neurons.

OBJECTIVE: Endocannabinoids and neuropeptide Y (NPY) promote energy storage via central and peripheral mechanisms. In the hypothalamus, the two systems were suggested to interact. To investigate such interplay also in non-hypothalamic tissues, we evaluated endocannabinoid levels in obese OE-NPY(DßH) mice, which overexpress NPY in the noradrenergic neurons in the sympathetic nervous system and the brain. METHODS: The levels of the endocannabinoids anandamide and 2-arachidonoylglycerol (2-AG) were measured in key regulatory tissues, that is, hypothalamus, pancreas, epididymal white adipose tissue (WAT), liver and soleus muscle, over the development of metabolic dysfunctions in OE-NPY(DßH) mice. The effects of a 5-week treatment with the CB1 receptor inverse agonist AM251 on adiposity and glucose metabolism were studied. RESULTS: 2-AG levels were increased in the hypothalamus and epididymal WAT of pre-obese and obese OE-NPY(DßH) mice. Anandamide levels in adipose tissue and pancreas were increased at 4 months concomitantly with higher fat mass and impaired glucose tolerance. CB1 receptor blockage reduced body weight gain and glucose intolerance in OE-NPY(DßH) to the level of vehicle-treated wild-type mice. CONCLUSIONS: Altered endocannabinoid tone may underlie some of the metabolic dysfunctions in OE-NPY(DßH) mice, which can be attenuated with CB1 inverse agonism suggesting interactions between endocannabinoids and NPY also in the periphery. CB1 receptors may offer a target for the pharmacological treatment of the metabolic syndrome with altered NPY levels.

α-MSH overexpression in the nucleus tractus solitarius decreases fat mass and elevates heart rate.

The POMC pathway is involved in the regulation of energy and cardiovascular homeostasis in the hypothalamus and the brain stem. Although the acute effects of POMC-derived peptides in different brain locations have been elucidated, the chronic site-specific effects of distinct peptides remain to be studied. To this end, we used a lentiviral gene delivery vector to study the long-term effects of α-MSH in the nucleus tractus solitarius (NTS) of the brain stem. The α-MSH vector (LVi-α-MSH-EGFP) based on the N-terminal POMC sequence and a control vector (LVi-EGFP) were delivered into the NTS of C57BL/6N male mice fed on a western diet. Effects on body weight and composition, feeding, glucose metabolism, and hemodynamics by telemetric analyses were studied during the 12-week follow-up. The LVi-α-MSH-EGFP-treated mice had a significantly smaller gain in the fat mass compared with LVi-EGFP-injected mice. There was a small initial decrease in food intake and no differences in the physical activity. Glucose metabolism was not changed compared with the control. LVi-α-MSH-EGFP increased the heart rate (HR), which was attenuated by adrenergic blockade suggesting an increased sympathetic activity. Reduced response to muscarinic blockade suggested a decreased parasympathetic activity. Fitting with sympathetic activation, LVi-α-MSH-EGFP treatment reduced urine secretion. Thus, the results demonstrate that long-term α-MSH overexpression in the NTS attenuates diet-induced obesity. Modulation of autonomic nervous system tone increased the HR and most probably contributed to an anti-obesity effect. The results underline the key role of NTS in the α-MSH-induced long-term effects on adiposity and in regulation of sympathetic and parasympathetic activities.

Spatiotemporal structure of host-pathogen interactions in a metapopulation.

The ecological and evolutionary dynamics of species are influenced by spatiotemporal variation in population size. Unfortunately, we are usually limited in our ability to investigate the numerical dynamics of natural populations across large spatial scales and over long periods of time. Here we combine mechanistic and statistical approaches to reconstruct continuous-time infection dynamics of an obligate fungal pathogen on the basis of discrete-time occurrence data. The pathogen, Podosphaera plantaginis, infects its host plant, Plantago lanceolata, in a metapopulation setting where the presence of the pathogen has been recorded annually for 6 years in approximately 4,000 host populations across an area of 50 km x 70 km in Finland. The dynamics are driven by strong seasonality, with a high extinction rate during winter and epidemic expansion in summer for local pathogen populations. We are able to identify with our model the regions in the study area where overwintering has been most successful. These overwintering sites represent foci that initiate local epidemics during the growing season. There is striking heterogeneity at the regional scale in both the overwintering success of the pathogen and the encounter intensity between the host and the pathogen. Such heterogeneity has profound implications for the coevolutionary dynamics of the interaction.

Mechanical-statistical modeling in ecology: from outbreak detections to pest dynamics.

Knowledge about large-scale and long-term dynamics of (natural) populations is required to assess the efficiency of control strategies, the potential for long-term persistence, and the adaptability to global changes such as habitat fragmentation and global warming. For most natural populations, such as pest populations, large-scale and long-term surveys cannot be carried out at a high resolution. For instance, for population dynamics characterized by irregular abundance explosions, i.e., outbreaks, it is common to report detected outbreaks rather than measuring the population density at every location and time event. Here, we propose a mechanical-statistical model for analyzing such outbreak occurrence data and making inference about population dynamics. This spatio-temporal model contains the main mechanisms of the dynamics and describes the observation process. This construction enables us to account for the discrepancy between the phenomenon scale and the sampling scale. We propose the Bayesian method to estimate model parameters, pest densities and hidden factors, i.e., variables involved in the dynamics but not observed. The model was specified and used to learn about the dynamics of the European pine sawfly (Neodiprion sertifer Geoffr., an insect causing major defoliation of pines in northern Europe) based on Finnish sawfly data covering the years 1961-1990. In this application, a dynamical Beverton-Holt model including a hidden regime variable was incorporated into the model to deal with large variations in the population densities. Our results gave support to the idea that pine sawfly dynamics should be studied as metapopulations with alternative equilibria. The results confirmed the importance of extreme minimum winter temperatures for the occurrence of European pine sawfly outbreaks. The strong positive connection between the ratio of lake area over total area and outbreaks was quantified for the first time.

A hierarchical Bayesian birth cohort analysis from incomplete registry data: evaluating the trends in the age of onset of insulin-dependent diabetes mellitus (T1DM).

Childhood diabetes is one of the major non-communicable diseases in children under 15 years of age. It requires a life-long insulin treatment and may lead to serious complications. Along with the worldwide increase in the incidence several countries have recently reported a decreasing trend in the age of onset of the disease. The aim of this study is to analyse long-term data on the incidence of the childhood diabetes in Finland from the birth cohorts perspective. The annual incidence data were available for the period 1965--1996 which translates into 1951--1996 birth cohorts. Hence the data consist of completely and partially observed cohorts. Bayesian modelling was employed in the analysis. Several different priors and cohort combinations were tried in order to determine the sensitivity of the results. The cumulative birth cohort incidence of diabetes was determined to have an increasing average annual trend of 2.5 per cent. Although the average birth cohort-specific age of onset was estimated to have decreased slightly over the years of observation, the trend could be a result of random variation.

Asthma exacerbations in children immediately following stressful life events: a Cox's hierarchical regression.

BACKGROUND: A recent prospective study of children with asthma employing a within subject, over time analysis using dynamic logistic regression showed that severely negative life events significantly increased the risk of an acute exacerbation during the subsequent 6 week period. The timing of the maximum risk depended on the degree of chronic psychosocial stress also present. A hierarchical Cox regression analysis was undertaken to examine whether there were any immediate effects of negative life events in children without a background of high chronic stress. METHODS: Sixty children with verified chronic asthma were followed prospectively for 18 months with continuous monitoring of asthma by daily symptom diaries and peak flow measurements, accompanied by repeated interview assessments of life events. The key outcome measures were asthma exacerbations and severely negative life events. RESULTS: An immediate effect evident within the first 2 days following a severely negative life event increased the risk of a new asthma attack by a factor of 4.69, 95% confidence interval 2.33 to 9.44 (p<0.001) [corrected] In the period 3-10 days after a severe event there was no increased risk of an asthma attack (p = 0.5). In addition to the immediate effect, an increased risk of 1.81 (95% confidence interval 1.24 to 2.65) [corrected] was found 5-7 weeks after a severe event (p = 0.002). This is consistent with earlier findings. There was a statistically significant variation due to unobserved factors in the incidence of asthma attacks between the children. CONCLUSION: The use of statistical methods capable of investigating short time lags showed that stressful life events significantly increase the risk of a new asthma attack immediately after the event; a more delayed increase in risk was also evident 5-7 weeks later.

Small area estimation of incidence of cancer around a known source of exposure with fine resolution data.

OBJECTIVES: To describe the small area system developed in Finland. To illustrate the use of the system with analyses of incidence of lung cancer around an asbestos mine. To compare the performance of different spatial statistical models when applied to sparse data. METHODS: In the small area system, cancer and population data are available by sex, age, and socioeconomic status in adjacent "pixels", squares of size 0.5 km x 0.5 km. The study area was partitioned into sub-areas based on estimated exposure. The original data at the pixel level were used in a spatial random field model. For comparison, standardised incidence ratios were estimated, and full bayesian and empirical bayesian models were fitted to aggregated data. Incidence of lung cancer around a former asbestos mine was used as an illustration. RESULTS: The spatial random field model, which has been used in former small area studies, did not converge with present fine resolution data. The number of neighbouring pixels used in smoothing had to be enlarged, and informative distributions for hyperparameters were used to stabilise the unobserved random field. The ordered spatial random field model gave lower estimates than the Poisson model. When one of the three effects of area were fixed, the model gave similar estimates with a narrower interval than the Poisson model. CONCLUSIONS: The use of fine resolution data and socioeconomic status as a means of controlling for confounding related to lifestyle is useful when estimating risk of cancer around point sources. However, better statistical methods are needed for spatial modelling of fine resolution data.

Probabilistic small area risk assessment using GIS-based data: a case study on Finnish childhood diabetes. Geographic information systems.

A Bayesian hierarchical spatial model is constructed to describe the regional incidence of insulin dependent diabetes mellitus (IDDM) among the under 15-year-olds in Finland. The model exploits aggregated pixel-wise locations for both the cases and the population at risk. Typically such data arise from combining geographic information systems (GIS) with large databases. The dates of diagnosis and locations of the cases are observed from 1987 to 1996. The population at risk counts are available for every second year during the same period. A hierarchical model is suggested for the pixel wise case counts, including a population model to account for the uncertainty of the population at risk over the years. The model is applied in the construction of disease maps (aggregated 100 km(2) pixels), and spatial posterior predictive distributions are computed to study whether there can be found a statistically exceptional number of cases in a small area of interest.

Somatosensory evoked cerebral magnetic fields from SI and SII in man.

We have recorded cerebral magnetic fields elicited by electrical stimulation of median and peroneal nerves. Field mapping indicates that the deflections at 30-80 and 150-180 msec are due to activity at SI. Additional activity at 90-125 msec is generated at SII, on the superior bank of the sylvian fissure. At SI, the source locations are in agreement with the known somatotopy. Only contralateral stimuli evoke responses at SI, whereas both ipsi- and contralateral stimuli elicit responses at SII.

A high resolution ultrasonic microscope.

The construction and imaging capabilities of a scanning high resolution ultrasonic microscope are considered in this paper. Theoretical expressions for the resolution limits of the microscope and some of the most important properties of the ultrasonic lenses are given. Ultrasonic through-transmission and back-scattering images of different objects are presented and a quasi-three-dimensional display mode is introdued. A resolution of about 4 micron is demonstrated at 300 MHZ by the present ultrasonic microscope.