Can a microscopic stochastic model explain the emergence of pain cycles in patients?

A stochastic model is here introduced to investigate the molecular mechanisms which trigger the perception of pain. The action of analgesic drug compounds is discussed in a dynamical context, where the competition with inactive species is explicitly accounted for. Finite size effects inevitably perturb the mean-field dynamics: Oscillations in the amount of bound receptors spontaneously manifest, driven by the noise which is intrinsic to the system under scrutiny. These effects are investigated both numerically, via stochastic simulations and analytically, through a large-size expansion. The claim that our findings could provide a consistent interpretative framework to explain the emergence of cyclic behaviors in response to analgesic treatments, is substantiated.Comment: J. Stat. Mech. (Proceedings UPON2008

Atomic Parity Violation : Principles, Recent Results, Present Motivations

We review the progress made in the determination of the weak charge, Q\_w, of the cesium nucleus which raises the status of Atomic Parity Violation measurements to that of a precision electroweak test. Not only is it necessary to have a precision measurement of the electroweak asymmetry in the highly forbidden 6S-7S transition, but one also needs a precise calibration procedure. The 1999 precision measurement by the Boulder group implied a 2.5 sigma deviation of Q\_w from the theoretical prediction. This triggered many particle physicist suggestions as well as examination by atomic theoretical physicists of several sources of corrections. After about three years the disagreement was removed without appealing to "New Physics". Concurrently, an original experimental approach was developed in our group for more than a decade. It is based on detection by stimulated emission with amplification of the left- right asymmetry. We present our decisive, recent progress together with our latest results. We emphasize the important impact for electroweak theory, of future measurements in cesium possibly pushed to the 0.1% level. Other possible approaches are currently explored in several atoms

Assessing the stability of tree ranges and influence of disturbance in eastern US forests

Shifts in tree species ranges may occur due to global climate change, which in turn may be exacerbated by natural disturbance events. Within the context of global climate change, developing techniques to monitor tree range dynamics as affected by natural disturbances may enable mitigation/adaptation of projected impacts. Using a forest inventory across the eastern U.S., the northern range margins of tree distributions were examined by comparing differences in the 95th percentile locations of seedlings to adults (i.e., trees) by 0.5° longitudinal bands over 5-years and by levels of disturbance (i.e., canopy gap formation). Our results suggest that the monitoring of tree range dynamics is complicated by the limits of forest inventory data across varying spatial/temporal scales and the diversity of tree species/environments in the eastern U.S. The vast majority of tree and seedling latitudinal comparisons across measurement periods and levels of disturbance in the study were not statistically different from zero (53 out of 60 comparisons). A potential skewing of ranges towards a northern limit was suggested by the stability of northern margins of tree ranges found in this study and shifts in mean locations identified in previous work. Only a partial influence of disturbances on tree range dynamics during the course of the 5-years was found in this study. The results of this study underscore the importance of continued examination of the role of disturbance in tree range dynamics and refined range monitoring techniques given future forest extent and biodiversity implications.Keywords: Disturbance, Seedlings, Tree range retreat, Climate change, Tree species migration, Canopy gapsKeywords: Disturbance, Seedlings, Tree range retreat, Climate change, Tree species migration, Canopy gap

Anxiety and depression after prostate cancer diagnosis and treatment: 5-year follow-up

To document anxiety and depression from pretreatment till 5-year follow-up in 299 men with localized prostate cancer. To assess, if baseline scores were predictive for anxiety and depression at 1-year follow-up. Respondents completed four assessments (pretreatment, at 6 and 12 months, and at 5-year follow-up) on anxiety, depression and mental health. Respondents were subdivided according to therapy (prostatectomy or radiotherapy) and high vs low-anxiety. Pretreatment 28% of all patients were classified as ‘high-anxiety'; their average anxiety scores decreased significantly post-treatment, that is towards less anxiety. At all assessments, high-anxiety men treated by prostatectomy reported less depression than high-anxiety men treated by radiotherapy. Of men treated by radiotherapy, 27% reported clinical significant levels of depression while 20% is expected in a general population. The improvement in mental health at 6-months follow-up was statistically significant and clinically meaningful in all respondent groups. Sensitivity of anxiety at baseline as a screening tool was 71% for anxiety and 60% for symptoms of depression. We recommend clinicians to attempt early detection of patients at risk of high levels of anxiety and depression after prostate cancer diagnosis since prevalence is high. STAI-State can be a useful screening tool but needs further development

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed