Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals

This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 +/- 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 +/- 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.

On a size-structured two-phase population model with infinite states-at-birth

In this work we introduce and analyze a linear size-structured population model with infinite states-at-birth. We model the dynamics of a population in which individuals have two distinct life-stages: an “active” phase when individuals grow, reproduce and die and a second “resting” phase when individuals only grow. Transition between these two phases depends on individuals’ size. First we show that the problem is governed by a positive quasicontractive semigroup on the biologically relevant state space. Then we investigate, in the framework of the spectral theory of linear operators, the asymptotic behavior of solutions of the model. We prove that the associated semigroup has, under biologically plausible assumptions, the property of asynchronous exponential growth.

Firm survival, uncertainty and financial frictions: Is there a financial uncertainty accelerator?

Using a large panel of unquoted UK over the period 2000-09, we examine the impact of firm-specific uncertainty on corporate failures. In this context we also distinguish between firms which are likely to be more or less dependant on bank finance as well as public and non-public companies. Our results document a significant effect of uncertainty on firm survival. This link is found to be more potent during the recent financial crisis compared with tranquil periods. We also uncover significant firm-level heterogeneity since the survival chance of bank-dependent and non-public firms are most affected by changes in uncertainty, especially during the recent global financial crisis.

Global Warming and Fat Tailed-uncertainty: Rethinking the Timing and Intensity of Climate Policy

The possibility of low-probability extreme natural events has reignited the debate over the optimal intensity and timing of climate policy. In this paper, we contribute to the literature by assessing the implications of low-probability extreme events on environmental policy in a continuous-time real options model with “tail risk”. In a nutshell, our results indicate the importance of tail risk and call for foresighted pre-emptive climate policies.

Firm survival, uncertainty and financial frictions: Is there a financial uncertainty accelerator?

Using a large panel of unquoted UK firms over the period 2000-09, we examine the impact of firm-specific uncertainty on corporate failures. In this context we also distinguish between firms which are likely to be more or less dependent on bank finance as well as public and non-public companies. Our results document a significant effect of uncertainty on firm survival. This link is found to be more potent during the recent financial crisis compared with tranquil periods. We also uncover significant firm-level heterogeneity since the survival chances of bank-dependent and non-public firms are most affected by changes in uncertainty, especially during the recent global financial crisis.

Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty

This paper extends the Nelson-Siegel linear factor model by developing a flexible macro-finance framework for modeling and forecasting the term structure of US interest rates. Our approach is robust to parameter uncertainty and structural change, as we consider instabilities in parameters and volatilities, and our model averaging method allows for investors' model uncertainty over time. Our time-varying parameter Nelson-Siegel Dynamic Model Averaging (NS-DMA) predicts yields better than standard benchmarks and successfully captures plausible time-varying term premia in real time. The proposed model has significant in-sample and out-of-sample predictability for excess bond returns, and the predictability is of economic value.

Quantile forecasts of inflation under model uncertainty

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future inflation by providing superior predictive densities compared to mean regression models with and without BMA.

Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This paper quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the paper proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. This methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.

Mean platelet volume in the early phase of acute ischemic stroke is not associated with severity or functional outcome.

Background: Previous studies reported an increase of mean platelet volume (MPV) in patients with acute ischemic stroke. However, its correlation with stroke severity has not been investigated. Moreover, studies on the association of MPV with functional outcome yielded inconsistent results. Methods: We included all consecutive ischemic stroke patients admitted to CHUV (Centre Hospitalier Universitaire Vaudois) Neurology Service within 24 h after stroke onset who had MPV measured on admission. The association of MPV with stroke severity (NIHSS score at admission and at 24 h) and outcome (Rankin Scale score at 3 and 12 months) was analyzed in univariate analysis. The chi(2) test was performed to compare the frequency of minor strokes (NIHSS score </=4) and good functional outcome (Rankin Scale score </=2) across MPV quartiles. The ANOVA test was used to compare MPV between stroke subtypes according to the TOAST classification. Student's two-tailed unpaired t test was performed to compare MPV between lacunar and nonlacunar strokes. MPV was generated at admission by the Sysmex XE-2100 automated cell counter (Sysmex Corporation, Kobe, Japan) from EDTA blood samples. Results: There was no significant difference in the frequency of minor strokes (p = 0.46) and good functional outcome (p = 0.06) across MPV quartiles. MPV was not associated with stroke severity or outcome in univariate analysis. There was no significant difference in MPV between stroke subtypes according to the TOAST classification (p = 0.173) or between lacunar and nonlacunar strokes (10.50 +/- 0.91 vs. 10.40 +/- 0.81 fl, p = 0.322). Conclusions: MPV, assessed within 24 h after ischemic stroke onset, is not associated with stroke severity or functional outcome.

T cell derived cytokines in lung-phase immunity to Schistosoma mansoni

In C57Bl/6 strain mice vaccinated with radiation-attenuated cercariae of Schistosoma mansoni immune elimination of challenge parasites occurs in the lungs. Leococytes were recovered from the lungs of such mice by bronchoalveolar lavage and cultured in vitro with larval antigen; the profile of cytokines released was then analyzed. From 14 days after vaccination, BAL cultures contained infiltrating lymphocytes wich produced abundant quantitties of IFN-g and IL-3. Challenge of vaccinated mice resulted in a second influx of IFN-g nd IL-3- producing cells, earlier than after vaccination or in the appropriate contropls. Ablation studies revealed that CD4+ T cells were the source of IFN-g. The timing of cytokine production after vaccination, and challenge was coincident with the phases of macrophage activation previously reported. At no time could lymphocytes in BAL cultures to stimulated to proliferate with either larval Ag or mitogen, in contrast to splenocytes from the same mice. Furthermore, T cell growth factor activity was not detected in BAL cultures stimulated with Ag. We suggest that the lymphocytes recruited to the lungs are memory/effector cells, When Ag. released challenge schistosomula is presented to these cells, they respond by secreting cytokines wich mediate the formation of cellular aggregates around the parasites, blocking their onward migration.

Experimental murine schistosomiasis mansoni: estabilishment of the chronic phase of the disease

After the acute hyperergic phase of schistosomal infection, the chronic phase of the disease corresponds to the estabilishment of a relative equilibrium between the host and the parasite. This involves: (1) A shift from the predominance of the TH2 response observed in the acute phase, to the predominance of the TH1 response in the chronic phase of the disease, with modification of lymphokine and immunoglobulin secretions patterns. (2) Redistribution of hosts responses to parasite, with predominance of systemic controls in the acute phase, and a shift towards local tissue responses in the chronic phase. This redistribution relieves the hyperergic responses involving the whole body of the host, and delimits cellular and molecular reactions to parasites to only those tissues that are directly involved by the adult parasites and their eggs. Mobilization of eosinophil granulocytes in schistosomal periovular granulomas is one of examples of this redistribution.

Development of Eosinophilia in dogs intradermically inoculated with sand fly saliva and Leishmania (Leishmania) chagasi stationary-phase promastigotes

Salivary glad lysates of the sand fly Lutzomia longipalpis have been shown to enhance the infectivity of Leishmania in mice. As shown herein, the simultaneous inoculation of Leishmania chagasi stationary-phase promastigotes and L. longipalpis salivary gland by the intradermal route in a group of mongrel dogs induced a statistically significant eosinophilia, in relation to dogs inoculated with Leishmania or with salivary gland lysate only. These dogs had no evidence of infection, in spite of the infectivity of the promastigotes when inoculated by the intravenous route.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.