Effects of thoracic epidural anesthesia on hemodynamics and global oxygen transport in ovine endotoxemia

Besides providing effective analgesia, thoracic epidural anesthesia (TEA) has been shown to decrease perioperative morbidity and mortality. Because of its vasodilatory properties in association with the sympathetic blockade, however, TEA may potentially aggravate cardiovascular dysfunctions resulting from sepsis and systemic inflammatory response syndrome. The objective of the present study was to assess the effects of TEA on hemodynamics, global oxygen transport, and renal function in ovine endotoxemia. After a baseline measurement in healthy sheep (n = 18), Salmonella typhosa endotoxin was centrally infused at incremental doses to induce and maintain a hypotensive-hypodynamic circulation using an established protocol. The animals were then randomly assigned to one of two groups. In the treatment group, continuous TEA was initiated with 0.1 mL.kg of 0.125% bupivacaine at the onset of endotoxemia and maintained with 0.1 mL.kg.h. In the control group, the same amount of isotonic sodium chloride solution was injected through the epidural catheter. In the animals surviving the entire experiment (n = 7 per group), cardiac index and mean arterial pressure decreased in a dose-dependent manner during endotoxin infusion. In the TEA group, neither systemic hemodynamics nor global oxygen transport were impaired beyond the changes caused by endotoxemia itself. Urinary output was increased in the TEA group as compared with the control group (P < 0.05). In this model of endotoxic shock, TEA improved renal perfusion without affecting cardiopulmonary hemodynamics and global oxygen transport.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Modulating the granularity of category formation by global cortical states

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients.

Comparison of low potassium Euro-Collins solution and standard Euro-Collins solution in an extracorporeal rat heart-lung model

OBJECTIVE: Euro-Collins solution (EC) is routinely used in lung transplantation. The high potassium of EC, however, may damage the vascular endothelium, thereby contributing to postischemic reperfusion injury. To assess the influence of the potassium concentration on lung preservation, we evaluated the effect of a "low potassium Euro-Collins solution" (LPEC), in which the sodium and potassium concentrations were reversed. METHODS: In an extracorporeal rat heart-lung model lungs were preserved with EC and LPEC. The heart-lung blocks (HLB) were perfused with Krebs-Henseleit solution containing washed bovine red blood cells and ventilated with room air. The lungs were perfused via the working right ventricle with deoxygenated perfusate. Oxygenation and pulmonary vascular resistance (PVR) were monitored. After baseline measurements, hearts were arrested with St. Thomas' solution and the lungs were perfused with EC or LPEC, or were not perfused (controls). The HLBs were stored for 5 min or 2 h ischemic time at 4 degrees C. Reperfusion and ventilation was performed for 40 min. At the end of the trial the wet/dry ratio of the lungs was calculated and light microscopic assessment of the degree of edema was performed. RESULTS: After 5 min of ischemia oxygenation was significantly better in both preserved groups compared to the controls. Pulmonary vascular resistance was elevated in all three groups after 30 min reperfusion at both ischemic times. After 2 h of ischemia PVR of the group preserved with LPEC was significantly lower than those of the EC and controls (LPEC-5 min: 184 +/- 65 dynes * sec * cm-5, EC-5 min: 275 +/- 119 dynes * sec * cm * cm-5, LPEC-2 h: 324 +/- 47 dynes * sec * m-5, EC-2 h: 507 +/- 83 dynes * sec * cm-5). Oxygenation after 2 h of ischemia and 30 min reperfusion was significantly better in the LPEC group compared to EC and controls (LPEC: 70 +/- 17 mmHg, EC: 44 +/- 3 mmHg). The wet/dry ratio was significantly lower in the two preserved groups compared to controls (LPEC-5 min: 5.7 +/- 0.7, EC-5 min: 5.8 +/- 1.2, controls-5 min: 7.5 +/- 1.8, LPEC-2 h: 6.7 +/- 0.4, EC: 6.9 +/- 0.4, controls-2 h: 7.3 +/- 0.4). CONCLUSIONS: We thus conclude that LPEC results in better oxygenation and lower PVR in this lung preservation model. A low potassium concentration in lung preservation solutions may help in reducing the incidence of early graft dysfunction following lung transplantation.

Imminent ocean acidification in the Arctic projected with the NCAR global coupled carbon cycle-climate model

Ocean acidification from the uptake of anthropogenic carbon is simulated for the industrial period and IPCC SRES emission scenarios A2 and B1 with a global coupled carbon cycle-climate model. Earlier studies identified seawater saturation state with respect to aragonite, a mineral phase of calcium carbonate, as a key variable governing impacts on corals and other shell-forming organisms. Globally in the A2 scenario, water saturated by more than 300%, considered suitable for coral growth, vanishes by 2070 AD (CO2≈630 ppm), and the ocean volume fraction occupied by saturated water decreases from 42% to 25% over this century. The largest simulated pH changes worldwide occur in Arctic surface waters, where hydrogen ion concentration increases by up to 185% (ΔpH=−0.45). Projected climate change amplifies the decrease in Arctic surface mean saturation and pH by more than 20%, mainly due to freshening and increased carbon uptake in response to sea ice retreat. Modeled saturation compares well with observation-based estimates along an Arctic transect and simulated changes have been corrected for remaining model-data differences in this region. Aragonite undersaturation in Arctic surface waters is projected to occur locally within a decade and to become more widespread as atmospheric CO2 continues to grow. The results imply that surface waters in the Arctic Ocean will become corrosive to aragonite, with potentially large implications for the marine ecosystem, if anthropogenic carbon emissions are not reduced and atmospheric CO2 not kept below 450 ppm.

Impact of volcanic stratospheric aerosols on diurnal temperature range in Europe over the past 200 years: Observations versus model simulations

We analyze the impact of stratospheric volcanic aerosols on the diurnal temperature range (DTR) over Europe using long-term subdaily station records. We compare the results with a 28-member ensemble of European Centre/Hamburg version 5.4 (ECHAM5.4) general circulation model simulations. Eight stratospheric volcanic eruptions during the instrumental period are investigated. Seasonal all- and clear-sky DTR anomalies are compared with contemporary (approximately 20 year) reference periods. Clear sky is used to eliminate cloud effects and better estimate the signal from the direct radiative forcing of the volcanic aerosols. We do not find a consistent effect of stratospheric aerosols on all-sky DTR. For clear skies, we find average DTR anomalies of −0.08°C (−0.13°C) in the observations (in the model), with the largest effect in the second winter after the eruption. Although the clear-sky DTR anomalies from different stations, volcanic eruptions, and seasons show heterogeneous signals in terms of order of magnitude and sign, the significantly negative DTR anomalies (e.g., after the Tambora eruption) are qualitatively consistent with other studies. Referencing with clear-sky DTR anomalies to the radiative forcing from stratospheric volcanic eruptions, we find the resulting sensitivity to be of the same order of magnitude as previously published estimates for tropospheric aerosols during the so-called “global dimming” period (i.e., 1950s to 1980s). Analyzing cloud cover changes after volcanic eruptions reveals an increase in clear-sky days in both data sets. Quantifying the impact of stratospheric volcanic eruptions on clear-sky DTR over Europe provides valuable information for the study of the radiative effect of stratospheric aerosols and for geo-engineering purposes.

A global historical ozone data set and prominent features of stratospheric variability prior to 1979

We present a vertically resolved zonal mean monthly mean global ozone data set spanning the period 1901 to 2007, called HISTOZ.1.0. It is based on a new approach that combines information from an ensemble of chemistry climate model (CCM) simulations with historical total column ozone information. The CCM simulations incorporate important external drivers of stratospheric chemistry and dynamics (in particular solar and volcanic effects, greenhouse gases and ozone depleting substances, sea surface temperatures, and the quasi-biennial oscillation). The historical total column ozone observations include ground-based measurements from the 1920s onward and satellite observations from 1970 to 1976. An off-line data assimilation approach is used to combine model simulations, observations, and information on the observation error. The period starting in 1979 was used for validation with existing ozone data sets and therefore only ground-based measurements were assimilated. Results demonstrate considerable skill from the CCM simulations alone. Assimilating observations provides additional skill for total column ozone. With respect to the vertical ozone distribution, assimilating observations increases on average the correlation with a reference data set, but does not decrease the mean squared error. Analyses of HISTOZ.1.0 with respect to the effects of El Niño–Southern Oscillation (ENSO) and of the 11 yr solar cycle on stratospheric ozone from 1934 to 1979 qualitatively confirm previous studies that focussed on the post-1979 period. The ENSO signature exhibits a much clearer imprint of a change in strength of the Brewer–Dobson circulation compared to the post-1979 period. The imprint of the 11 yr solar cycle is slightly weaker in the earlier period. Furthermore, the total column ozone increase from the 1950s to around 1970 at northern mid-latitudes is briefly discussed. Indications for contributions of a tropospheric ozone increase, greenhouse gases, and changes in atmospheric circulation are found. Finally, the paper points at several possible future improvements of HISTOZ.1.0.

Accounting for baseline differences and measurement error in the analysis of change over time

If change over time is compared in several groups, it is important to take into account baseline values so that the comparison is carried out under the same preconditions. As the observed baseline measurements are distorted by measurement error, it may not be sufficient to include them as covariate. By fitting a longitudinal mixed-effects model to all data including the baseline observations and subsequently calculating the expected change conditional on the underlying baseline value, a solution to this problem has been provided recently so that groups with the same baseline characteristics can be compared. In this article, we present an extended approach where a broader set of models can be used. Specifically, it is possible to include any desired set of interactions between the time variable and the other covariates, and also, time-dependent covariates can be included. Additionally, we extend the method to adjust for baseline measurement error of other time-varying covariates. We apply the methodology to data from the Swiss HIV Cohort Study to address the question if a joint infection with HIV-1 and hepatitis C virus leads to a slower increase of CD4 lymphocyte counts over time after the start of antiretroviral therapy.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis

The responses of carbon dioxide (CO2) and other climate variables to an emission pulse of CO2 into the atmosphere are often used to compute the Global Warming Potential (GWP) and Global Temperature change Potential (GTP), to characterize the response timescales of Earth System models, and to build reduced-form models. In this carbon cycle-climate model intercomparison project, which spans the full model hierarchy, we quantify responses to emission pulses of different magnitudes injected under different conditions. The CO2 response shows the known rapid decline in the first few decades followed by a millennium-scale tail. For a 100 Gt-C emission pulse added to a constant CO2 concentration of 389 ppm, 25 ± 9% is still found in the atmosphere after 1000 yr; the ocean has absorbed 59 ± 12% and the land the remainder (16 ± 14%). The response in global mean surface air temperature is an increase by 0.20 ± 0.12 °C within the first twenty years; thereafter and until year 1000, temperature decreases only slightly, whereas ocean heat content and sea level continue to rise. Our best estimate for the Absolute Global Warming Potential, given by the time-integrated response in CO2 at year 100 multiplied by its radiative efficiency, is 92.5 × 10−15 yr W m−2 per kg-CO2. This value very likely (5 to 95% confidence) lies within the range of (68 to 117) × 10−15 yr W m−2 per kg-CO2. Estimates for time-integrated response in CO2 published in the IPCC First, Second, and Fourth Assessment and our multi-model best estimate all agree within 15% during the first 100 yr. The integrated CO2 response, normalized by the pulse size, is lower for pre-industrial conditions, compared to present day, and lower for smaller pulses than larger pulses. In contrast, the response in temperature, sea level and ocean heat content is less sensitive to these choices. Although, choices in pulse size, background concentration, and model lead to uncertainties, the most important and subjective choice to determine AGWP of CO2 and GWP is the time horizon.