The Influence of Slow Blood Volume Waves on the Shape of the Dicrotic Complex

Recorded arterial blood volume pulses are often superimposed on slow rhythmic volume waves. It was noticed that in such recordings the dicrotic complex changes its shape as a function of the volume wave; on the ascending limb of the volume wave the dicrotic complex becomes more pronounced, whereas on the descending limb it becomes attenuated. In order to explain this observation a mathematical analysis of the recording was performed. The analysis suggests that the changes in the shape of the dicrotic complex can be explained by assuming a superposition of arterial wall extensions caused by volume changes due to 1) the downstroke of the volume pulse, 2) the dicrotic complex, and 3) the ascending (or descending) limb of the volume wave. Three functions simulating these three vascular events were electronically generated and superimposed. The result shows that the simulated dicrotic complex changes its shape in a similar manner as in the physiological recording, strengthening the proposed explanation. The origin of the dicrotic complex and of rhythmic waves in blood volume and blood pressure is still unclear and their interaction, therefore, is difficult to understand. The present paper offers a possible explanation for a case in which the shape of the dicrotic complex, appearing on a blood volume pulse, changes as a function of a rhythmic volume wave.     

The Motivating Impact of Informing Women Smokers of a Link Between Smoking and Cervical Cancer: The Role of Coherence.

This research assessed the role of having a coherent explanation of the link between smoking and cervical cancer in motivating women to stop smoking. In the 1st study, women were given a leaflet with either a detailed or a minimal explanation of the link or no leaflet. The leaflets were similarly effective at providing a coherent explanation. In a cross-sectional analysis, having a coherent explanation moderated the relationship between perceived vulnerability and intention: Higher perceived vulnerability to cervical cancer was associated with greater intention to quit smoking only amongst women with a more coherent explanation of the link between smoking and cervical cancer. This rinding was replicated in a 2nd study. These results are consistent with H. Leventhal et al.'s (1997) self-regulatory model, which suggests that motivation to change behavior depends not only on perceiving a threat but also on having a coherent model linking the behavior with the threat. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Memory bias in clinical anxiety.

Predictions from a cognitive schema model of anxiety were tested by comparing generally anxious patients and normal controls on their incidental recall of positive and negative, threatening and nonthreatening, self- and other-referenced words. There was no evidence of a self-referent recall bias favoring negative or threatening words in anxiety. Contrary to expectation, the results indicated relatively poorer memory for threatening material in anxious patients. We argue that the cognitive schema model could not adequately account for these and other recent research findings and suggest an alternative formulation of information-processing biases in anxiety. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Shift of the Arterial Pulse-Wave Foot on Recordings With a Rising or Falling Baseline

Recordings of arterial pulse waves often appear on a rising or falling baseline. This causes a shift in the position of the pulse-wave foot as compared to its position on the horizontal base-line. The magnitude of this shift is unknown. Workers therefore tend to disregard pulse-wave recordings with sloping baselines when measuring foot-to-foot pulse-wave velocities. A formula was derived that allows an estimation of the upper limit of the shift in the position of the pulse-wave foot to be tm = l000n/157 ms, where n = b/A, b is the slope of the baseline, and A is one-half the arterial pulse-wave amplitude. In the majority of recordings with sloping baselines, n < 0.5 and tm does therefore not exceed 3 ms. Compared to the measured foot-to-foot time intervals, tm is usually small enough to be disregarded. In cases where n is larger, the formula can be used to estimate the upper limit of the error caused by the sloping base-line.     

Evidence for active and passive urate transport in the rat proximal tubule

Studies utilizing the technique of simultaneous microperfusion of peritubular capillaries and tubular lumen of the proximal tubule of the rat were performed to determine if the absorption of urate was an active transport process and to determine the passive permeability coefficient for urate. When radioactive urate of equal specific activity and concentration was present in both perfusion solutions, the ratio of collected to initial concentrations of urate in the luminal perfusate (CO/CI) was 0.71 +/- 0.02. This gradient was higher than that predicted at equilibrium from the electrical potential difference determined in the in vitro perfused rabbit proximal tubule. The addition of para-chloromercuribenzoate (PCMB) to both solutions resulted in a significantly higher CO/CI of 0.90 +/- 0.02. This latter value is closer to the value predicted at electrochemical equilibrium. In separate studies, the unidirectional fluxes of urate were determined in the presence of PCMB. The calculated passive permeability coefficient averaged approximately 0.94 pmol . min-1 . mm-1 . mM-1 and was equal in both directions. These results indicate that in the rat proximal tubule urate absorption is an active transport process. In addition, there exists a passive permeation pathway for urate movement out of and into the proximal tubule.     

On Learning Conditional Random Fields for Stereo

Until recently, the lack of ground truth data has hindered the application of discriminative structured prediction techniques to the stereo problem. In this paper we use ground truth data sets that we have recently constructed to explore different model structures and parameter learning techniques. To estimate parameters in Markov random fields (MRFs) via maximum likelihood one usually needs to perform approximate probabilistic inference. Conditional random fields (CRFs) are discriminative versions of traditional MRFs. We explore a number of novel CRF model structures including a CRF for stereo matching with an explicit occlusion model. CRFs require expensive inference steps for each iteration of optimization and inference is particularly slow when there are many discrete states. We explore belief propagation, variational message passing and graph cuts as inference methods during learning and compare with learning via pseudolikelihood. To accelerate approximate inference we have developed a new method called sparse variational message passing which can reduce inference time by an order of magnitude with negligible loss in quality. Learning using sparse variational message passing improves upon previous approaches using graph cuts and allows efficient learning over large data sets when energy functions violate the constraints imposed by graph cuts.