Parametric estimation of pulse arrival time: a robust approach to pulse wave velocity

Pulse wave velocity (PWV) is a surrogate of arterial stiffness and represents a non-invasive marker of cardiovascular risk. The non-invasive measurement of PWV requires tracking the arrival time of pressure pulses recorded in vivo, commonly referred to as pulse arrival time (PAT). In the state of the art, PAT is estimated by identifying a characteristic point of the pressure pulse waveform. This paper demonstrates that for ambulatory scenarios, where signal-to-noise ratios are below 10 dB, the performance in terms of repeatability of PAT measurements through characteristic points identification degrades drastically. Hence, we introduce a novel family of PAT estimators based on the parametric modeling of the anacrotic phase of a pressure pulse. In particular, we propose a parametric PAT estimator (TANH) that depicts high correlation with the Complior(R) characteristic point D1 (CC = 0.99), increases noise robustness and reduces by a five-fold factor the number of heartbeats required to obtain reliable PAT measurements.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Single fluoroscopic image based 2D/3D reconstruction of a scaled, patient-specific vertebral model

Purpose Accurate three-dimensional (3D) models of lumbar vertebrae can enable image-based 3D kinematic analysis. The common approach to derive 3D models is by direct segmentation of CT or MRI datasets. However, these have the disadvantages that they are expensive, timeconsuming and/or induce high-radiation doses to the patient. In this study, we present a technique to automatically reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image. Methods Our technique is based on a hybrid 2D/3D deformable registration strategy combining a landmark-to-ray registration with a statistical shape model-based 2D/3D reconstruction scheme. Fig. 1 shows different stages of the reconstruction process. Four cadaveric lumbar spine segments (total twelve lumbar vertebrae) were used to validate the technique. To evaluate the reconstruction accuracy, the surface models reconstructed from the lateral fluoroscopic images were compared to the associated ground truth data derived from a 3D CT-scan reconstruction technique. For each case, a surface-based matching was first used to recover the scale and the rigid transformation between the reconstructed surface model Results Our technique could successfully reconstruct 3D surface models of all twelve vertebrae. After recovering the scale and the rigid transformation between the reconstructed surface models and the ground truth models, the average error of the 2D/3D surface model reconstruction over the twelve lumbar vertebrae was found to be 1.0 mm. The errors of reconstructing surface models of all twelve vertebrae are shown in Fig. 2. It was found that the mean errors of the reconstructed surface models in comparison to their associated ground truths after iterative scaled rigid registrations ranged from 0.7 mm to 1.3 mm and the rootmean squared (RMS) errors ranged from 1.0 mm to 1.7 mm. The average mean reconstruction error was found to be 1.0 mm. Conclusion An accurate, scaled 3D reconstruction of the lumbar vertebra can be obtained from a single lateral fluoroscopic image using a statistical shape model based 2D/3D reconstruction technique. Future work will focus on applying the reconstructed model for 3D kinematic analysis of lumbar vertebrae, an extension of our previously-reported imagebased kinematic analysis. The developed method also has potential applications in surgical planning and navigation.

Design of a semi-implantable hearing device for direct acoustic cochlear stimulation

A new hearing therapy based on direct acoustic cochlear stimulation was developed for the treatment of severe to profound mixed hearing loss. The device efficacy was validated in an initial clinical trial with four patients. This semi-implantable investigational device consists of an externally worn audio processor, a percutaneous connector, and an implantable microactuator. The actuator is placed in the mastoid bone, right behind the external auditory canal. It generates vibrations that are directly coupled to the inner ear fluids and that, therefore, bypass the external and the middle ear. The system is able to provide an equivalent sound pressure level of 125 dB over the frequency range between 125 and 8000 Hz. The hermetically sealed actuator is designed to provide maximal output power by keeping its dimensions small enough to enable implantation. A network model is used to simulate the dynamic characteristics of the actuator to adjust its transfer function to the characteristics of the middle ear. The geometry of the different actuator components is optimized using finite-element modeling.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

A Bootstrap Test for the Probability of Ruin in the Compound Poisson Risk Process

In this article we propose a bootstrap test for the probability of ruin in the compound Poisson risk process. We adopt the P-value approach, which leads to a more complete assessment of the underlying risk than the probability of ruin alone. We provide second-order accurate P-values for this testing problem and consider both parametric and nonparametric estimators of the individual claim amount distribution. Simulation studies show that the suggested bootstrap P-values are very accurate and outperform their analogues based on the asymptotic normal approximation.

Indigenous knowledge related to climate variability and change: insights from droughts in semi-arid areas of former Makueni District, Kenya

This article describes the indigenous knowledge (IK) that agro-pastoralists in larger Makueni District, Kenya hold and how they use it to monitor, mitigate and adapt to drought. It examines ways of integrating IK into formal monitoring, how to enhance its value and acceptability. Data was collected through target interviews, group discussions and questionnaires covering 127 households in eight villages. Daily rainfall data from 1961–2003 were analysed. Results show that agro-pastoralists hold IK on indicators of rainfall variability; they believe in IK efficacy and they rely on them. Because agro-pastoralists consult additional sources, the authors interpret that IK forms a basic knowledge frame within which agro-pastoralists position and interpret meteorological forecasts. Only a few agro-pastoralists adapt their practices in anticipation of IK-based forecasts partly due to the conditioning of the actors to the high rainfall variability characteristic of the area and partly due to lack of resources. Non-drought factors such as poverty, inadequate resources and lack of preparedness expose agro-pastoralists to drought impacts and limit their adaptive capacity. These factors need to be understood and effectively addressed to increase agro-pastoralists’ decision options and the influence of IK-based forecasts on their decision-making patterns. The limited intergenerational transfer of IK currently threatens its existence in the longer term. One way to ensure its continued existence and use is to integrate IK into the education curriculum and to link IK with formal climate change research through the participation of the local people. However, further studies are necessary to address the reliability and validity of the identified IK indicators of climate variability and change.

Activation of the parieto-premotor network is associated with vivid motor imagery - a parametric FMRI study

The present study examined the neural basis of vivid motor imagery with parametrical functional magnetic resonance imaging. 22 participants performed motor imagery (MI) of six different right-hand movements that differed in terms of pointing accuracy needs and object involvement, i.e., either none, two big or two small squares had to be pointed at in alternation either with or without an object grasped with the fingers. After each imagery trial, they rated the perceived vividness of motor imagery on a 7-point scale. Results showed that increased perceived imagery vividness was parametrically associated with increasing neural activation within the left putamen, the left premotor cortex (PMC), the posterior parietal cortex of the left hemisphere, the left primary motor cortex, the left somatosensory cortex, and the left cerebellum. Within the right hemisphere, activation was found within the right cerebellum, the right putamen, and the right PMC. It is concluded that the perceived vividness of MI is parametrically associated with neural activity within sensorimotor areas. The results corroborate the hypothesis that MI is an outcome of neural computations based on movement representations located within motor areas.

Nanoparticle-based test measures overall propensity for calcification in serum

Vascular and soft tissue calcification contributes to cardiovascular morbidity and mortality in both the general population and CKD. Because calcium and phosphate serum concentrations are near supersaturation, the balance of inhibitors and promoters critically influences the development of calcification. An assay that measures the overall propensity for calcification to occur in serum may have clinical use. Here, we describe a nanoparticle-based assay that detects, in the presence of artificially elevated calcium and phosphate concentrations, the spontaneous transformation of spherical colloidal primary calciprotein particles (CPPs) to elongate crystalline secondary CPPs. We used characteristics of this transition to describe the intrinsic capacity of serum to inhibit the precipitation of calcium and phosphate. Using this assay, we found that both the sera of mice deficient in fetuin-A, a serum protein that inhibits calcification, and the sera of patients on hemodialysis have reduced intrinsic properties to inhibit calcification. In summary, we developed a nanoparticle-based test that measures the overall propensity for calcification in serum. The clinical use of the test requires evaluation in a prospective study.

Heritability, determinants and reference values of renal length: a family-based population study

OBJECTIVES: In this population-based study, reference values were generated for renal length, and the heritability and factors associated with kidney length were assessed. METHODS: Anthropometric parameters and renal ultrasound measurements were assessed in randomly selected nuclear families of European ancestry (Switzerland). The adjusted narrow sense heritability of kidney size parameters was estimated by maximum likelihood assuming multivariate normality after power transformation. Gender-specific reference centiles were generated for renal length according to body height in the subset of non-diabetic non-obese participants with normal renal function. RESULTS: We included 374 men and 419 women (mean ± SD, age 47 ± 18 and 48 ± 17 years, BMI 26.2 ± 4 and 24.5 ± 5 kg/m(2), respectively) from 205 families. Renal length was 11.4 ± 0.8 cm in men and 10.7 ± 0.8 cm in women; there was no difference between right and left renal length. Body height, weight and estimated glomerular filtration rate (eGFR) were positively associated with renal length, kidney function negatively, age quadratically, whereas gender and hypertension were not. The adjusted heritability estimates of renal length and volume were 47.3 ± 8.5 % and 45.5 ± 8.8 %, respectively (P < 0.001). CONCLUSION: The significant heritability of renal length and volume highlights the familial aggregation of this trait, independently of age and body size. Population-based references for renal length provide a useful guide for clinicians. KEY POINTS: • Renal length and volume are heritable traits, independent of age and size. • Based on a European population, gender-specific reference values/percentiles are provided for renal length. • Renal length correlates positively with body length and weight. • There was no difference between right and left renal lengths in this study. • This negates general teaching that the left kidney is larger and longer.

Hepatitis C coinfection is independently associated with decreased adherence to antiretroviral therapy in a population-based HIV cohort

OBJECTIVE: To characterize the impact of hepatitis C (HCV) serostatus on adherence to antiretroviral treatment (ART) among HIV-infected adults initiating ART. METHODS: The British Columbia HIV/AIDS Drug Treatment Program distributes, at no cost, all ART in this Canadian province. Eligible individuals used triple combination ART as their first HIV therapy and had documented HCV serology. Statistical analyses used parametric and non-parametric methods, including multivariate logistic regression. The primary outcome was > or = 95% adherence, defined as receiving > or = 95% of prescription refills during the first year of antiretroviral therapy. RESULTS: There were 1186 patients eligible for analysis, including 606 (51%) positive for HCV antibody and 580 (49%) who were negative. In adjusted analyses, adherence was independently associated with HCV seropositivity [adjusted odds ratio (AOR), 0.48; 95% confidence interval (CI), 0.23-0.97; P = 0.003], higher plasma albumin levels (AOR, 1.07; 95% CI, 1.01-1.12; P = 0.002) and male gender (AOR, 2.53; 95% CI, 1.04-6.15; P = 0.017), but not with injection drug use (IDU), age or other markers of liver injury. There was no evidence of an interaction between HCV and liver injury in adjusted analyses; comparing different strata of HCV and IDU confirmed that HCV was associated with poor adherence independent of IDU. CONCLUSIONS: HCV-coinfected individuals and those with lower albumin are less likely to be adherent to their ART.