Abstract
A reflective fiber optic sensor based on multimode interference for the measurement of refractive index variations in glucose aqueous solutions is proposed. The sensor is fabricated by splicing a short section of coreless silica fiber to standard single mode fiber. The influence of the coreless fiber dimensions on the sensor performance is analyzed. By changing the sensor length, no significant impact is observed. However, the reduction of the sensing head diameter leads to a large improvement of the sensitivity. The smaller sensor, with a length of 5 mm and a diameter of 24 mu m, presents a maximum sensitivity of 1467.59 nm/RIU, for the refractive index range between 1.364 and 1.397 RIU. Taking into account the acquisition system, a maximum theoretical resolution of 6.8 x 10(-5) RIU is achieved.

Abstract
A fiber sensor based on a Fabry-Perot cavity is reported for measuring mixtures of water and glycerin. The sensor is fabricated by producing an air bubble near the end face of a multimode fiber section, and reshaping the tip in order to produce a thin silica diaphragm. It is observed that there is dependence between diaphragm dimensions and the structure sensitivity. The sensor with a 20 mu m thick diaphragm presents a sensitivity of 7.81 pm/wt.% regarding the variation of water mass fraction in glycerin. With this sensing head, an experimental resolution of 2.5 wt.% is estimated. By converting the mass fraction into refractive index variations, a maximum sensitivity of 5.49 nm/RIU is obtained. Moreover, given the low-temperature sensitivity (1.6 pm/degrees C), the proposed cavity should be adequate to perform temperature independent measurements. The purity degree of glycerin is one of the most important parameters to be determined in applications such as in pharmaceutical or cosmetic area. The proposed sensor can be an alternative to the previously developed ones.

Abstract
Linear parameter varying models (LPV) have proven to be effective to describe non-linearities and time-varying behaviors. In this work, a new non-parametric estimation algorithm for state-space LPV models based on support vector machines is presented. This technique allows the functional dependence between the model coefficients and the scheduling signal to be "learned" from the input and output data. The proposed algorithm is formulated in the context of instrumental (IV) estimators, in order to obtain consistent estimates for general noise conditions. The method is based on a canonical state space representation and admits a predictor form that has shown to be suitable for system identification, as it leads to a convenient regression form. In addition, this predictor has an inherent filtering feature. In the context of vector support machines, such filtering mechanism leads to two-dimensional data processing, which can be used to decrease the variance of estimates due to noisy data. The performance of the proposed approach is evaluated from simulated data subject to different noise scenarios. The technique was able to reduce the error due to the variance of the estimator in most of the analyzed scenarios.

Abstract
Background: Patients with single ventricle physiology and Fontan circulation are at increased risk for late complications and reduced survival. The aim of the study was to investigate the correlation between ventricular geometry and systolic regional function in different underlying anatomic conditions in adolescent and adult Fontan-palliated patients. Method: In a retrospective cross-sectional study, we measured 2D strain, ventricular diameters, ventricular volumes, ejection fraction (EF), global and segmental wall stress, and sphericity index. The same analyses were performed in 99 age- and gender-matched healthy individuals. Results: One hundred and one patients were included at a mean age of 21 (range 14–59) years. In comparison with healthy subjects, patients with Fontan circulation displayed larger ventricular volumes (153 ± 78 mL vs 116 ± 38 mL P < 0.05), reduced EF (43% ± 15% vs 55% ± 8% P < 0.05), reduced longitudinal (-13% ± 6% vs -21% ± 4% P < 0.05) and circumferential strain values (-15% ± 7% vs -22% ± 4% P < 0.05). Functionally single ventricles were more spherical (ratio of longitudinal to short-axis diameters 1.3 ± 0.3 vs 1.7 ± 0.2 P < 0.05). Circumferential strain correlated well with global wall stress and the degree of sphericity (R 2  = 0.320), while segmental strain did not correlate with segmental wall stress. The percentage of segments with akinesia was relatively high (16 ± 16% vs 0 ± 0% P < 0.05) indicating reduced segmental contractile function. Conclusion: Functionally single ventricles after Fontan palliation have significantly reduced systolic regional and global function with a high intersegmental inhomogeneity. The underlying pathological mechanisms might be multifactorial, including ventricular geometry, sphericity, and regional contractile properties. Future studies are needed to investigate the role of ventricular geometry for clinical performance and outcome. © 2018 Wiley Periodicals, Inc.

Abstract
The choroid is the middle layer of the eye globe located between the retina and the sclera. It is proven that choroidal thickness is a sign of multiple eye diseases. Optical Coherence Tomography (OCT) is an imaging technique that allows the visualization of tomographic images of near surface tissues like those in the eye globe. The automatic calculation of the choroidal thickness reduces the subjectivity of manual image analysis as well as the time of large scale measurements. In this paper, a method for the automatic estimation of the choroidal thickness from OCT images is presented. The pre-processing of the images is focused on noise reduction, shadow removal and contrast adjustment. The inner and outer boundaries of the choroid are delineated sequentially, resorting to a minimum path algorithm supported by new dedicated cost matrices. The choroidal thickness is given by the distance between the two boundaries. The data are then interpolated and mapped to an infrared image of the eye fundus. The method was evaluated by calculating the error as the distance from the automatically estimated boundaries to the boundaries delineated by an ophthalmologist. The error of the automatic segmentation was low and comparable to the differences between manual segmentations from different ophthalmologists.

Abstract
Quantitative assessment of mitral valve (MV) morphology is important for diagnosing MV pathology and for planning of reparative procedures. Although this is typically done using 3D transesophageal echocardiography (TEE), recent advances in the spatiotemporal resolution of 3D transthoracic echocardiography (TTE) have enabled the use of this more patient friendly modality. However, manual data analysis is time consuming and operator dependent. In this study, a fully automatic method for MV segmentation and tracking in 3D TTE is proposed and validated. The proposed framework takes advantage of a previously proposed left ventricle (LV) segmentation framework to localize the MV and performs segmentation based on the B-spline Explicit Active Surfaces (BEAS) framework. The orientation of the MV is obtained and the MV surface is cropped to the mitral annulus (MA) and divided into posterior and anterior leaflets. The segmented MV at end diastole (ED) is propagated to end systole (ES) using localized anatomical affine optical flow (lAAOF). Because the orientation and leaflet division is known, relevant clinical parameters can then be extracted from the mesh at any time point. The proposed framework shows excellent segmentation results with a mean absolute distance (MAD) and Hausdorff distance (HD) of 1.19 +/- 0.25 mm and 5.79 +/- 1.25 mm at ED and 1.39 +/- 0.32 mm and 6.70 +/- 1.97 mm at ES against manual analysis. In conclusion, an automatic method for MV segmentation is proposed which could provide valuable clinical information in a more patient-friendly manner.