Notes

Manual lymphatic system water drainage and also multilayer data compresion treatments regarding vulvar swelling: a case sequence.
The utility of ultrasound imaging and therapy with microbubbles may be greatly enhanced by determining their impulse-response dynamics as a function of size and composition. Prior methods for microbubble characterization utilizing high-speed cameras, acoustic transducers and laser-based techniques typically scan a limited frequency range. Here, we report on the use of a novel photoacoustic technique to measure the impulse response of single microbubbles. Individual microbubbles are driven with a broadband photoacoustic wave generated by a nanosecond-pulse laser illuminating an optical absorber. The resulting microbubble oscillations were detected by following transmission of a second laser as it passes twice through the microbubble. The system could even resolve oscillations resulting from a single-shot. As a proof-of-concept study, the size-dependent, linear impulse response of lipid-coated microbubbles was characterized using this technique. This unique method of microbubble characterization with exceptional spatiotemporal resolution opens new avenues for capturing and analyzing microbubble system dynamics.Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depend on the spatial distribution of intraoperative constraints provided to registration with relation to patient-specific organ geometry. Predictive linear and bivariate gamma models are fit and cross-validated using an existing dataset of 6291 simulated registration examples, plus 699 novel simulated registrations withheld for independent validation. Average uncertainty and average proportion of TRE remaining after elastic registration are strongly correlated ( r = 0.78 ), with mean absolute difference in predicted TRE equivalent to 0.9 ± 0.6 mm (cross-validation) and 0.9 ± 0.5 mm (independent validation). Spatial uncertainty maps also permit localized TRE estimates accurate to an equivalent of 3.0 ± 3.1 mm (cross-validation) and 1.6 ± 1.2 mm (independent validation). Additional clinical evaluation of vascular features yields localized TRE estimates accurate to 3.4 ± 3.2 mm. This work formalizes a lower bound for the inherent uncertainty of nonrigid elastic registrations given coverage of intraoperative data constraints, and demonstrates a relation to TRE that can be predictively leveraged to inform data collection and provide a measure of registration confidence for elastic methods.Multi-material decomposition (MMD) decomposes CT images into basis material images, and is a promising technique in clinical diagnostic CT to identify material compositions within the human body. MMD could be implemented on measurements obtained from spectral CT protocol, although spectral CT data acquisition is not readily available in most clinical environments. MMD methods using single energy CT (SECT), broadly applied in radiological departments of most hospitals, have been proposed in the literature while challenged by the inferior decomposition accuracy and the limited number of material bases due to the constrained material information in the SECT measurement. In this paper, we propose an image-domain SECT MMD method using material sparsity as an assistance under the condition that each voxel of the CT image contains at most two different elemental materials. L0 norm represents the material sparsity constraint (MSC) and is integrated into the decomposition objective function with a least-square data fidelity term, total variation term, and a sum-to-one constraint of material volume fractions. An accelerated primal-dual (APD) algorithm with line-search scheme is applied to solve the problem. The pixelwise direct inversion method with the two-material assumption (TMA) is applied to estimate the initials. We validate the proposed method on phantom and patient data. Compared with the TMA method, the proposed MSC method increases the volume fraction accuracy (VFA) from 92.0% to 98.5% in the phantom study. In the patient study, the calcification area can be clearly visualized in the virtual non-contrast image generated by the proposed method, and has a similar shape to that in the ground-truth contrast-free CT image. The high decomposition image quality from the proposed method substantially facilitates the SECT-based MMD clinical applications.Heterogeneous Face Recognition (HFR) refers to matching cross-domain faces and plays a crucial role in public security. Nevertheless, HFR is confronted with challenges from large domain discrepancy and insufficient heterogeneous data. In this paper, we formulate HFR as a dual generation problem, and tackle it via a novel Dual Variational Generation (DVG-Face) framework. Specifically, a dual variational generator is elaborately designed to learn the joint distribution of paired heterogeneous images. However, the small-scale paired heterogeneous training data may limit the identity diversity of sampling. In order to break through the limitation, we propose to integrate abundant identity information of large-scale visible data into the joint distribution. Furthermore, a pairwise identity preserving loss is imposed on the generated paired heterogeneous images to ensure their identity consistency. As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises. The identity consistency and identity diversity properties allow us to employ these generated images to train the HFR network via a contrastive learning mechanism, yielding both domain-invariant and discriminative embedding features. Concretely, the generated paired heterogeneous images are regarded as positive pairs, and the images obtained from different samplings are considered as negative pairs. Our method achieves superior performances over state-of-the-art methods on seven challenging databases belonging to five HFR tasks, including NIR-VIS, Sketch-Photo, Profile-Frontal Photo, Thermal-VIS, and ID-Camera.Image and sentence matching has attracted much attention recently, and many effective methods have been proposed to deal with it. But even the current state-of-the-arts still cannot well associate those challenging pairs of images and sentences containing few-shot content in their regions and words. In fact, such a few-shot matching problem is seldom studied and has become a bottleneck for further performance improvement in real-world applications. In this work, we formulate this challenging problem as few-shot image and sentence matching, and accordingly propose an Aligned Cross-Modal Memory (ACMM) model to deal with it. The model can not only softly align few-shot regions and words in a weakly-supervised manner, but also persistently store and update cross-modal prototypical representations of few-shot classes as references, without using any groundtruth region-word correspondence. The model can also adaptively balance the relative importance between few-shot and common content in the image and sentence, which leads to better measurement of overall similarity. We perform extensive experiments in terms of both few-shot and conventional image and sentence matching, and demonstrate the effectiveness of the proposed model by achieving the state-of-the-art results on two public benchmark datasets.
Purposes of this work were i) to develop an in silico model of tumor response to radiotherapy, ii) to perform an exhaustive sensitivity analysis in order to iii) propose a simplified version and iv) to predict biochemical recurrence with both the comprehensive and the reduced model.

A multiscale computational model of tumor response to radiotherapy was developed. It integrated the following radiobiological mechanisms oxygenation, including hypoxic death; division of tumor cells; VEGF diffusion driving angiogenesis; division of healthy cells and oxygen-dependent response to irradiation, considering, cycle arrest and mitotic catastrophe. A thorough sensitivity analysis using the Morris screening method was performed on 21 prostate computational tissues. Tumor control probability (TCP) curves of the comprehensive model and 15 reduced versions were compared. Logistic regression was performed to predict biochemical recurrence after radiotherapy on 76 localized prostate cancer patients using an output of the comprehensive and the reduced models.

No significant difference was found between the TCP curves of the comprehensive and a simplified version which only considered oxygenation, division of tumor cells and their response to irradiation. Biochemical recurrence predictions using the comprehensive and the reduced models improved those made from pre-treatment imaging parameters (AUC = 0.81 ± 0.02 and 0.82 ± 0.02 vs. 0.75 ± 0.03, respectively).

A reduced model of tumor response to radiotherapy able to predict biochemical recurrence in prostate cancer was obtained.

This reduced model may be used in the future to optimize personalized fractionation schedules.
This reduced model may be used in the future to optimize personalized fractionation schedules.AbstractThis study evaluated the contribution of physiological data collected during laboratory testing in predicting race performances of trained junior middle-distance track (TK) and cross-country (XC) athletes. Participants performed a submaximal incremental ramp test, followed by an incremental test to exhaustion in a laboratory, with the results used to predict either 800 m TK, 1500 m TK or 4000-6000 m XC race performance. Twenty-eight participants (male (M), 15; female (F), 13) were analysed (age = 17 ± 2 years, height = 1.72 ± 0.08 m, body mass = 58.9 ± 8.9 kg). Performance times (mins) for 800 m were M, 156.55 ± 005.55 and F, 214.21 ± 003.89; 1500 m M, 351.98 ± 007.35 and F 436.71 ± 016.58; XC M (4900 ± 741 m), 1600 ± 0153; F (4628 ± 670 m), 1741 ± 0209. GSK1210151A research buy Stepwise regression analysis indicated significant contributions of speed at V̇O2max (V̇O2max), and heart rate maximum (HRmax) to the prediction of 800 m TK (F(2,15) = 22.51, p  less then  0.001, adjusted R2 = 0.72), V̇O2max for 1500 m TK (F(1,13) = 36.65, p  less then  0.001, adjusted R2 = 0.72) and V̇O2max, allometrically scaled to body mass and speed at lactate threshold (sLT) for XC (F(2,17) = 25.1, p  less then  0.001, adjusted R2 = 0.72). Laboratory-based physiological measures can explain 72% of the variance in junior TK and XC events, although factors that explain performance alter depending on the race distance and tactics. The factors determining performance in TK and XC events are not interchangeable.Peritraumatic emotions are implicated in the elevated health risks associated with interpersonal trauma, but they have not been widely studied in the context of intimate partner abuse (IPA). To address this, community women with divorce histories completed IPA measures, along with an interview to assess posttraumatic stress symptoms and both DSM-IV A2 emotions (yes/no) and other emotions (free response) experienced during worst incidents of IPA. Anxiety/fright, helplessness, and horror were highly prevalent. Lexical analysis of the words women used to describe their other emotions revealed that anger and shame were the most prevalent, followed by dissociation and sadness. As predicted, chronicity of direct assault and frequency of verbal/emotional abuse showed significant, positive correlations with peritraumatic dissociation, and peritraumatic shame showed significant, positive correlations with current symptoms of effortful avoidance. Also, a negative correlation between frequency of dominance/isolation abuse - an indicator of coercive control - and peritraumatic anger approached statistical significance.
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