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Behçet's disease (BD) is a rare condition that is seldom associated with hematological malignancies. In this case report, we present the unique case of a 7‐year‐old girl diagnosed with juvenile myelomonocytic leukemia (JMML) and intestinal BD. The patient received allogeneic hematopoietic stem cell transplantation (allo‐HSCT), which resulted in complete remission of both JMML and BD. Our findings suggest that allo‐HSCT may be a feasible treatment option for JMML patients with coexisting BD, and holds promise for achieving remission of both illnesses. However, further clinical investigations are needed to validate these findings. [ABSTRACT FROM AUTHOR]

Radio Tomographic Imaging (RTI) is a low‐cost computational imaging method realised by the Radio Frequency (RF) signal sensing. The target‐induced shadowing effect in the RF sensing network is reconstructed as a probability image to estimate the target's position. Then, the RTI‐based Device‐free Localization (DFL) is becoming a promising research topic in the Location‐based Services applications by the Internet of Things (IoT). However, the multipath interference in the RF sensing network often induces the imaging degradation and decreases the DFL accuracy. To deal with the multipath‐induced imaging degradation, considering that the target's shadowing occupies a small spatial range in the RF network and expresses some spatial structure, this article explores the spatial correlation in the target's shadowing. Then, a new RTI reconstruction method based on the Structured Sparse Bayesian Learning is proposed to model the spatial correlation implied in the sparse target's shadowing image. Further, the localisation experiments in actual scenes are conducted to validate the utilisation of the spatial correlation in target's shadowing is able to improve the imaging quality of the RTI system by enhancing the robustness towards the multipath‐induced imaging degradation. [ABSTRACT FROM AUTHOR]

The thermal degradation of polylactide (PLA) during melt processing may lead to poor product properties. In this study, a unique Raman system was applied to explore the thermal degradation kinetics of molten PLA under five different temperatures and three atmospheres. It was found that the intensity of specific Raman bands correlated with the concentration of functional groups in the PLA during the degradation process. Kinetic models for thermal degradation were established based on the intensity of the characteristic band of C‐COO. Kinetic parameters determined by kinetic models quantitatively revealed the effect of temperature and atmosphere on thermal degradation. Furthermore, time sweep rheological tests using the same degradation conditions were performed for verification, and the results corroborated the spectral analyses. This investigation demonstrates the feasibility of Raman spectroscopy for thermal degradation kinetic analysis. [ABSTRACT FROM AUTHOR]

This study concerns the issue of jointly enhancing noise robustness and promoting signal sparsity in Sparse Bayesian Learning (SBL), which aims at addressing the performance deficiency of sparse signal recovery due to uninformative data with low signal‐to‐noise ratios. In particular, the authors propose a hierarchical prior noise model with a signal‐dependent parametrisation and incorporate it into developing the robust SBL algorithms for sparse signal recovery. The main contribution of the proposed approach is twofold. The first is the new consideration of noise‐robustness enhancement in building SBL algorithms, which devotes to noise awareness in counteracting outliers in measurements. Specifically, the idea of signal‐sparsity enforcing is extended to build a Least Absolute Deviation like loss criterion with the proposed hierarchical prior model of measurement noise. The second is the novelty of using the signal‐dependent parametrisation in the proposed noise model. Indeed, the signal‐dependent mechanism plays an indispensable role in producing the reliable noise parameter estimation jointly with updating signal model parameters under the fast SBL framework. In addition to numerical simulation studies, the real‐life application of radio tomographic imaging is presented to validate the proposed approach. [ABSTRACT FROM AUTHOR]

This study focuses on the issue of sparse signal recovery with sparse Bayesian learning in the context of a heterogeneous noise model, called by the heterogeneous Bayesian compressive sensing. The main contribution is to exploit the capability of noise variance learning in performance improvement and applicability enhancement. Experimental results on synthetic and real‐world data demonstrate that heterogeneous Bayesian compressive sensing has superior performance in terms of accuracy and sparsity for both homogeneous and heterogeneous noise scenarios. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate apparent diffusion coefficient (ADC) value, metabolic ratio ((Cho + Cr)/Cit) and the combination of the two in identifying prostate malignant regions. Materials and Methods: Fifty-six consecutive patients with prostate biopsy results were retrospectively recruited in this study. Transrectal ultrasound-guided (TRUS) systemic prostate biopsies were used as a standard of reference. Mean ADC value and mean metabolic ratio (MMR) were calculated within each benign sextant region or malignant region. The efficiency of these two indices in prostate cancer (PCa) diagnosis is estimated in Fisher linear discriminant analysis (FLDA). The area under the receiver operating characteristic (ROC) curve was used to evaluate the distinguishing capacity of mean ADC, MMR, and the combination of the two in differentiating between noncancerous and cancerous cases. Results: There were significant differences for mean ADC value and MMR between malignant and benign regions. Weights of mean ADC value obtained by FLDA were much higher than those of MMR. In differentiating malignant regions, both ADC alone and combined ADC and metabolic ratio performed significantly better than MMR alone. However, accuracy improvements were not significant by using combined ADC and MMR than ADC alone. Conclusion: DWI is more efficient than MR spectroscopic (MRS) in the detection of PCa in this study. Combined ADC and MMR performed significantly better than MMR alone in distinguishing malignant from benign region in prostate peripheral zone. J. Magn. Reson. Imaging 2013;37:194-200. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose To explore the potential of computerized characterization of prostate MR images by extracting the fractal features of texture and intensity distributions as indices in the differential diagnosis of prostate cancer. Materials and Methods MR T2-weighted images (T2WI) of 55 patients with pathologic results detected by ultrasound guided biopsy were collected and then divided in two groups, 27 with prostate cancer (PCa) and 28 with no histological abnormality. Texture fractal dimension (TFD) and histogram fractal dimension (HFD) were calculated to analyze complexity features of regions of Interest (ROIs) selected from the peripheral zone. Two-sample t-tests were performed to evaluate group differences for both parameters. Receiver operating characteristic (ROC) analysis was used to estimate the performance of TFD and HFD for discriminating PCa. Results Significant differences were found in both TFD and HFD between the two patient groups. The areas under the ROC curves of TFD and HFD were 0.691 and 0.966, respectively, in distinguishing prostatic carcinoma from normal peripheral zone. As characterized by the fractal indices, cancerous prostatic tissue exhibited smoother texture and lower variation in intensity distribution than normal prostatic tissue. Conclusion The study suggests that TFD and HFD depict the changes in texture and intensity distribution associated with prostate cancer on T2WI. Both TFD and HFDprovide promising quantitative indices for cancer identification. HFD performs better than TFD offering a more robust MR-based indicator in the diagnosis of prostatic carcinoma. J. Magn. Reson. Imaging 2009;30:161-168. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Current analysis of diffusion tensor imaging (DTI) is based mostly on a region of interest (ROI) in an image dataset, which is specified by users. This method is not always reliable, however, because of the uncertainty of manual specification. We introduce an improved fiber-based scheme rather than an ROI-based analysis to study in DTI datasets of 31 normal subjects the asymmetry of the cingulum, which is one of the most prominent white matter fiber tracts of the limbic system. The present method can automatically extract the quantitative anisotropy properties along the cingulum bundles from tractography. Moreover, statistical analysis was carried out after anatomic correspondence specific to the cingulum across subjects was established, rather than the traditional whole-brain registration. The main merit of our method compared to existing counterparts is that to find such anatomic correspondence in cingulum, a scale-invariant parameterization method by arc-angle was proposed. It can give a continuous and exact description on any segment of cingulum. More interestingly, a significant left-greater-than-right asymmetry pattern was obtained in most segments of cingulum bundle (−50-25 degrees), except in the most posterior portion of cingulum (25-50 degrees). Hum Brain Mapp 24:92-98, 2005. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

In this Letter, the authors propose a novel framework based on block sparse Bayesian learning (bSBL) for exploiting the tree structure on wavelet coefficients in the process of recovering signals. A Boolean matrix is designed to transfer the tree structure of wavelet coefficients to a non‐overlapped block structure. In this block‐structured sparse model, the bSBL‐based algorithm is used to learn the intra‐block correlations and to derive the updating rule of model parameters. Experimental results show that for both 1D and 2D signals their proposed algorithm has superior performances compared with other model‐based compressive sensing algorithms. [ABSTRACT FROM AUTHOR]