Genetic Architecture and Molecular,Imaging and Prodromic Markers in Dementia with Lewy Bodies: State of the Art,Opportunities and Challenges

Dementia with Lewy bodies (DLB) is one of the most common causes of dementia and belongs to the group of α-synucleinopathies. Due to its clinical overlap with other neurodegenerative disorders and its high clinical heterogeneity, the clinical differential diagnosis of DLB from other similar disorders is often difficult and it is frequently underdiagnosed. Moreover, its genetic etiology has been studied only recently due to the unavailability of large cohorts with a certain diagnosis and shows genetic heterogeneity with a rare contribution of pathogenic mutations and relatively common risk factors. The rapid increase in the reported cases of DLB highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods proposed by the International DLB consortium rely on a list of criteria that comprises both clinical observations and the use of biomarkers. Herein, we summarize the up-to-now reported knowledge on the genetic architecture of DLB and discuss the use of prodromal biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.     

Multicomponent Bioluminescence Imaging with Naphthylamino Luciferins

Bioluminescent tools have been used for decades to image processes in complex tissues and preclinical models. However, few distinct probes are available to probe multicellular interactions. We and others are addressing this limitation by engineering new luciferases that can selectively process synthetic luciferin analogues. In this work, we explored naphthylamino luciferins as orthogonal bioluminescent probes. Three analogues were prepared using an optimized synthetic route. The luciferins were found to be robust emitters with native luciferase in vitro and in cellulo. We further screened the analogues against libraries of luciferase mutants to identify unique enzyme-substrate pairs. The new probes can be used in conjunction with existing bioluminescent tools for multi-component imaging.     

Detection of water ingress in composite sandwich structures: a magnetic resonance approach

Water ingress inside honeycomb sandwich panels during service has been linked to in-flight failure in some aircraft. There is an ongoing effort to develop nondestructive testing methods to detect the presence of water within the panels. Magnetic resonance (MR) represents an attractive approach in that it is sensitive to moisture. Using a unilateral MR sensor, testing can be applied directly to the surface of the panel. The viability of MR is demonstrated through laboratory imaging of both water within sandwich panels, as well as the adhesive itself. The detection of water using a one-sided handheld MR sensor is presented. It is shown that simple detection, as well as spatial localization of water within sandwich panels is possible.     

A NEW ELECTROMAGNETIC IMAGING SCHEME

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基于优进策略的遗传算法对重油热解模型参数的估计

针对常规遗传算法全局寻优效率偏低的弱点，提出了一种优进策略，用以改进常规遗传算法。该策略将从繁衍过程中获取进化信息，自适应地改进子代分布，适时引入确定性操作，以提高全局寻优性能。提出的相关技术包括维持种群的多样性、改进交叉算子、增加Powell寻优算子等。实例测试表明这种优进策略效果良好，并已成功地应用于重油热解三集总动力学复杂数学模型的非线性参数估计。     

GRIP管混凝土组合结构压弯构件非线性全过程分析

本文应用细观力学、经典层板理论、平截面假定、FRP约束混凝土本构模型及数值积分对GFRP管混凝土组合结构压弯构件进行了非线性全过程分析。通过理论计算发现GFRP管混凝土组合结构能有效地提高构件的承载力，且构件具有良好的变形能力。FRP壳体参与承载且有效地约束混凝土的变形，显著提高核心混凝土强度和变形能力。文中讨论了轴压比、长细比、径厚比、混凝土强度等级、纵筋配筋率、铺层角变化对极限水平荷载和极限水平位移的影响，从而得到了一些有益的结论。     

广义线性模型与非线性模型参数的统计推断

概述了广义线性模型(含半参数广义线性模型)与非线性模型研究的新进展,着重讨论了它们参数估计的统计推断。近年来,广义线性模型与非线性模型的研究发展迅速,有着广泛的应用前景。     

Non-existence of finite-time stable equilibria in fractional-order nonlinear systems

We note that in the literature it is often taken for granted that for fractional-order system without delays, whenever the system trajectory reaches the equilibrium, it will stay there. In fact, this is the well-known phenomenon of finite-time stability. However, in this paper, we will prove that for fractional-order nonlinear system described by Caputo’s or Riemann–Liouville’s definition, any equilibrium cannot be finite-time stable as long as the continuous solution corresponding to the initial value problem globally exists. In addition, some examples of stability analysis are revisited and linear Lyapunov function is used to prove the asymptotic stability of positive fractional-order nonlinear systems.     

Augment low-field intra-operative MRI with preoperative MRI using a hybrid non-rigid registration method

Background

Preoperatively acquired diffusion tensor image (DTI) and blood oxygen level dependent (BOLD) have been proved to be effective in providing more anatomical and functional information; however, the brain deformation induced by brain shift and tumor resection severely impairs the correspondence between the image space and the patient space in image-guided neurosurgery.

Method

To address the brain deformation, we developed a hybrid non-rigid registration method to register high-field preoperative MRI with low-field intra-operative MRI in order to recover the deformation induced by brain shift and tumor resection. The registered DTI and BOLD are fused with low-field intra-operative MRI for image-guided neurosurgery.

Results

The proposed hybrid registration method was evaluated by comparing the landmarks predicted by the hybrid registration method with the landmarks identified in the low-field intra-operative MRI for 10 patients. The prediction error of the hybrid method is 1.92 ± 0.54 mm, and the compensation accuracy is 74.3 ± 5.0%. Compared to the landmarks far from the resection region, those near the resection region demonstrated a higher compensation accuracy (P-value = .003) although these landmarks had larger initial displacements.

Conclusions

The proposed hybrid registration method is able to bring preoperatively acquired BOLD and DTI into the operating room and compensate for the deformation to augment low-field intra-operative MRI with rich anatomical and functional information.     

Analysis of sub-anatomic diffusion tensor imaging indices in white matter regions of Alzheimer with MMSE score

In this study, an attempt has been made to find the correlation between diffusion tensor imaging (DTI) indices of white matter (WM) regions and mini mental state examination (MMSE) score of Alzheimer patients. Diffusion weighted images are obtained from the ADNI database. These are preprocessed for eddy current correction and removal of non-brain tissue. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (DA) indices are computed over significant regions (Fornix left, Splenium of corpus callosum left, Splenium of corpus callosum right, bilateral genu of the corpus callosum) affected by Alzheimer disease (AD) pathology. The correlation is computed between diffusion indices of the significant regions and MMSE score using linear fit technique so as to find the relation between clinical parameters and the image features. Binary classification has been employed using support vector machine, decision stumps and simple logistic classifiers on the extracted DTI indices along with MMSE score to classify Alzheimer patients from healthy controls. It is observed that distinct values of DTI indices exist for the range of MMSE score. However, there is no strong correlation (Pearson's correlation coefficient ‘r’ varies from 0.0383 to −0.1924) between the MMSE score and the diffusion indices over the significant regions. Further, the performance evaluation of classifiers shows 94% accuracy using SVM in differentiating AD and control. In isolation clinical and image features can be used for prescreening and diagnosis of AD but no sub anatomic region correlation exist between these features set. The discussion on the correlation of diffusion indices of WM with MMSE score is presented in this study.