Generalized theorems for nonlinear state space reconstruction

Takens' theorem (1981) shows how lagged variables of a single time series can be used as proxy variables to reconstruct an attractor for an underlying dynamic process. State space reconstruction (SSR) from single time series has been a powerful approach for the analysis of the complex, non-linear systems that appear ubiquitous in the natural and human world. The main shortcoming of these methods is the phenomenological nature of attractor reconstructions. Moreover, applied studies show that these single time series reconstructions can often be improved ad hoc by including multiple dynamically coupled time series in the reconstructions, to provide a more mechanistic model. Here we provide three analytical proofs that add to the growing literature to generalize Takens' work and that demonstrate how multiple time series can be used in attractor reconstructions. These expanded results (Takens' theorem is a special case) apply to a wide variety of natural systems having parallel time series observations for variables believed to be related to the same dynamic manifold. The potential information leverage provided by multiple embeddings created from different combinations of variables (and their lags) can pave the way for new applied techniques to exploit the time-limited, but parallel observations of natural systems, such as coupled ecological systems, geophysical systems, and financial systems. This paper aims to justify and help open this potential growth area for SSR applications in the natural sciences.     

脑源定位技术的精度评估及其在实际中的应用

脑源定位技术旨在通过头皮表面的脑电、脑磁信号来识别大脑内的神经活动源，是研究大脑皮层神经活动、认知过程和病理功能的基础。其毫秒级的时间分辨率可以有效弥补功能核磁共振在低时间分辨率方面的不足。然而，理论分析层面中逆问题的不适定性，以及实践操作层面上不同的记录方式、电极数量和头模型构建等过程带来的误差，给脑源定位的准确性带来极大挑战，也在一定程度上限制了脑源定位方法在神经科学和心理学研究以及临床诊断治疗中的实际应用。因此，理论分析和实践操作层面中的精度评估在脑源定位方法的实际使用中至关重要。针对以上问题，本文在对现有脑源定位方法介绍的基础上，着重分析了脑源定位技术的精度评估方法以及其在基础研究和临床诊断治疗中的实际应用。具体地，本文在理论分析中总结了基于空间分辨率、基于点扩散以及串扰函数的评估方法对于不同脑源定位方法中源的重叠程度和其他源对目标源的影响；在实践操作中介绍了记录方式、电极数量和密度、头部容积传导模型等因素对源定位精度的影响；进一步介绍了脑源定位技术在时频分析、连通性分析中的应用，以及其在临床中的应用，包括癫痫、注意缺陷与多动障碍等脑部疾病。     

Methodology of reconstruction and preliminary analysis of 1533 genealogies of parma valley population (centuries XVI–XX)

The reconstruction of the genealogies and the detailed knowledge of the biological and kinship relationship linking the past and the present members of a population and their succession in time, can be a precious source of information for those who want to reconstruct and analyze biological and cultural mechanisms by which human populations evolve and adapt to environmental changes.The methods used for the reconstruction of the genealogies are described and an analysis of the results obtained is presented in order to test the validity of the techniques employed on the basis of the reliability of the results.Finally, a discussion of some possible lines of research derived from analysis of the data are presented, given that the use of genealogies, far from being restricted to the genealogies themselves, can offer a valid contribution to the analysis of the structure and evolution of human populations.     

染色体三维结构重建方法研究

染色体三维结构重构问题是近年生物领域中基因组学的热点研究问题,是以二维交互频率数据为基础来预测其三维空间结构。最新相关实验表明染色质的三维空间结构对于基因表达、调控等方面都具有重要意义。而Hi-c数据能利用染色质交互信息形成二维接触矩阵重构出染色体三维结构。本综述以染色体三维结构重建方法为研究对象,通过对染色体三维结构重建方法进行比较分析,综述了目前基于Hi-c数据在染色体三维结构重建中的经典方法,系统介绍了染色体三维结构重建技术的发展脉络,以促进染色体三维结构重建的进一步研究。     

Endoscopically assisted reconstruction of male and female poland syndrome

Poland syndrome produces deformities of the breast and chest wall that can be highly disfiguring in both men and women. Incisions from traditional surgical approaches can be unsightly, especially if a muscle harvest is used as part of the reconstruction. Herein is described a case series in which minimally invasive techniques were used to reconstruct defects in male and female patients with Poland syndrome. When evaluating patients with Poland syndrome, one should consider using minimally invasive techniques as an alternative reconstructive option.     

降水量和温度对植被覆盖指数影响的空间非平稳性特征——以新疆伊犁河谷地区为例

多变量空间相关分析多基于时间序列数据,对数据时长与统计要求严格,空间非平稳性特征分析可以利用单期数据分析多变量之间的相关性。通过空间变系数回归模型分析了2006年和2011年的新疆伊犁地区降水量和温度对植被覆盖度指数影响的空间变化特征,利用局部线性地理加权回归(GWR)方法估计得到了回归系数曲面,揭示出变量间相互影响的空间异质性,同时利用线性回归最小二乘估计进行了对比。结果表明:(1)空间变系数回归模型可以用于变量间的空间相关分析;(2)局部线性GWR估计方法明显优于线性回归最小二乘估计;(3)拟合结果表明,伊犁地区降水量和温度对植被覆盖指数的影响具有显著的空间非平稳性特征;(4)模型估计误差是降水、气温之外的地形、地貌及人类活动等多种因素造成的,需进一步研究。方法可为具有空间非平稳性特征变量间空间相关性分析以及植被覆盖指数的空间模拟分布提供思路和方法。     

旅游型乡村生态空间演化与重构——以茶陵县卧龙村为例

旅游型乡村生态空间系统重构是乡村可持续发展的关键。以典型旅游型乡村茶陵县卧龙村为例,结合PRA方法（Participatory Rural Appraisl,又称参与性乡村评估）反演生态空间不同发展阶段并分析其演变特征,运用生态敏感性评价及最小阻力模型等定量方法系统分析生态空间构成要素,探索生态空间重构方法。研究表明：卧龙村生态空间由6个生态源地、15条生态廊道和22个生态节点构成,各类要素存在空间分布不均衡,空间结构不合理,生态功能不强等问题。按生态空间发展的四个阶段分析,其演变特征呈现出明显的规模递减性、时空差异性和功能复合性。可采取新增源地、调整廊道、提升节点等措施,构建以7个生态源地、14条生态廊道和19个生态节点为主的生态空间重构路径,对空间要素采取分类管控、生态空间结构优化及生态规划引导等措施。生态空间重构研究可为乡村生态保护和乡村振兴战略实施提供理论参考与案例借鉴。     

High resolution traction force microscopy based on experimental and computational advances

Cell adhesion and migration crucially depend on the transmission of actomyosin-generated forces through sites of focal adhesion to the extracellular matrix. Here we report experimental and computational advances in improving the resolution and reliability of traction force microscopy. First, we introduce the use of two differently colored nanobeads as fiducial markers in polyacrylamide gels and explain how the displacement field can be computationally extracted from the fluorescence data. Second, we present different improvements regarding standard methods for force reconstruction from the displacement field, which are the boundary element method, Fourier-transform traction cytometry, and traction reconstruction with point forces. Using extensive data simulation, we show that the spatial resolution of the boundary element method can be improved considerably by splitting the elastic field into near, intermediate, and far field. Fourier-transform traction cytometry requires considerably less computer time, but can achieve a comparable resolution only when combined with Wiener filtering or appropriate regularization schemes. Both methods tend to underestimate forces, especially at small adhesion sites. Traction reconstruction with point forces does not suffer from this limitation, but is only applicable with stationary and well-developed adhesion sites. Third, we combine these advances and for the first time reconstruct fibroblast traction with a spatial resolution of ∼1 μm.     

Automatic 4D Reconstruction of Patient-Specific Cardiac Mesh with 1-to-1 Vertex Correspondence from Segmented Contours Lines

We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial–temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities.     

Reconstructing the Diet of an Extinct Hominin Taxon: The Role of Extant Primate Models

Modern humans represent the only surviving species of an otherwise extinct clade of primates, the hominins. As the closest living relatives to extinct hominins, extant primates are an important source of comparative information for the reconstruction of the diets of extinct hominins. Methods such as comparative and functional morphology, finite element analysis, dental wear, dental topographic analysis, and stable isotope biogeochemistry must be validated and tested within extant populations before they can be applied to extinct taxa. Here we review how these methods have and might be used to reconstruct the diet of a particular extinct hominin, Paranthropus boisei, which has no extant analogue for its highly derived masticatory morphology. Our review emphasizes the potential and limitations of using extant primates as models for the reconstruction of extinct hominin diets. We encourage paleoanthropologists and those who study the feeding behaviors of extant primates to work together to investigate and validate methods for interpreting the diets of all extinct primates, including hominins.     

Dynamic 3D Scene Depth Reconstruction via Optical Flow Field Rectification

In this paper, we propose a depth propagation scheme based on optical flow field rectification towards more accurate depth reconstruction. In depth reconstruction, the occlusions and low-textural regions easily result in optical flow field errors, which lead ambiguous depth value or holes without depth in the obtained depth map. In this work, a scheme is proposed to improve the precision of depth propagation and the quality of depth reconstruction for dynamic scene. The proposed scheme first adaptively detects the occlusive or low-textural regions, and the obtained vectors in optical flow field are rectified properly. Subsequently, we process the occluded and ambiguous vectors for more precise depth propagation. We further leverage the boundary enhancement filters as a post-processing to sharpen the object boundaries in obtained depth maps for better quality. Quantitative evaluations show that the proposed scheme can reconstruct depth map with higher accuracy and better quality compared with the state-of-the-art methods.     

Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling

Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.     

SEBINI: Software Environment for BIological Network Inference

The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. It also allows the analysis within the same framework of experimental high-throughput expression data using the suite of (trained) inference methods; hence SEBINI should be useful to software developers wishing to evaluate, compare, refine or combine inference techniques, and to bioinformaticians analyzing experimental data. SEBINI provides a platform that aids in more accurate reconstruction of biological networks, with less effort, in less time. AVAILABILITY: A demonstration website is located at https://www.emsl.pnl.gov/NIT/NIT.html. The Java source code and PostgreSQL database schema are available freely for non-commercial use.     

Hierarchical regression models for dendroclimatic standardization and climate reconstruction

Tree-ring based paleoclimate reconstructions entail several sequential estimation or processing steps. Consequently, it can be difficult to isolate climatic from non-climatic variability in the raw ring width measurements, estimate the uncertainty associated with a reconstruction, and directly infer how specific techniques used to sequentially fit growth curves or to reconstruct climate influence the final estimates. This paper explores the use of hierarchical regression models to address these problems. The proposed models simultaneously model the entire reconstruction process in a way that is consistent with the existing step-by-step estimation framework, but allow for uncertainty estimation and propagation across steps, which can help determine how best to improve a candidate model. The utility of hierarchical models is tested for an example, the reconstruction of summertime temperatures in northern Sweden in a cross-validated framework relative to 1) a sequential process of growth curve fitting followed by chronology development, 3) an iterative, “signal-free” approach, and 2) a signal-free regional curve standardization (RCS-SF). Further, an exploration of different structures within the unifying hierarchical framework is provided to illustrate how one could easily test a variety of choices of model design. We focus on a subset of choices relevant to recent dendroclimatic studies using hierarchical methods and related to 1) data transformation, 2) the benefits of biological detrending and climate reconstruction in a single step 3) partial pooling of the age model across trees, 4) the homogeneity of variance across tree-ring residuals, 5) the structural form of the age model, and 6) the inclusion of autoregressive processes for the tree-ring residuals. The work described here represents part of a series of ongoing explorations of potential advances over current dendroclimatic reconstruction approaches and commonly implemented ways in which they have and are specifically implemented. The results show that hierarchical modeling appears to offer improved climate reconstructions over the standardization techniques explored in this exercise, substantially so for the non-RCS sequential and iterative methods.     

树木年轮在干扰历史重建中的应用

干扰是影响森林生态系统结构和功能的重要因子,重建森林群落干扰历史可以掌握干扰发生的机制和规律,对森林经营有重要意义。年轮记载了树木逐年的生长历史,利用年轮可以重建不同尺度上干扰的时空格局,具有重建历史长、定年准确和材料容易获得的优点,是森林动态历史研究不可替代的资料。干扰对树木个体的影响可以分为伤害干扰和生长干扰两种类型。不同类型的干扰的特点和对树木的生长影响不同所以重建方法也不同,对树干造成直接伤害的干扰可以利用树干的疤痕重建,如火灾和泥石流等;生长干扰对树木的生长势造成影响,可以通过识别年轮中的生长抑制和释放发生和持续的时间来确定干扰的时间和强度。但是对破坏性的干扰事件要通过群落中个体的建成时间高峰来判断。欧洲和北美地区利用树木年轮重建森林干扰历史的研究已经很广泛,主要包括火灾、虫灾、地质灾害和气象灾害等干扰类型,在中国利用年轮重建群落干扰的研究还处于起步阶段。本文探讨了应用树木年轮重建不同森林干扰事件的方法,总结了不同的重建方法和干扰重建取得的进展,并指出了以后研究的方向,为以后的干扰重建提供了参考。     

Three-dimensional reconstruction using an adaptive simultaneous algebraic reconstruction technique in electron tomography

Three-dimensional (3D) reconstruction of electron tomography (ET) has emerged as an important technique in analyzing structures of complex biological samples. However most of existing reconstruction methods are not suitable for extremely noisy and incomplete data conditions. We present an adaptive simultaneous algebraic reconstruction technique (ASART) in which a modified multilevel access scheme and an adaptive relaxation parameter adjustment method are developed to improve the quality of the reconstructed 3D structure. The reconstruction process is facilitated by using a column-sum substitution approach. This modified multilevel access scheme is adopted to arrange the order of projections so as to minimize the correlations between consecutive views within a limited angle range. In the adaptive relaxation parameter adjustment method, not only the weight matrix (as in the existing methods) but the gray levels of the pixels are employed to adjust the relaxation parameters so that the quality of the reconstruction is improved while the convergence process of the reconstruction is accelerated. In the column-sum substitution approach, the computation to obtain the reciprocal of the sum for the columns in each view is avoided so that the needed computations for each iteration can be reduced. Experimental results show that the proposed technique ASART is better based on objective quality measures than other methods, especially when data is noisy and limited in tilt angles. At the same time, the reconstruction by ASART outperforms that of simultaneous algebraic reconstruction technique (SART) in speed.     

Application of singular spectrum analysis to the smoothing of raw kinematic signals

Motion capture systems currently used in biomechanical analysis introduce systematic measurement errors that appear in the form of noise in recorded displacement signals. The noise is unacceptably amplified when differentiating displacements to obtain velocities and accelerations. To avoid this phenomenon, it is necessary to smooth the displacement signal prior to differentiation in order to eliminate the noise introduced by the experimental system. The use of singular spectrum analysis (SSA) is presented in this paper as an alternative to traditional digital filtering methods. SSA is a novel non-parametric technique based on principles of multivariate statistics. The original time series is decomposed into a number of additive time series, each of which can be easily identified as being part of the modulated signal, or as being part of the random noise. Several examples that demonstrate the superiority of this technique over other methods used in biomechanical analysis are presented in this paper.     

Unsupervised clustering of fMRI and MRI time series

Unsupervised clustering represents a powerful technique for self-organized segmentation of biomedical image time series data describing groups of pixels exhibiting similar properties of local signal dynamics. The theoretical background is presented in the beginning, followed by several medical applications demonstrating the flexibility and conceptual power of these techniques. These applications range from functional MRI data analysis to dynamic contrast-enhanced perfusion MRI and breast MRI. For fMRI, these methods can be employed to identify and separate time courses of interest, along with their associated spatial patterns. When applied to dynamic perfusion MRI, they identify groups of voxels associated with time courses that are clinically informative and straightforward to interpret. In breast MRI, a segmentation of the lesion is achieved and in addition a subclassification is obtained within the lesion with regard to regions characterized by different MRI signal time courses. In the present paper, we conclude that unsupervised clustering techniques provide a robust method for blind analysis of time series image data in the important and current field of functional and dynamic MRI.     

Towards zero training for brain-computer interfacing

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these 'experienced' BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed.