Vector-based Model Research Articles

Automated identification and characterization of parcels with OpenStreetMap and points of interest

Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.

ATrunk—An ALS-Based Trunk Detection Algorithm

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m2, while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59m average difference and 0.78m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

Watershed delineation using hydrographic features and a DEM in plain river network region

Watershed delineation is a required step when conducting any spatially distributed hydrological modelling. Automated approaches are often proposed to delineate a watershed based on a river network extracted from the digital elevation model (DEM) using the deterministic eight-neighbour (D8) method. However, a realistic river network cannot be derived from conventional DEM processing methods for a large flat area with a complex network of rivers, lakes, reservoirs, and polders, referred to as a plain river network region (PRNR). In this study, a new approach, which uses both hydrographic features and DEM, has been developed to address the problems of watershed delineation in PRNR. It extracts the river nodes and determines the flow directions of the river network based on a vector-based hydrographic feature data model. The river network, lakes, reservoirs, and polders are then used to modify the flow directions of grid cells determined by D8 approach. The watershed is eventually delineated into four types of catchments including lakes, reservoirs, polders, and overland catchments based on the flow direction matrix and the location of river nodes. Multiple flow directions of grid cells are represented using a multi-direction encoding method, and multiple outflows of catchments are also reflected in the topology of catchments. The proposed approach is applied to the western Taihu watershed in China. Comparisons between the results obtained from the D8 approach, the ‘stream burning’ approach, and those from the proposed approach clearly demonstrate an improvement of the new approach over the conventional approaches. This approach will benefit the development of distributed hydrological models in PRNR for the consideration of different types and multiple inlets and outlets of catchments. Copyright © 2015 John Wiley & Sons, Ltd.

The JAK/STAT3 pathway is a common inducer of astrocyte reactivity in Alzheimer's and Huntington's diseases.

Astrocyte reactivity is a hallmark of neurodegenerative diseases (ND), but its effects on disease outcomes remain highly debated. Elucidation of the signaling cascades inducing reactivity in astrocytes during ND would help characterize the function of these cells and identify novel molecular targets to modulate disease progression. The Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) pathway is associated with reactive astrocytes in models of acute injury, but it is unknown whether this pathway is directly responsible for astrocyte reactivity in progressive pathological conditions such as ND. In this study, we examined whether the JAK/STAT3 pathway promotes astrocyte reactivity in several animal models of ND. The JAK/STAT3 pathway was activated in reactive astrocytes in two transgenic mouse models of Alzheimer's disease and in a mouse and a nonhuman primate lentiviral vector-based model of Huntington's disease (HD). To determine whether this cascade was instrumental for astrocyte reactivity, we used a lentiviral vector that specifically targets astrocytes in vivo to overexpress the endogenous inhibitor of the JAK/STAT3 pathway [suppressor of cytokine signaling 3 (SOCS3)]. SOCS3 significantly inhibited this pathway in astrocytes, prevented astrocyte reactivity, and decreased microglial activation in models of both diseases. Inhibition of the JAK/STAT3 pathway within reactive astrocytes also increased the number of huntingtin aggregates, a neuropathological hallmark of HD, but did not influence neuronal death. Our data demonstrate that the JAK/STAT3 pathway is a common mediator of astrocyte reactivity that is highly conserved between disease states, species, and brain regions. This universal signaling cascade represents a potent target to study the role of reactive astrocytes in ND.

MapReduce-based Parallel Learning for Large-scale Remote Sensing Images

Machine learning applied to large-scale remote sensing images shows inadequacies in computational capability and storage space. To solve this problem, we propose a cloud computing-based scheme for learning remote sensing imag- es in a parallel manner: (1) a hull vector-based hybrid parallel support vector machine model (HHB-PSVM) is proposed. It can substantially improve the efficiency of training and prediction for the large-scale samples while guaranteeing classi- fication accuracy. (2) The MapReduce model is used to achieve parallel extraction of the classification features for the remote sensing images, and the MapReduce-based HHB-PSVM model (MapReduce-HHB-PSVM) is used to implement the training and prediction for large-scale samples. (3) MapReduce-HHB-PSVM is applied to land use classification, ena- bling various types of land use to be classified more efficiently by using fused hyperspectral images. Experimental results show that MapReduce-HHB-PSVM can substantially improve classification efficiency of large-scale remote sensing im- ages while guaranteeing classification accuracy, and it can promote the machine interpretation of ground objects infor- mation extracted from the large-scale remote sensing images to be conducted intelligently.

A vector-based Cellular Automata model for simulating urban land use change

Cellular Automata (CA) is widely used for the simulation of land use changes. This study applied a vector-based CA model to simulate land use change in order to minimize or eliminate the scale sensitivity in traditional raster-based CA model. The cells of vector-based CA model are presented according to the shapes and attributes of geographic entities, and the transition rules of vector-based CA model are improved by taking spatial variables of the study area into consideration. The vector-based CA model is applied to simulate land use changes in downtown of Qidong City, Jiangsu Province, China and its validation is confirmed by the methods of visual assessment and spatial accuracy. The simulation result of vector-based CA model reveals that nearly 75% of newly increased urban cells are located in the northwest and southwest parts of the study area from 2002 to 2007, which is in consistent with real land use map. In addition, the simulation results of the vector-based and raster-based CA models are compared to real land use data and their spatial accuracies are found to be 84.0% and 81.9%, respectively. In conclusion, results from this study indicate that the vector-based CA model is a practical and applicable method for the simulation of urbanization processes.

Graph- versus Vector-Based Analysis of a Consensus Protocol

The Paxos distributed consensus algorithm is a challenging case-study for standard, vector-based model checking techniques. Due to asynchronous communication, exhaustive analysis may generate very large state spaces already for small model instances. In this paper, we show the advantages of graph transformation as an alternative modelling technique. We model Paxos in a rich declarative transformation language, featuring (among other things) nested quantifiers, and we validate our model using the GROOVE model checker, a graph-based tool that exploits isomorphism as a natural way to prune the state space via symmetry reductions. We compare the results with those obtained by the standard model checker Spin on the basis of a vector-based encoding of the algorithm.

Hippocampal Surface Deformation Accuracy in T1‐Weighted Volumetric MRI Sequences in Subjects with Epilepsy

To demonstrate the accuracy across different acquisition and analysis methods, we evaluated the variability in hippocampal volumetric and surface displacement measurements resulting from two different MRI (magnetic resonance imaging) acquisition protocols. Nine epilepsy patients underwent two independent T1-weighted magnetization prepared spoiled gradient sequences during a single 3T MRI session. Using high-dimension mapping-large deformation (HDM-LD) segmentation, we calculated volumetric estimates and generated a vector-based 3-dimensional surface model of each subject's hippocampi, and evaluated volume and surface changes, the latter using a cluster-based noise estimation model. Mean hippocampal volumes and standard deviations for the left hippocampi were 2,750 (826) mm3 and 2,782 (859) mm3 (P = .13), and for the right hippocampi were 2,558 (750) mm3 and 2,547 (692) mm3 (P = .76), respectively for the MPR1 and MPR2 sequences. Average Dice coefficient comparing overlap for segmentations was 86%. There was no significant effect of MRI sequence on volume estimates and no significant hippocampal surface change between sequences. Statistical comparison of hippocampal volumes and statistically thresholded HDM-LD surfaces in TLE patients showed no differences between the segmentations obtained in the two MRI acquisition sequences. This validates the robustness across MRI sequences of the HDM-LD technique for estimating volume and surface changes in subjects with epilepsy.

The simulation of surface flow dynamics using a flow-path network model

This paper proposes a flow-path network (FPN) model to simulate complex surface flow based on a drainage-constrained triangulated irregular network (TIN). The TIN was constructed using critical points and drainage lines extracted from a digital terrain surface. Runoff generated on the surface was simplified as ‘water volumes’ at constrained random points that were then used as the starting points of flow paths (i.e. flow source points). The flow-path for each ‘water volume’ was constructed by tracing the direction of flow from the flow source point over the TIN surface to the stream system and then to the outlet of the watershed. The FPN was represented by a set of topologically defined one-dimensional line segments and nodes. Hydrologic variables, such as flow velocity and volume, were computed and integrated into the FPN to support dynamic surface flow simulation. A hypothetical rainfall event simulation on a hilly landscape showed that the FPN model was able to simulate the dynamics of surface flow over time. A real-world catchment test demonstrated that flow rates predicted by the FPN model agreed well with field observations. Overall, the FPN model proposed in this study provides a vector-based modeling framework for simulating surface flow dynamics. Further studies are required to enhance the simulations of individual hydrologic processes such as flow generation and overland and channel flows, which were much simplified in this study.

Dimensionality reduction-based dynamic reconstruction algorithm for electrical capacitance tomography

The image reconstruction task in electrical capacitance tomography (ECT) is an ill-posed problem, in which image reconstruction methods play a vital role in real applications. In this paper, a multiple measurement vector-based dimensionality reduction dynamic reconstruction model that simultaneously utilizes the ECT measurement information and the dynamic evolution information of a dynamic object is proposed. A robust sparse orthogonal projective non-negative matrix factorization (RSOPNMF) method is proposed for extracting the basis vectors from a set of snapshots, and the split Bregman iteration (SBI) algorithm is used to solve the RSOPNMF model. The original unknown variables are projected onto the subspaces spanned by a set of the basis vectors extracted by the RSOPNMF method from a set of snapshots to obtain a low-dimensional model, where the images are indirectly reconstructed by solving the corresponding low-dimensional coefficient vector, and thus the dimensionality of the unknown variables is reduced and the computational cost are decreased. Based on the multiple measurement vectors and the dimensionality reduction model, an objective functional that incorporates the ECT measurement information, the dynamic evolution information of a dynamic object, the spatial constraint and the temporal constraint is proposed, in which the unknown variables are solved in a batching pattern. An iterative scheme that integrates the beneficial advantages of the SBI method and the forward–backward splitting algorithm is developed for solving the proposed objective functional. Numerical simulation results validate the feasibility of the proposed algorithm.

Viral vector-based models of Parkinson's disease.

In order to study the molecular pathways of Parkinson's disease (PD) and to develop novel therapeutic strategies, scientific investigators rely on animal models. The identification of PD-associated genes has led to the development of genetic PD models as an alternative to toxin-based models. Viral vector-mediated loco-regional gene delivery provides an attractive way to express transgenes in the central nervous system. Several vector systems based on various viruses have been developed. In this chapter, we give an overview of the different viral vector systems used for targeting the CNS. Further, we describe the different viral vector-based PD models currently available based on overexpression strategies for autosomal dominant genes such as α-synuclein and LRRK2, and knockout or knockdown strategies for autosomal recessive genes, such as parkin, DJ-1, and PINK1. Models based on overexpression of α-synuclein are the most prevalent and extensively studied, and therefore the main focus of this chapter. Many efforts have been made to increase the expression levels of α-synuclein in the dopaminergic neurons. The best α-synuclein models currently available have been developed from a combined approach using newer AAV serotypes and optimized vector constructs, production, and purification methods. These third-generation α-synuclein models show improved face and predictive validity, and therefore offer the possibility to reliably test novel therapeutics.

RAAV2/7 vector-mediated overexpression of alpha-synuclein in mouse substantia nigra induces protein aggregation and progressive dose-dependent neurodegeneration

BackgroundAlpha-synuclein is a key protein implicated in the pathogenesis of Parkinson's disease (PD). It is the main component of the Lewy bodies, a cardinal neuropathological feature in the disease. In addition, whole locus multiplications and point mutations in the gene coding for alpha-synuclein lead to autosomal dominant monogenic PD. Over the past decade, research on PD has impelled the development of new animal models based on alpha-synuclein. In this context, transgenic mouse lines have failed to reproduce several hallmarks of PD, especially the strong and progressive dopaminergic neurodegeneration over time that occurs in the patients. In contrast, viral vector-based models in rats and non-human primates display prominent, although highly variable, nigral dopaminergic neuron loss. However, the few studies available on viral vector-mediated overexpression of alpha-synuclein in mice report a weak neurodegenerative process and no clear Lewy body-like pathology. To address this issue, we performed a comprehensive comparative study of alpha-synuclein overexpression by means of recombinant adeno-associated viral vectors serotype 2/7 (rAAV2/7) at different doses in adult mouse substantia nigra.ResultsWe noted a significant and dose-dependent alpha-synucleinopathy over time upon nigral viral vector-mediated alpha-synuclein overexpression. We obtained a strong, progressive and dose-dependent loss of dopaminergic neurons in the substantia nigra, reaching a maximum of 82% after 8 weeks. This effect correlated with a reduction in tyrosine hydroxylase immunoreactivity in the striatum. Moreover, behavioural analysis revealed significant motor impairments from 12 weeks after injection on. In addition, we detected the presence of alpha-synuclein-positive aggregates in the remaining surviving neurons. When comparing wild-type to mutant A53T alpha-synuclein at the same vector dose, both induced a similar degree of cell death. These data were supported by a biochemical analysis that showed a net increase in soluble and insoluble alpha-synuclein expression over time to the same extent for both alpha-synuclein variants.ConclusionsIn conclusion, our in vivo data provide evidence that strong and significant alpha-synuclein-induced neuropathology and progressive dopaminergic neurodegeneration can be achieved in mouse brain by means of rAAV2/7.

Adaptive graph walk-based similarity measures for parsed text

AbstractWe consider a dependency-parsed text corpus as an instance of a labeled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined with existing techniques of supervised learning that model local and global information about the graph walk process, can be used to derive a task-specific word similarity measure in this graph. We also propose and evaluate a new learning method in this framework, a path-constrained graph walk variant, in which the walk process is guided by high-level knowledge about meaningful edge sequences (paths) in the graph. Empirical evaluation on the tasks of named entity coordinate term extraction and general word synonym extraction show that this framework is preferable to, or competitive with, vector-based models when learning is applied, and using small to moderate size text corpora.

Vector-based model of elastic bonds for simulation of granular solids

A model (further referred to as the V model) for the simulation of granular solids, such as rocks, ceramics, concrete, nanocomposites, and agglomerates, composed of bonded particles (rigid bodies), is proposed. It is assumed that the bonds, usually representing some additional gluelike material connecting particles, cause both forces and torques acting on the particles. Vectors rigidly connected with the particles are used to describe the deformation of a single bond. The expression for potential energy of the bond and corresponding expressions for forces and torques are derived. Formulas connecting parameters of the model with longitudinal, shear, bending, and torsional stiffnesses of the bond are obtained. It is shown that the model makes it possible to describe any values of the bond stiffnesses exactly; that is, the model is applicable for the bonds with arbitrary length/thickness ratio. Two different calibration procedures depending on bond length/thickness ratio are proposed. It is shown that parameters of the model can be chosen so that under small deformations the bond is equivalent to either a Bernoulli-Euler beam or a Timoshenko beam or short cylinder connecting particles. Simple analytical expressions, relating parameters of the V model with geometrical and mechanical characteristics of the bond, are derived. Two simple examples of computer simulation of thin granular structures using the V model are given.

Silencing Relaxin-3 in Nucleus Incertus of Adult Rodents: A Viral Vector-based Approach to Investigate Neuropeptide Function

Relaxin-3, the most recently identified member of the relaxin peptide family, is produced by GABAergic projection neurons in the nucleus incertus (NI), in the pontine periventricular gray. Previous studies suggest relaxin-3 is a modulator of stress responses, metabolism, arousal and behavioural activation. Knockout mice and peptide infusions in vivo have significantly contributed to understanding the function of this conserved neuropeptide. Yet, a definitive role remains elusive due to discrepancies between models and a propensity to investigate pharmacological effects over endogenous function. To investigate the endogenous function of relaxin-3, we generated a recombinant adeno-associated viral (rAAV) vector expressing microRNA against relaxin-3 and validated its use to knock down relaxin-3 in adult rats. Bilateral stereotaxic infusion of rAAV1/2 EmGFP miR499 into the NI resulted in significant reductions in relaxin-3 expression as demonstrated by ablation of relaxin-3-like immunoreactivity at 3, 6 and 9 weeks and by qRT-PCR at 12 weeks. Neuronal health was unaffected as transduced neurons in all groups retained expression of NeuN and stained for Nissl bodies. Importantly, qRT-PCR confirmed that relaxin-3 receptor expression levels were not altered to compensate for reduced relaxin-3. Behavioural experiments confirmed no detrimental effects on general health or well-being and therefore several behavioural modalities previously associated with relaxin-3 function were investigated. The validation of this viral vector-based model provides a valuable alternative to existing in vivo approaches and promotes a shift towards more physiologically relevant investigations of endogenous neuropeptide function.

Inhibition of HBV replication by VPS4B and its dominant negative mutant VPS4B-K180Q in vivo

This study examined the anti-hepatitis B virus (HBV) effect of wild-type (WT) vacuolar protein sorting 4B (VPS4B) and its dominant negative (DN) mutant VPS4B-K180Q in vivo in order to further explore the relationship between HBV and the host cellular factor VPS4. VPS4B gene was amplified from Huh7 cells by RT-PCR and cloned into the eukaryotic expression vector pXF3H. Then, the VPS4B plasmid and the VPS4B-K180Q mutation plasmid were constructed by using the overlap extension PCR site-directed mutagenesis technique. VPS4B and HBV vectors were co-delivered into mice by the hydrodynamic tail-vein injection to establish HBV vector-based models. Quantities of HBsAg and HBeAg in the mouse sera were determined by ElectroChemiLuminescence (ECL). HBV DNA in sera was measured by real-time quantitative PCR. Southern blot analysis was used to assay the intracellular HBV nuclear capsid-related DNA, real-time quantitative PCR to detect the HBV-related mRNA and immunohistochemical staining to observe the HBcAg expression in the mouse liver tissues. Our results showed that VPS4B and its mutant VPS4B-K180Q could decrease the levels of serum HBsAg, HBeAg and HBV-DNA. In addition, the HBV DNA replication and the mRNA level of HBV in the liver tissues of treated mice could be suppressed by VPS4B and VPS4B-K180Q. It was also found that VPS4B and VPS4B-K180Q had an ability to inhibit core antigen expression in the infected mouse liver. Furthermore, the anti-HBV effect of mutant VPS4B-K180Q was more potent than that of wild-type VPS4B. Taken together, it was concluded that VPS4B and its DN mutant VPS4B-K180Q have anti-HBV effect in vivo, which helps develop molecular therapeutic strategies for HBV infection.

Exploiting Wikipedia for cross-lingual and multilingual information retrieval

In this article we show how Wikipedia as a multilingual knowledge resource can be exploited for Cross-Language and Multilingual Information Retrieval (CLIR/MLIR). We describe an approach we call Cross-Language Explicit Semantic Analysis (CL-ESA) which indexes documents with respect to explicit interlingual concepts. These concepts are considered as interlingual and universal and in our case correspond either to Wikipedia articles or categories. Each concept is associated to a text signature in each language which can be used to estimate language-specific term distributions for each concept. This knowledge can then be used to calculate the strength of association between a term and a concept which is used to map documents into the concept space. With CL-ESA we are thus moving from a Bag-Of-Words model to a Bag-Of-Concepts model that allows language-independent document representations in the vector space spanned by interlingual and universal concepts. We show how different vector-based retrieval models and term weighting strategies can be used in conjunction with CL-ESA and experimentally analyze the performance of the different choices. We evaluate the approach on a mate retrieval task on two datasets: JRC-Acquis and Multext. We show that in the MLIR settings, CL-ESA benefits from a certain level of abstraction in the sense that using categories instead of articles as in the original ESA model delivers better results.

Development and testing of a distributed urban storm runoff event model with a vector-based catchment delineation

Schematic of the rainfall–runoff process of the TSR model and urban landscape GIS delineation. ► A vector-based urban storm runoff event model is developed and tested. ► The model was set up and evaluated for a small urban catchment in Tokyo. ► Simulated water levels during two events closely reproduced the observed ones. ► The model can be used to evaluate flow and inundation conditions. The recent advances in GIS technology as well as data availability open up new possibilities concerning urban storm runoff modeling. In this paper, a vector-based distributed storm event runoff model – the Tokyo Storm Runoff (TSR) model – is developed and tested for urban runoff analysis using two historical storm events. The set-up of this model is based on urban landscape GIS delineation that faithfully describes the complicated urban land use features in detail. The flow between single spatial elements is based on established hydraulic and hydrological models with equations that describe all aspects of storm runoff generation in an urban environment. The model was set up and evaluated for the small urban lower Ekota catchment in Tokyo Metropolis, Japan. No calibration or tuning was performed, but the general model formulation was used with standard parameter values obtained from the literature. The runoff response to two storm events were simulated; one minor event resulting only in a small-scale flood wave and one major event which inundated parts of the catchment. For both events, the simulated water levels closely reproduced the observed ones. For the major event, also the reported inundation area was well described by the model. It was also demonstrated how the model can be used to evaluate the flow conditions in specific components of the urban hydrological system, which facilitates e.g. evaluation of flood-preventive measures.

Reversible Parallel Discrete Event Formulation of a TLM-Based Radio Signal Propagation Model

Radio signal strength estimation is essential in many applications, including the design of military radio communications and industrial wireless installations. For scenarios with large or richly featured geographical volumes, parallel processing is required to meet the memory and computation time demands. Here, we present a scalable and efficient parallel execution of the sequential model for radio signal propagation recently developed by Nutaro et al. [2008]. Starting with that model, we (a) provide a vector-based reformulation that has significantly lower computational overhead for event handling, (b) develop a parallel decomposition approach that is amenable to reversibility with minimal computational overheads, (c) present a framework for transparently mapping the conservative time-stepped model into an optimistic parallel discrete event execution, (d) present a new reversible method, along with its analysis and implementation, for inverting the vector-based event model to be executed in an optimistic parallel style of execution, and (e) present performance results from implementation on Cray XT platforms. We demonstrate scalability, with the largest runs tested on up to 127,500 cores of a Cray XT5, enabling simulation of larger scenarios and with faster execution than reported before on the radio propagation model. This also represents the first successful demonstration of the ability to efficiently map a conservative time-stepped model to an optimistic discrete-event execution.

Impact of wavefront distortion and scattering on 2-photon microscopy in mammalian brain tissue

Two-photon (2P) microscopy is widely used in neuroscience, but the optical properties of brain tissue are poorly understood. We have investigated the effect of brain tissue on the 2P point spread function (PSF2P) by imaging fluorescent beads through living cortical slices. By combining this with measurements of the mean free path of the excitation light, adaptive optics and vector-based modeling that includes phase modulation and scattering, we show that tissue-induced wavefront distortions are the main determinant of enlargement and distortion of the PSF2P at intermediate imaging depths. Furthermore, they generate surrounding lobes that contain more than half of the 2P excitation. These effects reduce the resolution of fine structures and contrast and they, together with scattering, limit 2P excitation. Our results disentangle the contributions of scattering and wavefront distortion in shaping the cortical PSF2P, thereby providing a basis for improved 2P microscopy.