Incidence of outer retinal tubulation in ranibizumab-treated age-related macular degeneration.

PURPOSE: To investigate the incidence of outer retinal tubulation (ORT) in ranibizumab-treated neovascular age-related macular degeneration patients. METHODS: We included 480 consecutive patients (546 eyes) with neovascular age-related macular degeneration, who were treated with variable-dosing intravitreal ranibizumab, evaluated with spectral domain optical coherence tomography, and followed-up for a minimum period of 6 months. Optical coherence tomographies were evaluated for the first appearance of ORT, precursor signs, and type of underlying lesion. Visual acuity was also recorded. RESULTS: Outer retinal tubulation was observed in 30% of eyes during a mean follow-up period of 26.7 months (SD, 13.5). Kaplan-Meier survival analysis revealed that the ORT incidence (2.5, 17.5, 28.4, and 41.6% at baseline, after 1, 2, and 4 years, respectively) continuously increased, despite visually effective anti-vascular endothelial growth factor treatment. Outer retinal tubulation was associated with a poorer functional benefit. Lower baseline visual acuity was associated with a higher risk of developing ORT. CONCLUSION: Incidence of ORT continuously increases despite visually optimal anti-vascular endothelial growth factor treatment of age-related macular degeneration. Outer retinal tubulation might be considered a prognostic factor for functional outcome and is relevant to avoid overtreatment.

The effect of skewness and kurtosis on the Kenward-Roger approximation when group distributions differ

This study examined the independent effect of skewness and kurtosis on the robustness of the linear mixed model (LMM), with the Kenward-Roger (KR) procedure, when group distributions are different, sample sizes are small, and sphericity cannot be assumed. Methods: A Monte Carlo simulation study considering a split-plot design involving three groups and four repeated measures was performed. Results: The results showed that when group distributions are different, the effect of skewness on KR robustness is greater than that of kurtosis for the corresponding values. Furthermore, the pairings of skewness and kurtosis with group size were found to be relevant variables when applying this procedure. Conclusions: With sample sizes of 45 and 60, KR is a suitable option for analyzing data when the distributions are: (a) mesokurtic and not highly or extremely skewed, and (b) symmetric with different degrees of kurtosis. With total sample sizes of 30, it is adequate when group sizes are equal and the distributions are: (a) mesokurtic and slightly or moderately skewed, and sphericity is assumed; and (b) symmetric with a moderate or high/extreme violation of kurtosis. Alternative analyses should be considered when the distributions are highly or extremely skewed and samples sizes are small.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Question Waves: A multicast query routing algorithm for social search

This paper describes Question Waves, an algorithm that can be applied to social search protocols, such as Asknext or Sixearch. In this model, the queries are propagated through the social network, with faster propagation through more trustable acquaintances. Question Waves uses local information to make decisions and obtain an answer ranking. With Question Waves, the answers that arrive first are the most likely to be relevant, and we computed the correlation of answer relevance with the order of arrival to demonstrate this result. We obtained correlations equivalent to the heuristics that use global knowledge, such as profile similarity among users or the expertise value of an agent. Because Question Waves is compatible with the social search protocol Asknext, it is possible to stop a search when enough relevant answers have been found; additionally, stopping the search early only introduces a minimal risk of not obtaining the best possible answer. Furthermore, Question Waves does not require a re-ranking algorithm because the results arrive sorted

Undressing of Waddlia chondrophila to enrich its outer membrane proteins to develop a new species-specific ELISA.

Waddlia chondrophila, an obligate intracellular bacterium of the Chlamydiales order, is considered as an agent of bovine abortion and a likely cause of miscarriage in humans. Its role in respiratory diseases was questioned after the detection of its DNA in clinical samples taken from patients suffering from pneumonia or bronchiolitis. To better define the role of Waddlia in both miscarriage and pneumonia, a tool allowing large-scale serological investigations of Waddlia seropositivity is needed. Therefore, enriched outer membrane proteins of W. chondrophila were used as antigens to develop a specific ELISA. After thorough analytical optimization, the ELISA was validated by comparison with micro-immunofluorescence and it showed a sensitivity above 85% with 100% specificity. The ELISA was subsequently applied to human sera to specify the role of W. chondrophila in pneumonia. Overall, 3.6% of children showed antibody reactivity against W. chondrophila but no significant difference was observed between children with and without pneumonia. Proteomic analyses were then performed using mass spectrometry, highlighting members of the outer membrane protein family as the dominant proteins. The major Waddlia putative immunogenic proteins were identified by immunoblot using positive and negative human sera. The new ELISA represents an efficient tool with high throughput applications. Although no association with pneumonia and Waddlia seropositivity was observed, this ELISA could be used to specify the role of W. chondrophila in miscarriage and in other diseases.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

Features extraction based on the Discrete Hartley Transform for closed contour

In this paper the authors propose a new closed contour descriptor that could be seen as a Feature Extractor of closed contours based on the Discrete Hartley Transform (DHT), its main characteristic is that uses only half of the coefficients required by Elliptical Fourier Descriptors (EFD) to obtain a contour approximation with similar error measure. The proposed closed contour descriptor provides an excellent capability of information compression useful for a great number of AI applications. Moreover it can provide scale, position and rotation invariance, and last but not least it has the advantage that both the parameterization and the reconstructed shape from the compressed set can be computed very efficiently by the fast Discrete Hartley Transform (DHT) algorithm. This Feature Extractor could be useful when the application claims for reversible features and when the user needs and easy measure of the quality for a given level of compression, scalable from low to very high quality.

Outer membrane vesicles from cold-adapted antarctic bacteria

Many Gram-negative, cold-adapted bacteria from the Antarctic environment produce large amounts of extracellular matter with potential biotechnological applications. Transmission electron microscopy (TEM) analysis after high-pressure freezing and freeze substitution (HPF-FS) showed that this extracellular matter is structurally complex, appearing around cells as a netlike mesh, and composed of an exopolymeric substance (EPS) containing large numbers of outer membrane vesicles (OMVs). Isolation, purification and protein profiling via 1D SDS-PAGE confirmed the outer membrane origin of these Antarctic bacteria OMVs. In an initial attempt to elucidate the role of OMVs in cold-adapted strains of Gram-negative bacteria, a proteomic analysis demonstrated that they were highly enriched in outer membrane proteins and periplasmic proteins associated with nutrient processing and transport, suggesting that the OMVs may be involved in nutrient sensing and bacterial survival. OMVs from Gram-negative bacteria are known to play a role in lateral DNA transfer, but the presence of DNA in these vesicles has remained difficult to explain. A structural study of Shewanella vesiculosa M7T using TEM and Cryo-TEM revealed that this Antarctic Gram-negative bacterium naturally releases conventional one-bilayer OMVs, together with a more complex type of OMV, previously undescribed, which on formation drags along inner membrane and cytoplasmic content and can therefore also entrap DNA.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

3D Surface Modeling From Point Clouds

The goal of this thesis is to implement software for creating 3D models from point clouds. Point clouds are acquired with stereo cameras, monocular systems or laser scanners. The created 3D models are triangular models or NURBS (Non-Uniform Rational B-Splines) models. Triangular models are constructed from selected areas from the point clouds and resulted triangular models are translated into a set of quads. The quads are further translated into an estimated grid structure and used for NURBS surface approximation. Finally, we have a set of NURBS surfaces which represent the whole model. The problem wasn’t so easy to solve. The selected triangular surface reconstruction algorithm did not deal well with noise in point clouds. To handle this problem, a clustering method is introduced for simplificating the model and removing noise. As we had better results with the smaller point clouds produced by clustering, we used points in clusters to better estimate the grids for NURBS models. The overall results were good when the point cloud did not have much noise. The point clouds with small amount of error had good results as the triangular model was solid. NURBS surface reconstruction performed well on solid models.

Outer membrane vesicles from cold-adapted antarctic bacteria

Many Gram-negative, cold-adapted bacteria from the Antarctic environment produce large amounts of extracellular matter with potential biotechnological applications. Transmission electron microscopy (TEM) analysis after high-pressure freezing and freeze substitution (HPF-FS) showed that this extracellular matter is structurally complex, appearing around cells as a netlike mesh, and composed of an exopolymeric substance (EPS) containing large numbers of outer membrane vesicles (OMVs). Isolation, purification and protein profiling via 1D SDS-PAGE confirmed the outer membrane origin of these Antarctic bacteria OMVs. In an initial attempt to elucidate the role of OMVs in cold-adapted strains of Gram-negative bacteria, a proteomic analysis demonstrated that they were highly enriched in outer membrane proteins and periplasmic proteins associated with nutrient processing and transport, suggesting that the OMVs may be involved in nutrient sensing and bacterial survival. OMVs from Gram-negative bacteria are known to play a role in lateral DNA transfer, but the presence of DNA in these vesicles has remained difficult to explain. A structural study of Shewanella vesiculosa M7T using TEM and Cryo-TEM revealed that this Antarctic Gram-negative bacterium naturally releases conventional one-bilayer OMVs, together with a more complex type of OMV, previously undescribed, which on formation drags along inner membrane and cytoplasmic content and can therefore also entrap DNA.

Shout and Act: an Algorithm for Digital Objects Preservation Inspired from Rescue Robots

We adapt the Shout and Act algorithm to Digital Objects Preservation where agents explore file systems looking for digital objects to be preserved (victims). When they find something they “shout” so that agent mates can hear it. The louder the shout, the urgent or most important the finding is. Louder shouts can also refer to closeness. We perform several experiments to show that this system works very scalably, showing that heterogeneous teams of agents outperform homogeneous ones over a wide range of tasks complexity. The target at-risk documents are MS Office documents (including an RTF file) with Excel content or in Excel format. Thus, an interesting conclusion from the experiments is that fewer heterogeneous (varying skills) agents can equal the performance of many homogeneous (combined super-skilled) agents, implying significant performance increases with lower overall cost growth. Our results impact the design of Digital Objects Preservation teams: a properly designed combination of heterogeneous teams is cheaper and more scalable when confronted with uncertain maps of digital objects that need to be preserved. A cost pyramid is proposed for engineers to use for modeling the most effective agent combinations