Coherent lidar airborne wind sensor II: flight-test results at 2 and 10 νm

The use of airborne laser radar (lidar) to measure wind velocities and to detect turbulence in front of an aircraft in real time can significantly increase fuel efficiency, flight safety, and terminal area capacity. We describe the flight-test results for two coherent lidar airborne shear sensor (CLASS) systems and discuss their agreement with our theoretical simulations. The 10.6-μm CO(2) system (CLASS-10) is a flying brassboard; the 2.02-μm Tm:YAG solid-state system (CLASS-2) is configured in a rugged, light-weight, high-performance package. Both lidars have shown a wind measurement accuracy of better than 1 m/s.     

Branching and circular features in high dimensional data

Large observations and simulations in scientific research give rise to high-dimensional data sets that present many challenges and opportunities in data analysis and visualization. Researchers in application domains such as engineering, computational biology, climate study, imaging and motion capture are faced with the problem of how to discover compact representations of high-dimensional data while preserving their intrinsic structure. In many applications, the original data is projected onto low-dimensional space via dimensionality reduction techniques prior to modeling. One problem with this approach is that the projection step in the process can fail to preserve structure in the data that is only apparent in high dimensions. Conversely, such techniques may create structural illusions in the projection, implying structure not present in the original high-dimensional data. Our solution is to utilize topological techniques to recover important structures in high-dimensional data that contains non-trivial topology. Specifically, we are interested in high-dimensional branching structures. We construct local circle-valued coordinate functions to represent such features. Subsequently, we perform dimensionality reduction on the data while ensuring such structures are visually preserved. Additionally, we study the effects of global circular structures on visualizations. Our results reveal never-before-seen structures on real-world data sets from a variety of applications.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

Symmetry in scalar field topology

Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights into the properties of the underlying phenomenon. Though geometric symmetry has been well studied within areas like shape processing, identifying symmetry in scalar fields has remained largely unexplored due to the high computational cost of the associated algorithms. We propose a computationally efficient algorithm for detecting symmetric patterns in a scalar field distribution by analysing the topology of level sets of the scalar field. Our algorithm computes the contour tree of a given scalar field and identifies subtrees that are similar. We define a robust similarity measure for comparing subtrees of the contour tree and use it to group similar subtrees together. Regions of the domain corresponding to subtrees that belong to a common group are extracted and reported to be symmetric. Identifying symmetry in scalar fields finds applications in visualization, data exploration, and feature detection. We describe two applications in detail: symmetry-aware transfer function design and symmetry-aware isosurface extraction.     

Computational study on the interaction of a ring-hydroxylating dioxygenase from Sphingomonas CHY-1 with PAHs

The massive computational resources available in the framework of a grid paradigm approach represent an emerging tool in the bioinformatics field. In this paper, we used the above approach in the rapid determination of the interactions between the ring-hydroxylating dioxygenase, comprised six enzymatic subunits, and polycyclic aromatic hydrocarbons (PAHs) in their optimal positions. The results were obtained by simulating enzyme dynamics at 300 K through molecular dynamics calculations. For the first time, the equilibrated structure of the dioxygenase revealed a network of channels throughout the enzyme that were sufficiently large to allow a flow of small ions or molecules from the inner core of the complex to its exterior surface. The ring-hydroxylating dioxygenase was able to interact with some of the studied PAHs. Additionally, not only the number of aromatic rings but also the PAH shape were critical in predicting the ability of the dioxygenase to interact with these types of molecules. Docking calculations shed light on a new possible binding site that is far from the enzymatic one, which is potentially interesting in considering the stability of the enzyme itself.     

Conjugate mixture models for clustering multimodal data

The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. Observations from different modalities are not necessarily aligned in the sense there there is no obvious way to associate or compare them in some common space. A solution may consist in considering multiple clustering tasks independently for each modality. The main difficulty with such an approach is to guarantee that the unimodal clusterings are mutually consistent. In this letter, we show that multimodal clustering can be addressed within a novel framework: conjugate mixture models. These models exploit the explicit transformations that are often available between an unobserved parameter space (objects) and each of the observation spaces (sensors). We formulate the problem as a likelihood maximization task and derive the associated conjugate expectation-maximization algorithm. The convergence properties of the proposed algorithm are thoroughly investigated. Several local and global optimization techniques are proposed in order to increase its convergence speed. Two initialization strategies are proposed and compared. A consistent model selection criterion is proposed. The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.     

Proteome of human endometrium: Identification of differentially expressed proteins in proliferative and secretory phase endometrium

Purpose: To exploit the potential of proteomics to identify and study additional yet‐unidentified important proteins present in human endometrium. Experimental design: The proteome of human endometrium would be established using 2‐DE and MALDI and the data analyzed to identify differential protein expression in the proliferative and secretory phase of the menstrual cycle using PDQuest software and MALDI. Results: In the present work, 2‐DE of human endometrium protein led to the resolution of over 200 spots. Subsequent MALDI analysis of 215 spots allowed the identification of 194 proteins. A total of 57 out of the 215 spots were found to be differentially expressed, out of which 49 could be identified using MALDI. These differentially expressed proteins included structural proteins, molecular chaperones, signaling proteins, metabolic proteins, proteins related to immunity, RNA biogenesis, protein biosynthesis and others. The differential expressions of seven representative proteins in secretory and proliferative phase endometrium tissue were confirmed by immunoblot analysis. Conclusion and clinical relevance: This study establishes the 2‐D proteome of human endometrium represented by 194 identified protein spots. The present data provides an important clue towards determining the function of these proteins with respect to endometrium related diseases.     

Assessment of the EEG complexity during activations from sleep

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.     

Conformational analysis of alkali metal complexes of anionic species of aspartic acid, their interconversion and deprotonation: a DFT investigation

Gas-phase geometry-optimized structures of aspartate complexes of anionic species (Hasp(-)) with lithium, sodium and potassium metal cations and transition-state structures for their interconversions were obtained using the density functional theory computations at the B3LYP/6-311++G(d,p) level of theory. The metal ion affinities of Hasp(-) species and deprotonation energies of [Hasp-M] complexes, M=Li+, Na+ and K+, and their conformers were obtained. Relative energies of the [Hasp-M] complex conformers, reaction energies, thermodynamic properties, rate and equilibrium constants of their complexation are reported. Binding energies of the most stable complexes with Li+, Na+ and K+ are -168.53, -133.34 and -117.68kcal/mol, respectively. The most stable complex conformer as a tri-coordinated form for aspartate complex with Li(+) and bi-coordinated form for aspartate complexes with Na+ and K+ were found.