MEMO: Mass Spectrometry-based Sample Vectorization to Explore Chemodiverse Datasets

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g. via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods. Indeed, the experimental variation - changes in chromatographical or mass spectrometric conditions - often hinders the direct comparison of the profiled samples. Here we introduce MEMO - MS2 BasEd SaMple VectOrization - a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics taking into account fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.

BOPAL 2.0 and a study of tRNA and rRNA gene evolution in Clostridium

We present BOPAL 2.0, an improved version of the BOPAL algorithm for the evolutionary history inference of tRNA and rRNA genes in bacterial genomes. Our approach can infer complete evolutionary scenarios and ancestral gene orders on a phylogeny and considers a wide range of events such as duplications, deletions, substitutions, inversions and transpositions. It is based on the fact that tRNA and rRNA genes are often organized in operons/clusters in bacteria, and this information is used to help identify orthologous genes for each genome comparison. BOPAL 2.0 introduces new features, such as a triple-wise alignment step, context-aware singleton matching and a second pass of the algorithm. Evaluation on simulated datasets shows that BOPAL 2.0 outperforms the original BOPAL in terms of the accuracy of inferred events and ancestral genomes. We also present a study of the tRNA/rRNA gene evolution in the Clostridium genus, in which the organization of these genes is very divergent. Our results indicate that tRNA and rRNA genes in Clostridium have evolved through numerous duplications, losses, transpositions and substitutions, but very few inversions were inferred.

EVALUATION OF 3D BUILDING MODEL USING TERRESTRIAL LASER SCANNING AND DRONE PHOTOGRAMMETRY

The objective of our study is the evaluation of the 3D modeling of buildings and the extraction of structural elements from point clouds obtained using two acquisition techniques (drone and terrestrial laser scanner), as well as the evaluation of the usefulness of their integration. The drone shooting mission was carried using the DJI Phantom 3 Professional and the Sony EXMOR 1/2.3" CMOS RGB camera. For the TLS scanning mission, 9 scanning stations were performed using the FARO Focus S350 laser scanner.To allow the fusion of the two point clouds obtained from drone imagery and TLS, an alignment step is applied. This step was performed using the Iterative Closest Point algorithm. Segmentation was performed using the adapted RANSAC algorithm on point clouds obtained from the drone mission and the TLS mission as well as on the merged point cloud in order to extract structural elements of the building such as windows, doors and stairs. Analysis of the results emphasizes the importance of TLS and drone in 3D modeling. TLS gave better results than the drone in extracting structural elements. This work confirms the importance of complementarity between these two technologies to produce detailed, complete and precise 3D models.

Assimilating sea ice deformation observations using a multiscale alignment ensemble data assimilation method

<p>A multiscale alignment (MSA) method was proposed by Ying (2019) for ensemble data assimilation to reduce the errors caused by displacement of coherent features. The MSA method decomposes a model state into components ranging from large to small spatial scales, then applies ensemble filters to update each scale component sequentially. After a larger scale component analysis increment is derived from the observations, displacement vectors are computed from the analysis increments through an optical flow algorithm. These displacement vectors are then used to warp the model mesh, which reduces position errors in the smaller scale components before the ensemble filter is applied again.</p><p>The MSA method is now applied to a sea ice prediction problem at NERSC to assimilate satellite-derived sea ice deformation observations into the next generation Sea Ice Model (neXtSIM) simulations. Preliminary results show that the MSA can more effectively reduce the position errors of the linear kinematic features of sea ice than the traditional ensemble Kalman filter. The alignment step is shown to be a big contributor for error reduction in our test case. We will also discuss the remaining challenges of tuning parameters in the MSA method and dealing with model deficiencies.</p>

Monocular Visual Inertial Direct SLAM with Robust Scale Estimation for Ground Robots/Vehicles

In this paper, we present a novel method for visual-inertial odometry for land vehicles. Our technique is robust to unintended, but unavoidable bumps, encountered when an off-road land vehicle traverses over potholes, speed-bumps or general change in terrain. In contrast to tightly-coupled methods for visual-inertial odometry, we split the joint visual and inertial residuals into two separate steps and perform the inertial optimization after the direct-visual alignment step. We utilize all visual and geometric information encoded in a keyframe by including the inverse-depth variances in our optimization objective, making our method a direct approach. The primary contribution of our work is the use of epipolar constraints, computed from a direct-image alignment, to correct pose prediction obtained by integrating IMU measurements, while simultaneously building a semi-dense map of the environment in real-time. Through experiments, both indoor and outdoor, we show that our method is robust to sudden spikes in inertial measurements while achieving better accuracy than the state-of-the art direct, tightly-coupled visual-inertial fusion method.

Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal’s baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed.

Application of the “EigenValue Analysis (EVANS)” Methodology to Build Quantitative Structure Pharmacokinetic Relationship Models

We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.

Application of the “EigenValue Analysis (EVANS)” Methodology to Build Quantitative Structure Pharmacokinetic Relationship Models

We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.

Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity

Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: Some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (Bamboo), the first benchmark to support empirical assessment of graph-based MR similarity metrics. Bamboo maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric’s robustness against meaning-altering and meaning- preserving graph transformations. We show the benefits of Bamboo by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.