Nonlinear principal component analysis

We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organizing maps. We present a general algorithm which maps low-dimensional lattices into high-dimensional data manifolds without violation of topology. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. Moreover we present a second algorithm for the extraction of generalized principal curves comprising disconnected and branching manifolds. The performance of the algorithm is demonstrated for both one- and two-dimensional principal manifolds and also for the case of sparse data sets. As an application we reveal cluster structures in a set of real world data from the domain of ecotoxicology

1-Methyl-4-(4-nitro­benzo­yl)pyridinium perchlorate

In the main mol­ecule of the title compound, C13H11N2O3 +·ClO4 −, the two aromatic rings are twisted by 56.19 (3)° relative to each other and the nitro group is not coplanar with the benzene ring [36.43 (4)°]. The crystal packing is dominated by infinite aromatic stacks in the a-axis direction. These are formed by the benzene units of the mol­ecule featuring an alternating arrangement, which explains the two different distances of 3.3860 (4) and 3.4907 (4) Å for the aromatic units (these are the perpendicular distances of the centroid of one aromatic ring on the mean plane of the other other aromatic ring). Adjacent stacks are connected by π–π stacking between two pyridinium units [3.5949 (4) Å] and weak C—H⋯O inter­actions. The perchlorate anions are accomodated in the lattice voids connected to the cation via weak C—H⋯O contacts between the O atoms of the anion and various aromatic as well as methyl H atoms

Categorical Modeling of the Flow Pattern of Liquid Organic Compounds Between Blade Electrodes Using Semiempirical and ab initio Quantum Chemical Descriptors

For a data set of 30 organic fluids, categorical modeling has been employed to predict the flow pattern under an external electric field. To this end, a previously generated data set was augmented by 10 compounds with new experimental results, and quantum chemical methods have been used to characterize the geometric and electronic structure of the molecules on both the semiempirical and ab initio levels of theory. Both linear discriminant analysis (LDA) and binary logistic regression (BLR) have been employed to model the flow rate (high vs. low) and flow direction (left vs. right). For the flow rate, good LDA and BLR calibration statistics using the dipole moment, hydrophobicity and some charged partial surface area (CPSA) descriptors is accompanied with moderate prediction statistics, as evaluated through simulated external validation, and activity scrambling shows that chance correlation is not relevant. Additional neural network analyses yielded no stable models due to constraints imposed by the data set size. For the flow direction, LDA and BLR calibration and prediction statistics show more variation among the different models generated, with an overall performance inferior to the one for the flow rate. Here, besides CPSA descriptors, two parameters characterizing the softness of the electronic structure are involved. In general, BLR is slightly superior to LDA for both properties. The results are discussed in terms of contingency table statistics and with respect to the mechanistic meaning of molecular descriptors

Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making

Background: Safety assessment for repeated dose toxicity is one of the largest challenges in the process to replace animal testing. This is also one of the proof of concept ambitions of SEURAT-1, the largest ever European Union research initiative on alternative testing, co-funded by the European Commission and Cosmetics Europe. This review is based on the discussion and outcome of a workshop organized on initiative of the SEURAT-1 consortium joined by a group of international experts with complementary knowledge to further develop traditional read-across and include new approach data. Objectives: The aim of the suggested strategy for chemical read-across is to show how a traditional read-across based on structural similarities between source and target substance can be strengthened with additional evidence from new approach data—for example, information from in vitro molecular screening, “-omics” assays and computational models—to reach regulatory acceptance. Methods: We identified four read-across scenarios that cover typical human health assessment situations. For each such decision context, we suggested several chemical groups as examples to prove when read-across between group members is possible, considering both chemical and biological similarities. Conclusions: We agreed to carry out the complete read-across exercise for at least one chemical category per read-across scenario in the context of SEURAT-1, and the results of this exercise will be completed and presented by the end of the research initiative in December 2015

Survey of Appearance and temporal concentrations of polar organic pollutants in Saxon waters

Integrative passive samplers such as the Chemcatcher are often proposed as alternatives for conventional grab sampling of surface waters. So far, their routine application for regulatory monitoring is hampered (among others) by the fact that TWA concentrations may depend significantly on the design and specifics of the samplers employed. The presented study addresses this issue, focusing on the uptake of polar organic pollutants in three different Chemcatcher configurations and polydimethylsiloxane (PDMS) sheets in the field. Covering waste water treatment plant effluents, creeks, and rivers, samplers were deployed for periods of 14–21 days in eight trials over the course of one year. 33 organic pesticides, 14 transformation products and 31 pharmaceuticals could be detected at least once in TWA concentrations ranging from 0.03 ng/L to 16.5 μg/L. We show that through employing generic, i.e. sampler specific, rather than compound specific sampling rates, the variation among results from three integrative passive sampler designs yields linear correlations with an offset of less than 0.1 and correlation coefficients r2 > 0.8. In this way, TWA concentrations enable the identification of low-concentration xenobiotics of concern, which may support regulatory monitoring correspondingly