In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This study has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, were developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given

Automated workflows for modelling chemical fate, kinetics and toxicity.

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Toward Good Read-Across Practice (GRAP) guidance.

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015

Feature Representations for the Recognition of 3D Emblematic Gestures

Abstract. In human-machine interaction, gestures play an important role as input modality for natural and intuitive interfaces. The class of gestures often called “emblems ” is of special interest since they convey a well-defined meaning in an intuitive way. We present an approach for the visual recognition of 3D dynamic emblematic gestures in a smart room scenario using a HMM-based recognition framework. In particular, we assess the suitability of several feature representations calculated from a gesture trajectory in a detailed experimental evaluation on realistic data. Key words: 3D dynamic gesture recognition, human-machine interac-tion, smart rooms, time-series analysis

Saliency-based identification and recognition of pointed-at objects

Abstract — When persons interact, non-verbal cues are used to direct the attention of persons towards objects of interest. Achieving joint attention this way is an important aspect of natural communication. Most importantly, it allows to couple verbal descriptions with the visual appearance of objects, if the referred-to object is non-verbally indicated. In this contri-bution, we present a system that utilizes bottom-up saliency and pointing gestures to efficiently identify pointed-at objects. Furthermore, the system focuses the visual attention by steering a pan-tilt-zoom camera towards the object of interest and thus provides a suitable model-view for SIFT-based recognition and learning. We demonstrate the practical applicability of the proposed system through experimental evaluation in different environments with multiple pointers and objects

Moir\'e Fringes in Conductive Atomic Force Microscopy

Moir\'e physics plays an important role for the characterization of functional materials and the engineering of physical properties in general, ranging from strain-driven transport phenomena to superconductivity. Here, we report the observation of moir\'e fringes in conductive atomic force microscopy (cAFM) scans gained on the model ferroelectric Er(Mn,Ti)O$_3$. By performing a systematic study of the impact of key experimental parameters on the emergent moir\'e fringes, such as scan angle and pixel density, we demonstrate that the observed fringes arise due to a superposition of the applied raster scanning and sample-intrinsic properties, classifying the measured modulation in conductance as a scanning moir\'e effect. Our findings are important for the investigation of local transport phenomena in moir\'e engineered materials by cAFM, providing a general guideline for distinguishing extrinsic from intrinsic moir\'e effects. Furthermore, the experiments provide a possible pathway for enhancing the sensitivity, pushing the resolution limit of local transport measurements by probing conductance variations at the spatial resolution limit via more long-ranged moir\'e patterns

Assessing Uncertainty in Read-Across: Questions to Evaluate Toxicity Predictions Based on Knowledge Gained from Case Studies

Read-across as an alternative assessment method for chemical toxicity has growing interest in both the regulatory and industrial communities. The pivotal means of acquiring acceptance of a read-across prediction is identifying and assessing uncertainties associated with it. This study has identified and summarised in a structured way the variety of uncertainties that potentially impact acceptance of a readacross argument. The main sources of uncertainty were established and divided into four main categories: i) the regulatory use of the prediction, ii) the data for the apical endpoint being assessed, iii) the readacross argumentation, and iv) the similarity justification. Specifically, the context of, and relevance to, the regulatory use of a read-across will dictate the acceptable level of uncertainties. The apical endpoint (or other) data must be of sufficient quality and relevance for data gap filling. Read-Across argumentation uncertainties include: 1) mechanistic plausibility (i.e., the knowledge of the chemical and biological mechanisms leading to toxicity), 2) completeness of the supporting evidence, 3) robustness of the supporting data, and 4) Weight-of-Evidence. In addition, similarity arguments for chemistry, physicochemical properties, toxicokinetics and toxicodynamics are linked to these read-across argumentation issues. To further progress in this area, a series of questions are proposed with the goal of addressing each type of uncertainty