Gas kinematics around filamentary structures in the Orion B cloud

Context. Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young high-mass stars on future star formation properties is still poorly constrained. Aims. In the framework of the IRAM 30m ORION-B large program, we obtained observations of the translucent (2 ≤ AV < 6 mag) and moderately dense gas (6 ≤ AV < 15 mag), which we used to analyze the kinematics over a field of 5 deg2 around the filamentary structures. Methods. We used the Regularized Optimization for Hyper-Spectral Analysis (ROHSA) algorithm to decompose and de-noise the C 18 O(1−0) and 13CO(1−0) signals by taking the spatial coherence of the emission into account. We produced gas column density and mean velocity maps to estimate the relative orientation of their spatial gradients. Results. We identified three cloud velocity layers at different systemic velocities and extracted the filaments in each velocity layer. The filaments are preferentially located in regions of low centroid velocity gradients. By comparing the relative orientation between the column density and velocity gradients of each layer from the ORION-B observations and synthetic observations from 3D kinematic toy models, we distinguish two types of behavior in the dynamics around filaments: (i) radial flows perpendicular to the filament axis that can be either inflows (increasing the filament mass) or outflows and (ii) longitudinal flows along the filament axis. The former case is seen in the Orion B data, while the latter is not identified. We have also identified asymmetrical flow patterns, usually associated with filaments located at the edge of an H II region. Conclusions. This is the first observational study to highlight feedback from H II regions on filament formation and, thus, on star formation in the Orion B cloud. This simple statistical method can be used for any molecular cloud to obtain coherent information on the kinematics

QSPR Models for Predictions and Data Qualit\ue0 assurances: Melting Point and Boiling Point of Perfluorinated Chemicals,

10noneBhhatarai B.; Teetz W.; Öberg T.; Liu T.; Jeliazkova N.; Kochev N. ; Ognyan P.; Tetko I.; Kovarich S.; Gramatica P.Bhhatarai, Barun; Teetz, W.; Öberg, T.; Liu, T.; Jeliazkova, N.; Kochev, N.; Ognyan, P.; Tetko, I.; Kovarich, Simona; Gramatica, Paol

QSAR and QSPR models for emerging pollutants: WP3 activities within the FP7 European Project CADASTER

The EU-REACH regulation encourages the use of alternative in vitro and in silico methods in order to minimize animal testing, costs and time. Among these, quantitative structure-activity relationships (QSARs) represent a useful tool to predict unknown activities/properties for existing or even not yet synthesized chemicals. The development and validation of QSAR models for four classes of emerging pollutants (brominated flame retardants, fragrances, perfluorinated compounds and (benzo)triazoles is the central topic of Work Package 3 (WP3) within the FP7 European project CADASTER (CAse studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment). The final goal of the project is to exemplify the integration of information, models and strategies for carrying out hazard and risk assessments for large numbers of substances, organized in the four representative chemical classes. The aim of this poster is to summarize the WP3 activities within CADASTER project and the QSAR/QSPR models developed so far for the four classes of compounds under investigation. This modeling activity involved different project partners in universities and research institutes across Europe (University of Insubria, Linnaeus University, IVL Swedish Environmental Research Institute, Ideaconsult Ltd. and Helmholtz Zentrum M\ufcnchen), and was realized by different modeling approaches. For each class, ad hoc QSARs were developed for all the available experimental data (i.e. physico-chemical properties, environmental and mammalian toxicity) in order to characterize environmental behavior and activity profile of the chemicals. In agreement with the OECD principles for the validation of QSARs for regulatory purposes, all the proposed models were checked for their robustness, external predictivity and applicability domain. QSAR predictions, together with structural analysis (e.g. similarity analysis and multivariate ranking methods), were used for the identification of priority compounds (also among the ECHA pre-registration list) to optimize the experimental testing to be performed in WP2

QSAR and QSPR models for emerging pollutants: WP3 activities within the FP7 European Project CADASTER

The EU-REACH regulation encourages the use of alternative in vitro and in silico methods in order to minimize animal testing, costs and time. Among these, quantitative structure-activity relationships (QSARs) represent a useful tool to predict unknown activities/properties for existing or even not yet synthesized chemicals. The development and validation of QSAR models for four classes of emerging pollutants (brominated flame retardants, fragrances, perfluorinated compounds and (benzo)triazoles) is the central topic of Work Package 3 (WP3) within the FP7 European project CADASTER. The final goal of the project is to exemplify the integration of information, models and strategies for carrying out hazard and risk assessments for large numbers of substances, organized in the four representative chemical classes. The aim of this poster is to summarize the WP3 activities within CADASTER project and the QSAR models developed so far for the four classes of compounds under investigation. This modeling activity involved different project partners in universities and research institutes across Europe and was realized by different modeling approaches. For each class, ad hoc QSARs were developed for the available endpoints (physico-chemical properties, eco- and mammalian toxicity) in order to characterize environmental behavior and activity profile of the chemicals. In agreement with the OECD principles for the validation of QSARs for regulatory purposes, all the proposed models were checked for their robustness, external predictivity and applicability domain. QSAR predictions, together with structural analysis (similarity analysis and multivariate ranking methods), were used for the identification of priority compounds to optimize the experimental testing