Chemical recycling of plastics assisted by microwave multi-frequency heating

Handling plastic waste through recycling allows extending the life of polymeric materials, avoiding recurrence to incineration or landfilling. In contrast with traditional mechanical recycling technologies, chemical recycling enables the obtention of the virgin monomers by means of depolymerisation to create new polymers with the same mechanical and thermal properties as the originals. Research presented in this paper is part of the polynSPIRE project (Horizon 2020 European funding programme) and develops and scales-up a heated reactor to carry out the depolymerisation of polyamide-6 (PA6), polyamide-6, 6 (PA66) and polyurethane (PU) using microwave (MW) technology as the heating source. The purpose is to design and optimize a MW reactor using up to eight ports emitting electromagnetic waves. Finite element method (FEM) simulation and optimisation are used to design the reactor, considering as parameters the data obtained from experimental dielectric testing and lab-scale characterisation of the processes and materials studied. Two different COMSOL Multiphysics modules are involved in this work: Radio Frequency (RF) and Chemical Reaction Engineering (RE), to simulate the reactor cavity using two frequency levels (915 MHz and 2.45 GHz) with a power level of 46 kW, and the chemical depolymerisation process, respectively. A sensitivity study has been performed on key parameters such as the frequency, the number of ports, and position inside the reactor to consolidate the final design. It is expected that these results assist in the design and scale-up of microwave technology for the chemical recycling of plastics, and for the large-scale deployment of this sustainable recovery alternative. © 2021 The Author

Stereo visual odometry in urban environments based on detecting ground features

publisher: Elsevier articletitle: Stereo visual odometry in urban environments based on detecting ground features journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.03.004 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.publisher: Elsevier articletitle: Stereo visual odometry in urban environments based on detecting ground features journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.03.004 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Deciphering cell-cell interactions and communication from gene expression.

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context

Deciphering cell-cell interactions and communication from gene expression.

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context

Whole-body gene expression atlas of an adult metazoan

Gene activity defines cell identity, drives intercellular communication, and underlies the functioning of multicellular organisms. We present the single-cell resolution atlas of gene activity of a fertile adult metazoan: Caenorhabditis elegans. This compendium comprises 180 distinct cell types and 19,657 expressed genes. We predict 7541 transcription factor expression profile associations likely responsible for defining cellular identity. We predict thousands of intercellular interactions across the C. elegans body and the ligand-receptor pairs that mediate them, some of which we experimentally validate. We identify 172 genes that show consistent expression across cell types, are involved in basic and essential functions, and are conserved across phyla; therefore, we present them as experimentally validated housekeeping genes. We developed the WormSeq application to explore these data. In addition to the integrated gene-to-systems biology, we present genome-scale single-cell resolution testable hypotheses that we anticipate will advance our understanding of the molecular mechanisms, underlying the functioning of a multicellular organism and the perturbations that lead to its malfunction

Pre-collision systems for urban environment accidents avoidance

Los sistemas de seguridad primaria están orientados a evitar accidentes, mientras que los de seguridad secundaria tienen como objetivo el reducir las consecuencias de los mismos. Existe un grupo de sistemas que se solapan entre ambos, ya que aprovechan información propia de los primeros pero, en general, persiguen el segundo objetivo: son los sistemas pre-colisión. Los sistemas pre-colisión que se contemplan en este trabajo de investigación consideran acciones sobre los controles del vehículo (frenos y dirección) y la generación de señales para el despliegue de sistemas de seguridad que pueden ser activados antes de la colisión. En este artículo, se presenta un sistema pre-colisión cuyo objetivo es evitar el accidente o atropello, o bien, minimizar las posibles lesiones ocasionadas. El desarrollo de maniobras de mitigación dependerá del correcto análisis e interpretación del entorno, por lo que, en este trabajo, se presentan algoritmos de detección de obstáculos, evaluación del riesgo y la evitabilidad del accidente.No data (2013)UE

Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

AbstractCell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which is the first strategy to decipher context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand receptor pairs) linked to COVID-19 severities and Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.</jats:p

Context-aware deconvolution of cell-cell communication with Tensor-cell2cell.

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions

Context-aware deconvolution of cell–cell communication with Tensor-cell2cell

AbstractCell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell–cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell–cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell–cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.</jats:p