Automation of product packaging for industrial applications

[EN] This work presents a robotic-based solution devised to automate the product packaging in industrial environments. Although the proposed approach is illustrated for the case of the shoe industry, it applies to many other products requiring similar packaging processes. The main advantage obtained with the automated task is that productivity could be significantly increased. The key algorithms for the developed robot system are: object detection using a computer vision system; object grasping; trajectory planning with collision avoidance; and operator interaction using a force/torque sensor. All these algorithms have been experimentally tested in the laboratory to show the effectiveness and applicability of the proposed approach.This work has been partly supported by Ministerio de Economia y Competitividad of the Spanish Government [Grant No. RTC201654086 and PRI-AIBDE-2011-1219], by the Deutscher Akademischer Austauschdienst (DAAD) of the German Government (Projekt-ID 54368155) and by ROBOFOOT project [Grant No. 260159] of the European Commission.Perez-Vidal, C.; Gracia, L.; De Paco, J.; Wirkus, M.; Azorin, J.; De Gea, J. (2018). Automation of product packaging for industrial applications. International Journal of Computer Integrated Manufacturing. 31(2):129-137. https://doi.org/10.1080/0951192X.2017.1369165S12913731

The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags.

Whereas genome sequencing defines the genetic potential of an organism, transcript sequencing defines the utilization of this potential and links the genome with most areas of biology. To exploit the information within the human genome in the fight against cancer, we have deposited some two million expressed sequence tags (ESTs) from human tumors and their corresponding normal tissues in the public databases. The data currently define approximately 23,500 genes, of which only approximately 1,250 are still represented only by ESTs. Examination of the EST coverage of known cancer-related (CR) genes reveals that <1% do not have corresponding ESTs, indicating that the representation of genes associated with commonly studied tumors is high. The careful recording of the origin of all ESTs we have produced has enabled detailed definition of where the genes they represent are expressed in the human body. More than 100,000 ESTs are available for seven tissues, indicating a surprising variability of gene usage that has led to the discovery of a significant number of genes with restricted expression, and that may thus be therapeutically useful. The ESTs also reveal novel nonsynonymous germline variants (although the one-pass nature of the data necessitates careful validation) and many alternatively spliced transcripts. Although widely exploited by the scientific community, vindicating our totally open source policy, the EST data generated still provide extensive information that remains to be systematically explored, and that may further facilitate progress toward both the understanding and treatment of human cancers