Bio-plausible digital implementation of a reward modulated STDP synapse

Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external learning signal to modulate the synaptic plasticity produced by Spike-Timing-Dependent Plasticity (STDP). Combining the advantages of reinforcement learning and the biological plausibility of STDP, online learning on SNN in real-world scenarios can be applied. This paper presents a fully digital architecture, implemented on an Field-Programmable Gate Array (FPGA), including the R-STDP learning mechanism in a SNN. The hardware results obtained are comparable to the software simulations results using the Brian2 simulator. The maximum error is of 0.083 when a 14-bits fix-point precision is used in realtime. The presented architecture shows an accuracy of 95% when tested in an obstacle avoidance problem on mobile robotics with a minimum use of resources

Event-Based Regression with Spiking Networks

Spiking Neuron Networks (SNNs), also known as the third generation of neural networks, are inspired from natural computing in the brain and recent advances in neuroscience. SNNs can overcome the computational power of neural networks made of threshold or sigmoidal units. Recent advances on event-based devices along with their great power, considering the time factor, make SNNs a cutting-edge priority research objective. SNNs have been used mainly for classification problems, but their application to regression tasks remains challenging due to the complexity of training with continuous output data. In the literature we can find some first approximations in regression, specifically, for problems of a single variable of continuous values. This work deals with the analysis of the behavior of SNNs as predictors of multivariable continuous values. For this, a data set based on events has been generated from a bouncing ball and an event-based camera. The goal is to predict the next position of the ball over time

SpikeBALL: Neuromorphic Dataset for Object Tracking

Most of widely used datasets are not suitable for Spiking Neural Networks (SNNs) due to the need to encode the static data into spike trains and then put them into the network. In addition, the majority of these datasets have been generated to classify objects and can not be used to solve object tracking problems. Therefore, we propose a new neuromorphic dataset, SpikeBALL, for object tracking that contributes to improve the development of the SNN algorithm for these type of problems

Deep Spiking Neural Network for object tracking

We are investigating surrogate gradient as optimization methods in Deep SNN for regression prob- lems. A SNN able to detect a ball at high speed is being developed in which the voltage potential of the output neurons correspond, in real time, with its position, making possible its application in robotic systems that require fast object tracking. As a future work, training and validation over the network and dataset design would be performed using PyTorch framework, as well as the deployment of the system into a robotic platform, for object identification and tracking

Online programming system for robotic fillet welding in Industry 4.0

Purpose Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with "U"- and "L"-shaped structures, which responds to the needs of the Fourth Industrial Revolution. Design/methodology/approach In this paper, the authors propose an online robot programming methodology that eliminates unnecessary steps traditionally performed in robotic welding, so that the operator only performs three steps to complete the welding task. First, choose the piece to weld. Then, enter the welding parameters. Finally, it sends the automatically generated program to the robot. Findings The system finally managed to perform the fillet welding task with the proposed method in a more efficient preparation time than the compared methods. For this, a reduced number of components was used compared to other systems: a structured light 3 D camera, two computers and a concentrator, in addition to the six-axis industrial robotic arm. The operating complexity of the system has been reduced as much as possible. Practical implications To the best of the authors' knowledge, there is no scientific or commercial evidence of an online robot programming system capable of performing a fillet welding process, simplifying the process so that it is completely transparent for the operator and framed in the Industry 4.0 paradigm. Its commercial potential lies mainly in its simple and low-cost implementation in a flexible system capable of adapting to any industrial fillet welding job and to any support that can accommodate it. Originality/value In this study, a robotic robust system is achieved, aligned to Industry 4.0, with a friendly, intuitive and simple interface for an operator who does not need to have knowledge of industrial robotics, allowing him to perform a fillet welding saving time and increasing productivity

SUMOylation controls Hu antigen R posttranscriptional activity in liver cancer

© 2024 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).The posttranslational modification of proteins critically influences many biological processes and is a key mechanism that regulates the function of the RNA-binding protein Hu antigen R (HuR), a hub in liver cancer. Here, we show that HuR is SUMOylated in the tumor sections of patients with hepatocellular carcinoma in contrast to the surrounding tissue, as well as in human cell line and mouse models of the disease. SUMOylation of HuR promotes major cancer hallmarks, namely proliferation and invasion, whereas the absence of HuR SUMOylation results in a senescent phenotype with dysfunctional mitochondria and endoplasmic reticulum. Mechanistically, SUMOylation induces a structural rearrangement of the RNA recognition motifs that modulates HuR binding affinity to its target RNAs, further modifying the transcriptomic profile toward hepatic tumor progression. Overall, SUMOylation constitutes a mechanism of HuR regulation that could be potentially exploited as a therapeutic strategy for liver cancer.This work was supported by grants to M.L.M.-C. from Departamento de Industria del Gobierno Vasco, Spain; Ministerio de Ciencia e Innovación, Spain (grant no. PID2020-117116RB-I00); European Regional Development Fund (ERDF), EU; and CIBERehd, which is funded by Instituto de Salud Carlos III (ISCIII), Spain. M.L.M.-C. and J.S. received funding from Ministerio de Ciencia e Innovación (grant no. RTC2019-007125-1) and ISCIII (grant no. DTS20/00138). M.L.M.-C. and R.M.L. acknowledge Ministerio de Ciencia e Innovación (grant no. RED2022-134397-T). M.L.M.-C. and J.M.B. were awarded with a grant from Fundación la Caixa, Spain (grant no. HR17-00601). M.L.M.-C., J.M.B., M.A.A., and J.J.G.M. acknowledge financial support from Fundación Científica de la Asociación Española Contra el Cáncer (AECC), Spain. M.S.R. recognizes funding from Fondo Sectorial de Investigación SRE - CONACYT, Mexico (grant no. 0280365); Horizon 2020 Research and Innovation Program funded under Marie Skłodowska-Curie Actions, EU (grant no. 765445); and REPÈRE and Programme de Prématuration from Région Occitanie, France. M.G., S.D., and K.M.-M. were supported by the National Institute on Aging (NIA), National Institutes of Health (NIH), US (grant no. Z01-AG000511-23). I.D.-M. is grateful for the grants received from Junta de Andalucía, Spain (grant no. BIO-198, US-1254317, P18-FR-3487, and P18-HO-4091); Ministerio de Ciencia, Innovación y Universidades, Spain (grant no. PGC2018-096049-BI00); and Fundación Ramón Areces, Spain. T.D. acknowledges Fondation ARC, France (grant no. 208084). J.J.G.M. was supported by Junta de Castilla y León, Spain (grant no. SA063P17); Fundación La Marató TV3, Spain (grant no. 201916-31); ISCIII (grant no. PI19/00819); CIBERehd; and ERDF (grant no. OLD-HEPAMARKER). M.A.A. recognizes Gobierno de Navarra, Spain (grant no. GºNa 42/21); Eurorregión Nueva Aquitania-Euskadi-Navarra, Spain; Ministerio de Ciencia e Innovación (grant no. PID2019-104878RB-I00); and CIBERehd. A.P. expresses gratitude to the European Research Council (ERC), EU (grant no. 804236) for their support. M.D.G. received financial support from Junta de Andalucía (grant no. PEMP-0036-2020 and BIO-0139); Ministerio de Universidades, Spain (grant no. FPU20/03957); ISCIII (grant no. PI20/01301), Fundación Sociedad Española de Endocrinología y Nutrición (FSEEN), Spain; CIBERehd; and CIBERobn, which is also funded by ISCIII. J.M.B. acknowledges Euskadi RIS3 (grant no. 2019222054, 2020333010, and 2021333003) and Elkartek programs from Gobierno Vasco (grant no. KK-2020/00008); ISCIII (grant no. PI18/01075, CPII19/00008, and PI21/00922); CIBERehd; PSC Support, UK; AMMF The Cholangiocarcinoma Charity, UK (grant no. EU/2019/AMMFt/001); Horizon 2020 Research and Innovation Program (grant no. 825510); ERDF; and PSC Partners Seeking a Cure, US. A.L. received financial support from the Damon Runyon-Rachleff Innovation Award, US (grant no. DR52-18) and the MERIT Award (R37) from the National Cancer Institute (NCI), NIH (grant no. R37CA230636). F.E. expresses his gratitude to ProteoRed from ISCIII (grant no. PT13/0001/0027) and CIBERehd. N.G.A.A. was funded by Ministerio de Ciencia, Innovación y Universidades (grant no. RTI2018-095700-B-I00). R.B. acknowledges financial support from Gobierno Vasco (grant no. IT1165-19); Ministerio de Economía, Industria y Competitividad, Spain (grant no. SAF2017-90900-REDT); Ministerio de Economía, Industria y Competitividad, ERDF (grant no. BFU2017-84653-P); Ministerio de Ciencia e Innovación (grant no. PID2020-114178GB-I00); and Horizon 2020 funded under Marie Skłodowska-Curie Actions (grant no. 765445-EU). A.M.A. acknowledges CIBERehd. L.A.M.-C. obtained grants from Ministerio de Economía y Competitividad (grant no. CSD2008-00005); Ministerio de Economía, Industria y Competitividad (grant no. BFU2016-77408-R); ISCIII; and EJP RD, EU (grant no. EJPRD19-040). I.G.-R. was supported by Ministerio de Economía, Industria y Competitividad (grant no. BES-2017-080435 ). M.S.-M. is grateful to the AECC, Sede de Bizkaia, Spain for the financial support. J.D.Z. was awarded with a grant from Ministerio de Economía, Industria y Competitividad (grant no. SEV-2016-0644-18-2). C.M. acknowledges Gobierno Vasco (grant no. IT-1264-19) and Ministerio de Ciencia e Innovación (grant no. PID2022-136788OB-I00). A.V.-C. was supported by Ministerio de Educación, Cultura y Deporte, Spain (grant no. FPU016/01513). C.F.-R. thanks Tekniker, Spain and CIC bioGUNE, Spain for financial support. A.G.-d.R. was funded by Bikaintek program from Gobierno Vasco (grant no. 48-AF-W1-2019-00012). N.G.-U. obtained a grant from Gobierno Vasco. T.C.D. expresses gratitude to AECC. J.S. received financial support from CIBERehd. C.M.R.-G. was supported by Ayudas a la Recualificación Margarita Salas from Universidad de Extremadura, Ministerio de Universidades financed by NextGenerationEU.Peer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Release 2.0—NESIM-RT: A real-time distributed spiking neural network simulator

NESIM-RT is a specialized tool designed for simulating neuromorphic systems. In this new release we extend its capabilities to include state-of-the art models like the AdexLIF and Izhikevich, and to incorporate dynamic synaptic mechanisms such as Spike-Timing Dependent Plasticity (STDP). With these new features, researchers can now observe in real-time how different parameters influence these models and learning rules, thereby gaining deeper insights into neuronal function and network dynamics.This work was also supported by the project NEMOVISION from the Ministerio de Ciencia e Innovación, PID2019-109465RB-I00/ AEI/10.13039/501100011033 and by the Junta de Andalucía and ERDF (GENIUS –P18-2399), and ERDF (OPTIMALE – FEDER-UCA18-108393). It is also part of the project TED2021-131880B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union ‘‘NextGenerationEU’’/PRTR

SUMOylation controls Hu antigen R posttranscriptional activity in liver cancer

International audienceThe posttranslational modification of proteins critically influences many biological processes and is a key mechanism that regulates the function of the RNA-binding protein Hu antigen R (HuR), a hub in liver cancer. Here, we show that HuR is SUMOylated in the tumor sections of patients with hepatocellular carcinoma in contrast to the surrounding tissue, as well as in human cell line and mouse models of the disease. SUMOylation of HuR promotes major cancer hallmarks, namely proliferation and invasion, whereas the absence of HuR SUMOylation results in a senescent phenotype with dysfunctional mitochondria and endoplasmic reticulum. Mechanistically, SUMOylation induces a structural rearrangement of the RNA recognition motifs that modulates HuR binding affinity to its target RNAs, further modifying the transcriptomic profile toward hepatic tumor progression. Overall, SUMOylation constitutes a mechanism of HuR regulation that could be potentially exploited as a therapeutic strategy for liver cancer

La investigación universitaria y sus contribuciones en Mesoamérica

La Universidad Autónoma de Chiapas a través de su Proyecto Académico 2014-2018, reafirma su compromiso con el desarrollo de nuestra región, al establecer líneas de desarrollo de nuestra región, al establecer líneas de desarrollo institucional, donde la vinculación de la investigación ocupa un lugar preponderante; en este sentido, a partir de 2015, junto con la comunidad académica internacional, se unió a la Agenda 2030 para el Desarrollo sostenible de la ONU y priorizó los 17 Objetivos de Desarrollo Sostenible (ODS) y sus 169 metas, con la finalidad de dar soluciona los grandes desafíos sociales, económicos y medioambientales que enfrenta la sociedad. Este libro es la recopilación de trabajos realizados por académicos de diversas Instituciones de Educación Superior y Centros de Investigación, de manera multidisciplinaria, interinstitucional e internacional, los cuales han permitido compartir intereses en diversas líneas de generación y aplicación del conocimiento