A high-performance IoT solution to reduce frost damages in stone fruits

[EN] Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the successful deployment of such services as they require both, ie, fully connected infrastructures and highly computational resources. Edge computing has emerged as a solution to bring computing power in close proximity to the sensors, providing energy savings, highly responsive web services, and the ability to mask transient cloud outages. In this paper, we propose an IoT monitoring system to activate anti-frost techniques to avoid crop loss, by defining two intelligent services to detect outliers caused by the sensor errors. The former is a nearest neighbor technique and the latter is the k-means algorithm, which provides better quality results but it increases the computational cost. Cloud versus edge computing approaches are analyzed by targeting two different low-power GPUs. Our experimental results show that cloud-based approaches provides highest performance in general but edge computing is a compelling alternative to mask transient cloud outages and provide highly responsive data analytic services in technologically hostile environments.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. Finally, we thank the farmers for the availability of their resources to be able to asses and improve the IoT monitoring system proposed.Guillén-Navarro, MA.; Martínez-España, R.; López, B.; Cecilia-Canales, JM. (2021). A high-performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience (Online). 33(2):1-14. https://doi.org/10.1002/cpe.529911433

c-Jun N-terminal kinase phosphorylation is a biomarker of plitidepsin activity

Plitidepsin is an antitumor drug of marine origin currently in Phase III clinical trials in multiple myeloma. In cultured cells, plitidepsin induces cell cycle arrest or an acute apoptotic process in which sustained activation of c-Jun N-terminal kinase (JNK) plays a crucial role. With a view to optimizing clinical use of plitidepsin, we have therefore evaluated the possibility of using JNK activation as an in vivo biomarker of response. In this study, we show that administration of a single plitidepsin dose to mice xenografted with human cancer cells does indeed lead to increased phosphorylation of JNK in tumors at 4 to 12 h. By contrast, no changes were found in other in vitro plitidepsin targets such as the levels of phosphorylated-ERK, -p38MAPK or the protein p27KIP1. Interestingly, plitidepsin also increased JNK phosphorylation in spleens from xenografted mice showing similar kinetics to those seen in tumors, thereby suggesting that normal tissues might be useful for predicting drug activity. Furthermore, plitidepsin administration to rats at plasma concentrations comparable to those achievable in patients also increased JNK phosphorylation in peripheral mononuclear blood cells. These findings suggest that changes in JNK activity provide a reliable biomarker for plitidepsin activity and this could be useful for designing clinical trials and maximizing the efficacy of plitidepsin.This work has been partially supported by grants (Programa Cenit, CEN-20091016, SAF2010-18302 and Fondo Europeo de Desarrollo Regional-Instituto de Salud Carlos III, RD12/0036/0021) from Ministerio de Economíay Competitividad of Spain.S

A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers

[EN] Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 degrees C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 degrees C and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Guillén-Navarro, MA.; Martínez-España, R.; Bueno-Crespo, A.; Morales-García, J.; Ayuso, B.; Cecilia-Canales, JM. (2020). A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers. Sensors. 20(24):1-15. https://doi.org/10.3390/s20247129S1152024Melgarejo-Moreno, J., López-Ortiz, M.-I., & Fernández-Aracil, P. (2019). Water distribution management in South-East Spain: A guaranteed system in a context of scarce resources. Science of The Total Environment, 648, 1384-1393. doi:10.1016/j.scitotenv.2018.08.263Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors, 18(6), 1731. doi:10.3390/s18061731Liaghat. (2010). A Review: The Role of Remote Sensing in Precision Agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50-55. doi:10.3844/ajabssp.2010.50.55Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., … Willenbockel, D. (2013). Agriculture and climate change in global scenarios: why don’t the models agree. Agricultural Economics, 45(1), 85-101. doi:10.1111/agec.12091Crookston, R. K. (2006). A Top 10 List of Developments and Issues Impacting Crop Management and Ecology During the Past 50 Years. Crop Science, 46(5), 2253-2262. doi:10.2135/cropsci2005.11.0416gasDutta, R., Morshed, A., Aryal, J., D’Este, C., & Das, A. (2014). Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environmental Modelling & Software, 52, 264-272. doi:10.1016/j.envsoft.2013.10.004Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. doi:10.1016/j.jhydrol.2018.04.065Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 53(5), 3878-3895. doi:10.1002/2016wr019933Coopersmith, E. J., Minsker, B. S., Wenzel, C. E., & Gilmore, B. J. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture, 104, 93-104. doi:10.1016/j.compag.2014.04.004Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214-225. doi:10.1016/j.compag.2015.08.008Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71-78. doi:10.1016/j.compag.2017.01.027Jin, X.-B., Yu, X.-H., Wang, X.-Y., Bai, Y.-T., Su, T.-L., & Kong, J.-L. (2020). Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4), 1433. doi:10.3390/su12041433Castañeda-Miranda, A., & Castaño-Meneses, V. M. (2020). Internet of things for smart farming and frost intelligent control in greenhouses. Computers and Electronics in Agriculture, 176, 105614. doi:10.1016/j.compag.2020.105614Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Jawad, H., Nordin, R., Gharghan, S., Jawad, A., & Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17(8), 1781. doi:10.3390/s17081781Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, 33(2). doi:10.1002/cpe.5299Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-

Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

[EN] The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 (AEI/FEDER, UE) and RTC-2017-6389-5.Guillén-Navarro, MA.; Llanes, A.; Imbernón, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, J.; Cecilia-Canales, JM. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77:818-840. https://doi.org/10.1007/s11227-020-03288-w8188407

Un programa de entrenamiento para familiares de pacientes con trastorno límite de la personalidad basado en la terapia dialéctica comportamental

Los familiares de pacientes con trastorno de la personalidad límite (TLP) experimentan ansiedad y depresión, y están inmersos en un clima familiar disfuncional. Aunque existen tratamientos eficaces para el TLP, se ha prestado menos atención a los familiares. El objetivo de este trabajo fue adaptar para familiares el entrenamiento grupal en habilidades de la terapia dialéctica comportamental, en 14 sesiones. La muestra estuvo formada por 12 familiares: 50% madres, 41,7% padres y 8,3% parejas. Se evaluaron los niveles de depresión (BDI-II), ansiedad (OASIS) y emoción expresada (LEE-S) antes y después de la intervención. Los resultados indican una mejoría estadística y clínicamente significativa en depresión, y clínicamente significativa en ansiedad. La actitud negativa hacia la enfermedad mejora estadísticamente, y la hostilidad, la baja tolerancia a la frustración y la emoción expresada total mejoran clínicamente. Por su parte, la intrusión empeora tanto estadística como clínicamente, sin embargo, sus niveles son similares a los de la población general tras la intervención. Resulta imprescindible seguir investigando sobre la efectividad de las intervenciones para familiares

PM060184, a new tubulin binding agent with potent antitumor activity including P-glycoprotein over-expressing tumors

PM060184 belongs to a new family of tubulin-binding agents originally isolated from the marine sponge Lithoplocamia lithistoides. This compound is currently produced by total synthesis and is under evaluation in clinical studies in patients with advanced cancer diseases. It was recently published that PM060184 presents the highest known affinities among tubulin-binding agents, and that it targets tubulin dimers at a new binding site. Here, we show that PM060184 has a potent antitumor activity in a panel of different tumor xenograft models. Moreover, PM060184 is able to overcome P-gp mediated resistance in vivo, an effect that could be related to its high binding affinity for tubulin. To gain insight into the mechanism responsible of the observed antitumor activity, we have characterized its molecular and cellular effects. We have observed that PM060184 is an inhibitor of tubulin polymerization that reduces microtubule dynamicity in cells by 59%. Interestingly, PM060184 suppresses microtubule shortening and growing at a similar extent. This action affects cells in interphase and mitosis. In the first case, the compound induces a disorganization and fragmentation of the microtubule network and the inhibition of cell migration. In the second case, it induces the appearance of multipolar mitosis and lagging chromosomes at the metaphase plate. These effects correlate with prometaphase arrest and induction of caspase-dependent apoptosis or appearance of cells in a multinucleated interphase-like state unrelated to classical apoptosis pathways. Taken together, these results indicate that PM060184 represents a new tubulin binding agent with promising potential as an anticancer agent.This work was supported by grants BIO2010-16351 (JFD), CAM S2010/BMD-2457 (JFD), CAM S2010/BMD-2353 (JMA), BFU2011-23416 (JMA) and PharmaMar-CSIC contracts. BP had a contract from Comunidad de Madrid

A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.

Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities. Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.Secretaría de Estado de Investigación, Desarrollo e InnovaciónInstituto de Salud Carlos II

Effect of a nutritional intervention on the intestinal microbiota of vertically HIV-infected children: The pediabiota study

This article belongs to the Special Issue Role of Prebiotics and Probiotics in Health and Disease.[Aims]: The gut microbiota exerts a critical influence in the immune system. The gut microbiota of human virus immunodeficiency (HIV)-infected children remains barely explored. We aimed to characterize the fecal microbiota in vertically HIV-infected children and to explore the effects of its modulation with a symbiotic nutritional intervention.[Methods]: A pilot, double blind, randomized placebo-controlled study including HIV-infected children who were randomized to receive a nutritional supplementation including prebiotics and probiotics or placebo for four weeks. HIV-uninfected siblings were recruited as controls. The V3–V4 region of the 16S rRNA gene was sequenced in fecal samples.[Results]: 22 HIV-infected children on antiretroviral therapy (ART) and with viral load (VL) <50/mL completed the follow-up period. Mean age was 11.4 ± 3.4 years, eight (32%) were male. Their microbiota showed reduced alpha diversity compared to controls and distinct beta diversity at the genus level (Adonis p = 0.042). Patients showed decreased abundance of commensals Faecalibacterium and an increase in Prevotella, Akkermansia and Escherichia. The nutritional intervention shaped the microbiota towards the control group, without a clear directionality.[Conclusions]: Vertical HIV infection is characterized by changes in gut microbiota structure, distinct at the compositional level from the findings reported in adults. A short nutritional intervention attenuated bacterial dysbiosis, without clear changes at the community level.[Summary]: In a group of 24 vertically HIV-infected children, in comparison to 11 uninfected controls, intestinal dysbiosis was observed despite effective ART. Although not fully effective to restore the microbiota, a short intervention with pre/probiotics attenuated bacterial dysbiosis.This work was funded by the Instituto de Salud Carlos III, Acción Estratégica en Salud (PI13/0422, ICI14/00207, PI17/01283, and PI18/00042) and by an agreement between the Instituto de Salud Carlos III and the Fundación Asociación Española contra el Cáncer within the ERANET TRANSCAN-2 program, grant number AC17/00022. CoRISpe is integrated in the Spanish AIDS Research Network (RIS), supported by the Instituto de Salud Carlos III (Grant nº RD06/0006/0034 and nº RD06/0006/0035). TS was funded by a 2014 Research Fellowship Award from the European Society of Pediatric Infectious Diseases (ESPID) and is now funded by the Spanish Ministry of Science and Innovation- Instituto de Salud Carlos III and Fondos FEDER (Contratos Juan Rodés R16/00021). Nutricion Médica NM, SL, manufactured and packaged the nutritional product under investigation.Peer reviewe

Prognostic factors of a lower CD4/CD8 ratio in long term viral suppression HIV infected children

CoRISpe (Cohorte Nacional de VIH pediátrica de la RED RIS).[Background] Combination antiretroviral therapy (cART) is associated with marked immune reconstitution. Although a long term viral suppression is achievable, not all children however, attain complete immunological recovery due to persistent immune activation. We use CD4/CD8 ratio like a marker of immune reconstitution.[Methods] Perinatal HIV-infected children who underwent a first-line cART, achieved viral suppression in the first year and maintained it for more than 5 years, with no viral rebound were included. Logistic models were applied to estimate the prognostic factors, clinical characteristics at cART start, of a lower CD4/CD8 ratio at the last visit.[Results] 146 HIV-infected children were included: 77% Caucasian, 45% male and 28% CDC C. Median age at cART initiation was 2.3 years (IQR: 0.5–6.2). 42 (30%) children received mono-dual therapy previously to cART. Time of undetectable viral load was 9.5 years (IQR: 7.8, 12.5). 33% of the children not achieved CD4/CD8 ratio >1. Univariate analysis showed an association between CD4/CD8 1 was not achieved in 33% of the children. Lower CD4 nadir and previous exposure to suboptimal therapy, before initiating cART, are factors showing independently association with a worse immune recovery (CD4/CD8 < 1).Peer reviewe

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