Dual effects of noradrenaline on astroglial production of chemokines and pro-inflammatory mediators

BACKGROUND: Noradrenaline (NA) is known to limit neuroinflammation. However, the previously described induction by NA of a chemokine involved in the progression of immune/inflammatory processes, such as chemokine (C-C motif) ligand 2 (CCL2)/monocyte chemotactic protein-1 (MCP-1), apparently contradicts NA anti-inflammatory actions. In the current study we analyzed NA regulation of astroglial chemokine (C-X3-C motif) ligand 1 (CX3CL1), also known as fractalkine, another chemokine to which both neuroprotective and neurodegenerative actions have been attributed. In addition, NA effects on other chemokines and pro-inflammatory mediators were also analyzed. METHODS: Primary astrocyte-enriched cultures were obtained from neonatal Wistar rats. These cells were incubated for different time durations with combinations of NA and lipopolysaccharide (LPS). The expression and synthesis of different proteins was measured by RT-PCR and enzyme-linked immunosorbent assay (ELISA) or enzyme immunoassays. Data were analyzed by one-way analysis of variance (ANOVA), followed by Newman-Keuls multiple comparison tests. RESULTS: The data presented here show that in control conditions, NA induces the production of CX3CL1 in rat cultured astrocytes, but in the presence of an inflammatory stimulus, such as LPS, NA has the opposite effect inhibiting CX3CL1 production. This inversion of NA effect was also observed for MCP-1. Based on the observation of this dual action, NA regulation of different chemokines and pro-inflammatory cytokines was also analyzed, observing that in most cases NA exerts an inhibitory effect in the presence of LPS. One characteristic exception was the induction of cyclooxygenase-2 (COX-2), where a summative effect was detected for both LPS and NA. CONCLUSION: These data suggest that NA effects on astrocytes can adapt to the presence of an inflammatory agent reducing the production of certain cytokines, while in basal conditions NA may have the opposite effect and help to maintain moderate levels of these cytokines

Origin and consequences of brain Toll-like receptor 4 pathway stimulation in an experimental model of depression

<p>Abstract</p> <p>Background</p> <p>There is a pressing need to identify novel pathophysiological pathways relevant to depression that can help to reveal targets for the development of new medications. Toll-like receptor 4 (TLR-4) has a regulatory role in the brain's response to stress. Psychological stress may compromise the intestinal barrier, and increased gastrointestinal permeability with translocation of lipopolysaccharide (LPS) from Gram-negative bacteria may play a role in the pathophysiology of major depression.</p> <p>Methods</p> <p>Adult male Sprague-Dawley rats were subjected to chronic mild stress (CMS) or CMS+intestinal antibiotic decontamination (CMS+ATB) protocols. Levels of components of the TLR-4 signaling pathway, of LPS and of different inflammatory, oxidative/nitrosative and anti-inflammatory mediators were measured by RT-PCR, western blot and/or ELISA in brain prefrontal cortex. Behavioral despair was studied using Porsolt's test.</p> <p>Results</p> <p>CMS increased levels of TLR-4 and its co-receptor MD-2 in brain as well as LPS and LPS-binding protein in plasma. In addition, CMS also increased interleukin (IL)-1β, COX-2, PGE₂and lipid peroxidation levels and reduced levels of the anti-inflammatory prostaglandin 15d-PGJ₂in brain tissue. Intestinal decontamination reduced brain levels of the pro-inflammatory parameters and increased 15d-PGJ₂, however this did not affect depressive-like behavior induced by CMS.</p> <p>Conclusions</p> <p>Our results suggest that LPS from bacterial translocation is responsible, at least in part, for the TLR-4 activation found in brain after CMS, which leads to release of inflammatory mediators in the CNS. The use of Gram-negative antibiotics offers a potential therapeutic approach for the adjuvant treatment of depression.</p

Benefits of ensemble models in road pavement cracking classification

The maintenance of road pavements is an essential task to prevent major deterioration and to reduce accident rates. In this task, the detection and classification of different types of cracks on the roads is usually considered. However, in most cases, these tasks are not fully automated and they need to be supervised by an expert to make repair decisions. This work focuses on the automatic classification of the most common types of cracks: longitudinal cracks, transverse cracks, and alligator cracks. Our proposal combines, first, computer vision techniques for crack segmentation and second, an ensemble model (composed of different rule-based algorithms) for the classification. This approach achieves an average precision and recall values greater than 94% for three analyzed data sets improving the results in comparison to other approaches

Tablets for access to electronic resources and for use as teaching support in the university library: A case study

The experience of the Library of the Faculty of Law of the Universidad Autónoma de Madrid (UAM) is presented, where patrons use tablets to access books that have been acquired in electronic format. In recent years, there has been an increasing number of titles that are being distributed in paper with an electronic copy attached (duo). These are characterized by the indivisibility of the pack and the increase in their price. This system encourages both the individual and private use of the copies by using a code associated with the user’s profile. The creation of a specific system based on the use of a tablet in order to provide the user public access to these copies is described. The system duplicates the number of copies available to users. In addition to access to e-books, a selection of electronic resources and programs previously offered by the University has been included in the tablets. The tablets are also configured for teaching uses, both for seminars and exams

Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

[EN] We are witnessing the dramatic consequences of the COVID¿19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID¿19 pandemic to create a decision support system for policy¿makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14¿day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14¿day CI in scenarios with and without trend changes, reaching 0.93 R2, 4.16 RMSE and 1.08 MAE.This work has been partially supported by the Spanish Ministry of Science and Innovation, under Grants RYC2018-025580-I, 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, by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302 and a predoctoral contract by the Generalitat Valenciana and the European Social Fund under Grant ACIF/2018/219.García-Cremades, S.; Morales-García, J.; Hernández-Sanjaime, R.; Martínez-España, R.; Bueno-Crespo, A.; Hernández-Orallo, E.; López-Espín, JJ.... (2021). Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data. Scientific Reports. 11(1):1-16. https://doi.org/10.1038/s41598-021-94696-2S11611

Structural design and particle size examination on NiO-CeO2 catalysts supported on 3D-printed carbon monoliths for CO2 methanation

3D-printed high-surface carbon monoliths have been fabricated and tested as catalyst supports of CO2 metha nation active phases (NiO-CeO2, 12 wt% Ni). The carbon carriers show a developed microporosity and good adherence to the catalytic phases of NiO-CeO2, showing great stability and cyclability. Two monolith designs were used: a conventional parallel-channeled structure (honeycomb) and a complex 3D network of non-linear channels built upon interconnected circular sections (circles), where flow turbulences along the reactant gas path are spurred. The effect of the active phases particle size on the catalyst distribution and the overall per formance has been assessed by comparing NiO-CeO2 nanoparticles of 7 nm verage (Np), with a reference counterpart of uncontrolled structure (Ref). The improved radial gases diffusion in the circles monolith design is confirmed, and nanoparticles show enhanced CO2 methanation activity than the uncontrolled-size active phase at low temperatures ( 300 ºC). SEM and Hg porosimetry evidence that nanoparticles are deposited at deeper penetration through the narrow micropores of the carbon matrix of the monolithic supports, which tend to accumulate on the channels surface remaining more accessible to the reactant molecules. Altogether, this study examines the impact of the channel tortuosity and the active phase sizing on the CO2 methanation activity, serving as ground knowledge for the further rational and scalable fabrication of carbon monolith for catalytic applications.The authors thank the financial support of the Spanish Ministry of Science and Innovation (Projects PID2019-105960RB-C22, TED2021-129216B-I00 and PDC2022-133839-C22), Generalitat Valenciana (Projects CIPROM/2021/74, MFA/2022/036), and the EU NextGener ation (PRTR-C17.I1). ADQ acknowledges the support from the Spanish Ministry of Science and Innovation (RYC2021-034791-I)

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-

The microbiome of the uropygial secretion in hoopoes is shaped along the nesting phase

Microbial symbiont acquisition by hosts may determine the effectiveness of the mutualistic relationships. A mix of vertical and horizontal transmission may be advantageous for hosts by allowing plastic changes of microbial communities depending on environmental conditions. Plasticity is well known for gut microbiota but is poorly understood for other symbionts of wild animals. We here explore the importance of environmental conditions experienced by nestling hoopoes (Upupa epops) during the late nesting phase determining microbiota in their uropygial gland. In cross-fostering experiments of 8 days old nestlings, “sibling-sibling” and “mother-offspring” comparisons were used to explore whether the bacterial community naturally established in the uropygial gland of nestlings could change depending on experimental environmental conditions (i.e., new nest environment). We found that the final microbiome of nestlings was mainly explained by nest of origin. Moreover, cross-fostered nestlings were more similar to their siblings and mothers than to their stepsiblings and stepmothers. We also detected a significant effect of nest of rearing, suggesting that nestling hoopoes acquire most bacterial symbionts during the first days of life but that the microbiome is dynamic and can be modified along the nestling period depending on environmental conditions. Estimated effects of nest of rearing, but also most of those of nest of origin are associated to environmental characteristics of nests, which are extended phenotypes of parents. Thus, natural selection may favor the acquisition of appropriated microbial symbionts for particular environmental conditions found in nests.Support by funding was provided by Spanish Ministerio de Economía y Competitividad, European funds (FEDER) (CGL2013-48193-C3-1-P, CGL2013-48193-C3-2-P), and Junta de Andalucía (P09-RNM-4557). AM-G had a predoctoral grant from the Junta de Andalucía (P09-RNM-4557).Peer reviewe

Intracellular inflammatory and antioxidant pathways in postmortem frontal cortex of subjects with major depression: effect of antidepressants

Background: Studies show that Toll-like receptors (TLRs), members of the innate immune system, might participate in the pathogenesis of the major depressive disorder (MDD). However, evidence of this participation in the brain of patients with MDD has been elusive. Methods: This work explores whether the protein expression by immunodetection assays (Western blot) of elements of TLR-4 pathways controlling inflammation and the oxidative/nitrosative stress are altered in postmortem dorsolateral prefrontal cortex of subjects with MDD. The potential modulation induced by the antidepressant treatment on these parameters was also assessed. Thirty MDD subjects (15 antidepressant-free and 15 under antidepressant treatment) were matched for gender and age to 30 controls in a paired design. Results: No significant changes in TLR-4 expression were detected. An increased expression of the TLR-4 endogenous ligand Hsp70 (+ 33%), but not of Hsp60, and the activated forms of mitogen-activated protein kinases (MAPKs) p38 (+ 47%) and JNK (+ 56%) was observed in MDD. Concomitantly, MDD subjects present a 45% decreased expression of DUSP2 (a regulator of MAPKs) and reduced (- 21%) expression of the antioxidant nuclear factor Nrf2. Antidepressant treatment did not modify the changes detected in the group with MDD and actually increased (+ 25%) the expression of p11, a protein linked with the transport of neurotransmitters and depression. Conclusion: Data indicate an altered TLR-4 immune response in the brain of subjects with MDD. Additional research focused on the mechanisms contributing to the antidepressant-induced TLR-4 pathway modulation is warranted and could help to develop new treatment strategies for MDD.This work was supported by the Instituto de Salud Carlos III and Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through the Plan Estatal de I+D+i 2013-2016 (FIS-PI13/01102 and SAF2016-75500-R to JCL), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) (SAF2017-83053-R to JRC), the Basque Government (IT-616-13), CIBERSAM and the EDR Funds. JRC and BGB are Ramon y Cajal fellows (MINECO)