Laser-driven direct synthesis of carbon nanodots and application as sensitizers for visible-light photocatalysis

We present the first successful synthesis of monodisperse carbon nanodots (CNDs) with tunable photoluminescence (PL) carried out by laser pyrolysis of two common volatile organic precursors such as toluene and pyridine. Remarkably, the initial chemical composition of the precursor determines the formation of undoped or N-doped CNDs and their corresponding absorption response in the visible range (expanded for the latter). We demonstrate the control and versatility of this synthesis method to tune the final outcome and its potential to explore a great number of potential solvent candidates. Furthermore, we have successfully exploited these CNDs (both undoped and N-doped) as effective sensitizers of TiO2 nanoparticles in the visible-light driven photo-degradation of a cationic dye selected as model organic pollutant

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

[EN] Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 +/- 4.3% and 76.2 +/- 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and by the Generalitat Valenciana (AICO/2019/220).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Monfort-Ortiz, R.; Martinez-Saez, C.; Perales, A.... (2020). Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. 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(2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Fergus, P., Idowu, I., Hussain, A., & Dobbins, C. (2016). Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing, 188, 42-49. doi:10.1016/j.neucom.2015.01.107Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013Fergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., & Iram, S. (2013). Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. PLoS ONE, 8(10), e77154. doi:10.1371/journal.pone.0077154Ren, P., Yao, S., Li, J., Valdes-Sosa, P. A., & Kendrick, K. M. (2015). Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLOS ONE, 10(7), e0132116. doi:10.1371/journal.pone.0132116Degbedzui, D. K., & Yüksel, M. E. (2020). Accurate diagnosis of term–preterm births by spectral analysis of electrohysterography signals. Computers in Biology and Medicine, 119, 103677. doi:10.1016/j.compbiomed.2020.103677Borowska, M., Brzozowska, E., Kuć, P., Oczeretko, E., Mosdorf, R., & Laudański, P. (2018). Identification of preterm birth based on RQA analysis of electrohysterograms. Computer Methods and Programs in Biomedicine, 153, 227-236. doi:10.1016/j.cmpb.2017.10.018Vinken, M. P. G. C., Rabotti, C., Mischi, M., van Laar, J. O. E. H., & Oei, S. G. (2010). Nifedipine-Induced Changes in the Electrohysterogram of Preterm Contractions: Feasibility in Clinical Practice. Obstetrics and Gynecology International, 2010, 1-8. doi:10.1155/2010/325635Mas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yBradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi:10.1016/s0031-3203(96)00142-2Alexandersson, A., Steingrimsdottir, T., Terrien, J., Marque, C., & Karlsson, B. (2015). The Icelandic 16-electrode electrohysterogram database. Scientific Data, 2(1). doi:10.1038/sdata.2015.17Maner, W. (2003). Predicting term and preterm delivery with transabdominal uterine electromyography. Obstetrics & Gynecology, 101(6), 1254-1260. doi:10.1016/s0029-7844(03)00341-7Maner, W. L., & Garfield, R. E. (2007). Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola-Rubio, J., Perales, A., & Ye-Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/5373810Terrien, J., Marque, C., & Karlsson, B. (2007). Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2007.4352680Rooijakkers, M. J., Rabotti, C., Oei, S. G., Aarts, R. M., & Mischi, M. (2014). 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Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Kl. Comparison between Using Linear and Non-linear Features to Classify Uterine Electromyography Signals of Term and Preterm Deliveries. (2013). 2013 30th National Radio Science Conference (NRSC). doi:10.1109/nrsc.2013.6587953Aditya, S., & Tibarewala, D. N. (2012). Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data. International Journal of Artificial Intelligence and Soft Computing, 3(2), 143. doi:10.1504/ijaisc.2012.049010Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Li, Z., Zhang, Q., & Zhao, X. (2017). Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries. International Journal of Distributed Sensor Networks, 13(9), 155014771773339. doi:10.1177/1550147717733391Murthy, H. S. N., & Meenakshi, D. M. (2015). ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia- A Comparison. Bonfring International Journal of Research in Communication Engineering, 5(2), 07-11. doi:10.9756/bijrce.8030Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-153. doi:10.1016/j.knosys.2011.07.016Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: International Journal of Forecasting, 14(1), 35-62. doi:10.1016/s0169-2070(97)00044-7Lawrence, S., & Giles, C. L. (2000). Overfitting and neural networks: conjugate gradient and backpropagation. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. doi:10.1109/ijcnn.2000.857823Diab, A., Hassan, M., Boudaoud, S., Marque, C., & Karlsson, B. (2013). Nonlinear estimation of coupling and directionality between signals: Application to uterine EMG propagation. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2013.6610513Most, O., Langer, O., Kerner, R., Ben David, G., & Calderon, I. (2008). Can myometrial electrical activity identify patients in preterm labor? American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Mas-Cabo, J., Prats-Boluda, G., Ye-Lin, Y., Alberola-Rubio, J., Perales, A., & Garcia-Casado, J. (2019). Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomedical Signal Processing and Control, 52, 198-205. doi:10.1016/j.bspc.2019.04.001You, J., Kim, Y., Seok, W., Lee, S., Sim, D., Park, K. S., & Park, C. (2019). Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor. Journal of Electrical Engineering & Technology, 14(2), 897-916. doi:10.1007/s42835-019-00118-9Schuit, E., Scheepers, H., Bloemenkamp, K., Bolte, A., Duvekot, H., … van Eyck, J. (2015). Predictive Factors for Delivery within 7 Days after Successful 48-Hour Treatment of Threatened Preterm Labor. American Journal of Perinatology Reports, 05(02), e141-e149. doi:10.1055/s-0035-1552930Liao, J. B., Buhimschi, C. S., & Norwitz, E. R. (2005). Normal Labor: Mechanism and Duration. Obstetrics and Gynecology Clinics of North America, 32(2), 145-164. doi:10.1016/j.ogc.2005.01.001Ye-Lin, Y., Garcia-Casado, J., Prats-Boluda, G., Alberola-Rubio, J., & Perales, A. (2014). Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions. Computational and Mathematical Methods in Medicine, 2014, 1-11. doi:10.1155/2014/47078

Subsidence and thermal history of an inverted Late Jurassic-Early Cretaceous extensional basin (Cameros, North-central Spain) affected by very low- to low-grade metamorphism.

The Cameros Basin (North Spain) is a Late Jurassic-Early Cretaceous extensional basin, which was inverted during the Cenozoic. It underwent a remarkable thermal evolution, as indicated by the record of anomalous high temperatures in its deposits. In this work the subsidence and thermal history of the basin is reconstructed, using subsidence analysis and 2D thermal modeling. Tectonic subsidence curves provide evidence of the occurrence of two rapid subsidence phases during the syn-extensional stage. In the first phase (Tithonian-Early Berriasian), the largest accommodation space was formed in the central sector of the basin, whereas in the second (Early Barremian-Early Albian), it was formed in the northern sector. These rapid subsidence phases could correspond to relevant tectonic events affecting the Iberian Plate at that time. By distinguishing between the initial and thermal subsidence and defining their relative magnitudes, Royden's (1986) method was used to estimate the heat flow at the end of the extensional stage. A maximum heat flow of 60-65 mW/m2 is estimated, implying only a minor thermal disturbance associated with extension. In contrast with these data, very high vitrinite reflectance, anomalously distributed in some case with respect to the typical depth-vitrinite reflectance relation, was measured in the central-northern sector of the basin. Burial and thermal data are used to construct a 2D thermal basin model, to elucidate the role of the processes involved in sediment heating. Calibration of the thermal model with the vitrinite reflectance (%Ro) and fluid inclusion (FI) data indicates that in the central and northern sectors of the basin, an extra heat source, other than a typical rift, is required to explain the observed thermal anomalies. The distribution of the %Ro and FI values in these sectors suggests that the high temperatures and their distribution are related to the circulation of hot fluids. Hot fluids were attributed to the hydrothermal metamorphic events affecting the area during the early post-extensional and inversion stages of the basin

Tree-based ensembles unveil the microhabitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L.): Introducing XGBoost to eco-informatics

[EN] Random Forests (RFs) and Gradient Boosting Machines (GBMs) are popular approaches for habitat suitability modelling in environmental flow assessment. However, both present some limitations theoretically solved by alternative tree-based ensemble techniques (e.g. conditional RFs or oblique RFs). Among them, eXtreme Gradient Boosting machines (XGBoost) has proven to be another promising technique that mixes subroutines developed for RFs and GBMs. To inspect the capabilities of these alternative techniques, RFs and GBMs were compared with: conditional RFs, oblique RFs and XGBoost by modelling, at the micro-scale, the habitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L). XGBoost outperformed the other approaches, particularly conditional and oblique RFs, although there were no statistical differences with standard RFs and GBMs. The partial dependence plots highlighted the lacustrine origins of pumpkinseed and the preference for lentic habitats of bleak. However, the latter depicted a larger tolerance for rapid microhabitats found in run-type river segments, which is likely to hinder the management of flow regimes to control its invasion. The difference in the computational burden and, especially, the characteristics of datasets on microhabitat use (low data prevalence and high overlapping between categories) led us to conclude that, in the short term, XGBoost is not destined to replace properly optimised RFs and GBMs in the process of habitat suitability modelling at the micro-scale.This project had the support of Fundacion Biodiversidad, of Spanish Ministry for Ecological Transition. We want to thank the volunteering students of the Universitat Politecnica de Valencia, Marina de Miguel, Carlos A. Puig-Mengual, Cristina Barea, Rares Hugianu, and Pau Rodriguez. R. Munoz-Mas benefitted from a postdoctoral Juan de la Cierva fellowship from the Spanish Ministry of Science, Innovation and Universities (ref. FJCI-2016-30829). This research was supported by the Government of Catalonia (ref. 2017 SGR 548).Muñoz-Mas, R.; Gil-Martínez, E.; Oliva-Paterna, FJ.; Belda, E.; Martinez-Capel, F. (2019). Tree-based ensembles unveil the microhabitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L.): Introducing XGBoost to eco-informatics. Ecological Informatics. 53:1-12. https://doi.org/10.1016/j.ecoinf.2019.100974S1125

Identification of EP300 as a Key Gene Involved in Antipsychotic-Induced Metabolic Dysregulation Based on Integrative Bioinformatics Analysis of Multi-Tissue Gene Expression Data

Antipsychotics (APs) are associated with weight gain and other metabolic abnormalities such as hyperglycemia, dyslipidemia and metabolic syndrome. This translational study aimed to uncover the underlying molecular mechanisms and identify the key genes involved in AP-induced metabolic effects. An integrative gene expression analysis was performed in four different mouse tissues (striatum, liver, pancreas and adipose) after risperidone or olanzapine treatment. The analytical approach combined the identification of the gene co-expression modules related to AP treatment, gene set enrichment analysis and protein-protein interaction network construction. We found several co-expression modules of genes involved in glucose and lipid homeostasis, hormone regulation and other processes related to metabolic impairment. Among these genes, EP300, which encodes an acetyltransferase involved in transcriptional regulation, was identified as the most important hub gene overlapping the networks of both APs. Then, we explored the genetically predicted EP300 expression levels in a cohort of 226 patients with first-episode psychosis who were being treated with APs to further assess the association of this gene with metabolic alterations. The EP300 expression levels were significantly associated with increases in body weight, body mass index, total cholesterol levels, low-density lipoprotein cholesterol levels and triglyceride concentrations after 6 months of AP treatment. Taken together, our analysis identified EP300 as a key gene in AP-induced metabolic abnormalities, indicating that the dysregulation of EP300 function could be important in the development of these side effects. However, more studies are needed to disentangle the role of this gene in the mechanism of action of APs. Keywords: EP300; antipsychotics; gene; gene expression; metabolic syndrome; microarray; pharmacogenetics; weight gain

Exploring the key drivers of riparian woodland successional pathways across three European river reaches

"This is the peer reviewed version of the following article: Muñoz-Mas, R., V. Garófano-Gómez, I. Andrés-Doménech, D. Corenblit, G. Egger, F. Francés, M.T. Ferreira, et al. 2017. ¿Exploring the Key Drivers of Riparian Woodland Successional Pathways across Three European River Reaches.¿ Ecohydrology 10 (8). Wiley: e1888. doi:10.1002/eco.1888, which has been published in final form at https://doi.org/10.1002/eco.1888. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Climate change and river regulation are negatively impacting riparian vegetation. To evaluate these impacts, process-based models are preferred over data-driven approaches. However, they require extensive knowledge about ecohydrological processes. To facilitate the implementation of such process-based models, the key drivers of riparian woodland successional pathways across three river reaches, in Austria, Portugal, and Spain, were explored, employing two complementary approaches. The principal component analyses highlighted the importance of the physical gradients determining the placement of the succession phases within the riparian and floodplain zones. The generalized additive models revealed that the initial and pioneer succession phases, characteristic of the colonization stage, appeared in areas highly morphodynamic, close in height and distance to the water table, and with coarse substrate, whereas elder phases within the transitional and mature stages showed incremental differences, occupying less dynamic areas with finer substrate. The Austrian site fitted well the current successional theory (elder phases appearing sequentially further up and distant), but at the Portuguese site, the tolerance of the riparian species to drought and flash flood events governed their placement. Finally, at the Spanish site, the patchy distribution of the elder phases was the remnants of formative events that reshaped the river channel. These results highlight the complex relationships between flow regime, channel morphology, and riparian vegetation. The use of succession phases, which rely on the sequential evolution of riparian vegetation as a response to different drivers, may be potentially better reproducible, within numerical process-based models, and transferable to other geographical regions.This work was supported by the IWRM Era-NET Funding Initiative through the RIPFLOW project (references ERACCT-2005-026025, ERA-IWRM/0001/2008, CGL2008-03076-E/BTE), http://www.old.iwrm-net.eu/spip.php, by the Spanish Ministry of Economy and Competitiveness through the project SCARCE (Consolider¿Ingenio 2010 CSD2009-00065), and by the project ¿Natural and anthropogenic changes in Mediterranean river drainage basins: historical impacts on rivers morphology, sedimentary flows and vegetation¿ of the Spanish MINECO (CGL2013-44917-R). Virginia Garófano-Gómez received a postdoctoral grant from the Université Blaise Pascal (now: Université Clermont Auvergne). Rui Rivaes benefited from a PhD grant (SFRH/BD/52515/2014) sponsored by Fundação para a Ciência e Tecnologia (FCT) under the FCT PhD programme FLUVIO¿River Restoration and Management. Patricia María Rodríguez González was funded by FCT through an SFRH/BPD/47140/2008 postdoctoral fellowship and through an FCT Investigator Programme grant (IF/00059/2015). The authors also thank all the colleagues and master students who contributed enthusiastically to the field campaigns of this study.Muñoz Mas, R.; Garófano-Gómez, V.; Andrés Doménech, I.; Corenblit, D.; Egger, G.; Francés, F.; Ferreira, M.... (2017). Exploring the key drivers of riparian woodland successional pathways across three European river reaches. Ecohydrology. 10(8):1-19. https://doi.org/10.1002/eco.1888S11910

The contribution of qualitative research within the PRECISE study in sub-Saharan Africa.

The PRECISE Network is a cohort study established to investigate hypertension, fetal growth restriction and stillbirth (described as "placental disorders") in Kenya, Mozambique and The Gambia. Several pregnancy or birth cohorts have been set up in low- and middle-income countries, focussed on maternal and child health. Qualitative research methods are sometimes used alongside quantitative data collection from these cohorts. Researchers affiliated with PRECISE are also planning to use qualitative methods, from the perspective of multiple subject areas. This paper provides an overview of the different ways in which qualitative research methods can contribute to achieving PRECISE's objectives, and discusses the combination of qualitative methods with quantitative cohort studies more generally.We present planned qualitative work in six subject areas (health systems, health geography, mental health, community engagement, the implementation of the TraCer tool, and respectful maternity care). Based on these plans, with reference to other cohort studies on maternal and child health, and in the context of the methodological literature on mixed methods approaches, we find that qualitative work may have several different functions in relation to cohort studies, including informing the quantitative data collection or interpretation. Researchers may also conduct qualitative work in pursuit of a complementary research agenda. The degree to which integration between qualitative and quantitative methods will be sought and achieved within PRECISE remains to be seen. Overall, we conclude that the synergies resulting from the combination of cohort studies with qualitative research are an asset to the field of maternal and child health

Increased ventral striatal volume in college-aged binge drinkers

BACKGROUND Binge drinking is a serious public health issue associated with cognitive, physiological, and anatomical differences from healthy individuals. No studies, however, have reported subcortical grey matter differences in this population. To address this, we compared the grey matter volumes of college-age binge drinkers and healthy controls, focusing on the ventral striatum, hippocampus and amygdala. METHOD T1-weighted images of 19 binge drinkers and 19 healthy volunteers were analyzed using voxel-based morphometry. Structural data were also covaried with Alcohol Use Disorders Identification Test (AUDIT) scores. Cluster-extent threshold and small volume corrections were both used to analyze imaging data. RESULTS Binge drinkers had significantly larger ventral striatal grey matter volumes compared to controls. There were no between group differences in hippocampal or amygdalar volume. Ventral striatal, amygdalar, and hippocampal volumes were also negatively related to AUDIT scores across groups. CONCLUSIONS Our findings stand in contrast to the lower ventral striatal volume previously observed in more severe forms of alcohol use disorders, suggesting that college-age binge drinkers may represent a distinct population from those groups. These findings may instead represent early sequelae, compensatory effects of repeated binge and withdrawal, or an endophenotypic risk factor