1,153 research outputs found

    Renormalization group contraction of tensor networks in three dimensions

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    We present a new strategy for contracting tensor networks in arbitrary geometries. This method is designed to follow as strictly as possible the renormalization group philosophy, by first contracting tensors in an exact way and, then, performing a controlled truncation of the resulting tensor. We benchmark this approximation procedure in two dimensions against an exact contraction. We then apply the same idea to a three dimensional system. The underlying rational for emphasizing the exact coarse graining renormalization group step prior to truncation is related to monogamy of entanglement.Comment: 5 pages, 8 figure

    How Prosecutors and Defense Attorneys Differ in Their Use of Neuroscience Evidence

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    Much of the public debate surrounding the intersection of neuroscience and criminal law is based on assumptions about how prosecutors and defense attorneys differ in their use of neuroscience evidence. For example, according to some commentators, the defense’s use of neuroscience evidence will abdicate criminals of all responsibility for their offenses. In contrast, the prosecution’s use of that same evidence will unfairly punish the most vulnerable defendants as unfixable future dangers to society. This “double- edged sword” view of neuroscience evidence is important for flagging concerns about the law’s construction of criminal responsibility and punishment: it demonstrates that the same information about the defendant can either be mitigating or aggravating depending on who is raising it. Yet empirical assessments of legal decisions reveal a far more nuanced reality, showing that public beliefs about the impact of neuroscience on the criminal law can often be wrong. This Article takes an evidence-based and multidisciplinary approach to examining how courts respond to neuroscience evidence in capital cases when the defense presents it to argue that the defendant’s mental state at the time of the crime was below the given legal requisite due to some neurologic or cognitive deficiency

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

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    [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. Sensors. 20(9):1-16. https://doi.org/10.3390/s20092681S116209Beck, S., Wojdyla, D., Say, L., Pilar Bertran, A., Meraldi, M., Harris Requejo, J., … Van Look, P. (2010). The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bulletin of the World Health Organization, 88(1), 31-38. doi:10.2471/blt.08.062554Zeitlin, J., Szamotulska, K., Drewniak, N., Mohangoo, A., Chalmers, J., … Sakkeus, L. (2013). Preterm birth time trends in Europe: a study of 19 countries. BJOG: An International Journal of Obstetrics & Gynaecology, 120(11), 1356-1365. doi:10.1111/1471-0528.12281Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. The Lancet, 371(9606), 75-84. doi:10.1016/s0140-6736(08)60074-4Petrou, S. (2005). The economic consequences of preterm birth duringthe first 10 years of life. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 10-15. doi:10.1111/j.1471-0528.2005.00577.xLucovnik, M., Chambliss, L. R., & Garfield, R. E. (2013). Costs of unnecessary admissions and treatments for «threatened preterm labor». American Journal of Obstetrics and Gynecology, 209(3), 217.e1-217.e3. doi:10.1016/j.ajog.2013.06.046Haas, D., Benjamin, T., Sawyer, R., & Quinney, S. (2014). Short-term tocolytics for preterm delivery &ndash; current perspectives. International Journal of Women’s Health, 343. doi:10.2147/ijwh.s44048Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Devedeux, D., Marque, C., Mansour, S., Germain, G., & Duchêne, J. (1993). Uterine electromyography: A critical review. American Journal of Obstetrics and Gynecology, 169(6), 1636-1653. doi:10.1016/0002-9378(93)90456-sChkeir, A., Fleury, M.-J., Karlsson, B., Hassan, M., & Marque, C. (2013). Patterns of electrical activity synchronization in the pregnant rat uterus. BioMedicine, 3(3), 140-144. doi:10.1016/j.biomed.2013.04.007Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., & Jager, F. (2008). A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yHoroba, K., Jezewski, J., Matonia, A., Wrobel, J., Czabanski, R., & Jezewski, M. (2016). Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybernetics and Biomedical Engineering, 36(4), 574-583. doi:10.1016/j.bbe.2016.06.004Vinken, M. P. G. C., Rabotti, C., Mischi, M., & Oei, S. G. (2009). Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery. Obstetrical & Gynecological Survey, 64(8), 529-541. doi:10.1097/ogx.0b013e3181a8c6b1Vrhovec, J., Macek-Lebar, A., & Rudel, D. (s. f.). Evaluating Uterine Electrohysterogram with Entropy. IFMBE Proceedings, 144-147. doi:10.1007/978-3-540-73044-6_36Diab, A., Hassan, M., Marque, C., & Karlsson, B. (2014). Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals. Medical Engineering & Physics, 36(6), 761-767. doi:10.1016/j.medengphy.2014.01.009Lemancewicz, A., Borowska, M., Kuć, P., Jasińska, E., Laudański, P., Laudański, T., & Oczeretko, E. (2016). Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (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). Low-complexity intrauterine pressure estimation using the Teager energy operator on electrohysterographic recordings. Physiological Measurement, 35(7), 1215-1228. doi:10.1088/0967-3334/35/7/1215Ahmed, M., Chanwimalueang, T., Thayyil, S., & Mandic, D. (2016). A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis. Entropy, 19(1), 2. doi:10.3390/e19010002Karmakar, C. K., Khandoker, A. H., Gubbi, J., & Palaniswami, M. (2009). Complex Correlation Measure: a novel descriptor for Poincaré plot. BioMedical Engineering OnLine, 8(1), 17. doi:10.1186/1475-925x-8-17Roy, B., & Ghatak, S. (2013). Nonlinear Methods to Assess Changes in Heart Rate Variability in Type 2 Diabetic Patients. Arquivos Brasileiros de Cardiologia. doi:10.5935/abc.20130181Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. 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

    Cerebrospinal fluid Presenilin-1 increases at asymptomatic stage in genetically determined Alzheimer's disease

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    BACKGROUND: Presenilin-1 (PS1), the active component of the intramembrane γ-secretase complex, can be detected as soluble heteromeric aggregates in cerebrospinal fluid (CSF). The aim of this study was to examine the different soluble PS1 complexes in the lumbar CSF (CSF-PS1) of individuals with Alzheimer’s disease (AD), particularly in both symptomatic and asymptomatic genetically determined AD, in order to evaluate their potential as early biomarkers. METHODS: Western blotting, differential centrifugation and co-immunoprecipitation served to determine and characterize CSF-PS1 complexes. We also monitored the assembly of soluble PS1 into complexes in a cell model, and the participation of Aβ in the dynamics and robustness of the stable PS1 complexes. RESULTS: There was an age-dependent increase in CSF-PS1 levels in cognitively normal controls, the different complexes represented in similar proportions. The total levels of CSF-PS1, and in particular the proportion of the stable 100–150 kDa complexes, increased in subjects with autosomal dominant AD that carried PSEN1 mutations (eight symptomatic and six asymptomatic ADAD) and in Down syndrome individuals (ten demented and ten non-demented DS), compared with age-matched controls (n = 23), even prior to the appearance of symptoms of dementia. The proportion of stable CSF-PS1 complexes also increased in sporadic AD (n = 13) and mild-cognitive impaired subjects (n = 12), relative to age-matched controls (n = 17). Co-immunoprecipitation demonstrated the association of Aβ oligomers with soluble PS1 complexes, particularly the stable complexes. CONCLUSIONS: Our data suggest that CSF-PS1 complexes may be useful as an early biomarker for AD, reflecting the pathology at asymptomatic state

    Levels of ADAM10 are reduced in Alzheimer's disease CSF

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    BACKGROUND: The disintegrin metalloproteinase 10 (ADAM10) is the main α-secretase acting in the non-amyloidogenic processing of the amyloid precursor protein. This study assesses whether ADAM10 is present in cerebrospinal fluid (CSF), and whether it has potential as a biomarker for Alzheimer’s disease (AD). METHODS: ADAM10 was characterized in human CSF samples by immunoprecipitation and western blotting using antibodies specific for different domains of the protein and by ultracentrifugation in sucrose density gradients. Samples from AD patients (n = 20) and age-matched non-AD controls (n = 20) were characterized for classical CSF biomarkers, Aβ42, T-tau, or P-tau by ELISA, and assayed for soluble ADAM10 levels by western blotting. RESULTS: We found that ADAM10 is present in human CSF as several distinct species: an immature form retaining the prodomain (proADAM10; ~ 80 kDa), a mature unprocessed full-length form (ADAM10f; ~ 55 kDa), and a truncated large soluble form released from the membrane (sADAM10; ~ 50 kDa). Fractionation by ultracentrifugation on sucrose density gradients showed that the ADAM10f and sADAM10 species form large complexes. Immunoblotting revealed a significant decrease in ADAM10f and sADAM10 in AD CSF compared to control CSF, while proADAM10 levels remained unaltered. CONCLUSIONS: Several forms of ADAM10 are present in CSF, mainly assembled as high-molecular weight complexes. The determination of the levels of mature forms of CSF-ADAM10 may be useful as a biomarker for AD

    Inhibition of gamma-Secretase Leads to an Increase in Presenilin-1

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    γ-Secretase inhibitors (GSIs) are potential therapeutic agents for Alzheimer’s disease (AD); however, trials have proven disappointing. We addressed the possibility that γ-secretase inhibition can provoke a rebound effect, elevating the levels of the catalytic γ-secretase subunit, presenilin-1 (PS1). Acute treatment of SH-SY5Y cells with the GSI LY-374973 (N-[N-(3,5-difluorophenacetyl)-l-alanyl]-S-phenylglycine t-butyl ester, DAPT) augments PS1, in parallel with increases in other γ-secretase subunits nicastrin, presenilin enhancer 2, and anterior pharynx-defective 1, yet with no increase in messenger RNA expression. Over-expression of the C-terminal fragment (CTF) of APP, C99, also triggered an increase in PS1. Similar increases in PS1 were evident in primary neurons treated repeatedly (4 days) with DAPT or with the GSI BMS-708163 (avagacestat). Likewise, rats examined after 21 days administered with avagacestat (40 mg/kg/day) had more brain PS1. Sustained γ-secretase inhibition did not exert a long-term effect on PS1 activity, evident through the decrease in CTFs of APP and ApoER2. Prolonged avagacestat treatment of rats produced a subtle impairment in anxiety-like behavior. The rebound increase in PS1 in response to GSIs must be taken into consideration for future drug development

    Conformational changes and protein stability of the pro-apoptotic protein Bax

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    Pro-apoptotic Bax is a soluble and monomeric protein under normal physiological conditions. Upon its activation substantial structural rearrangements occur: The protein inserts into the mitochondrial outer membrane and forms higher molecular weight oligomers. Subsequently, the cells can undergo apoptosis. In our studies, we focused on the structural rearrangements of Bax during oligomerization and on the protein stability. Both protein conformations exhibit high stability against thermal denaturation, chemically induced unfolding and proteolytic processing. The oligomeric protein is stable up to 90 °C as well as in solutions of 8 M urea or 6 M guanidinium hydrochloride. Helix 9 appears accessible in the monomer but hidden in the oligomer assessed by proteolysis. Tryptophan fluorescence indicates that the environment of the C-terminal protein half becomes more apolar upon oligomerization, whereas the loop region between helices 1 and 2 gets solvent exposed

    Analysis of relevant technical issues and deficiencies of the existing sensors and related initiatives currently set and working in marine environment. New generation technologies for cost-effective sensors

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    The last decade has seen significant growth in the field of sensor networks, which are currently collecting large amounts of environmental data. This data needs to be collected, processed, stored and made available for analysis and interpretation in a manner which is meaningful and accessible to end users and stakeholders with a range of requirements, including government agencies, environmental agencies, the research community, industry users and the public. The COMMONSENSE project aims to develop and provide cost-effective, multi-functional innovative sensors to perform reliable in-situ measurements in the marine environment. The sensors will be easily usable across several platforms, and will focus on key parameters including eutrophication, heavy metal contaminants, marine litter (microplastics) and underwater noise descriptors of the MSFD. The aims of Tasks 2.1 and 2.2 which comprise the work of this deliverable are: • To obtain a comprehensive understanding and an up-to-date state of the art of existing sensors. • To provide a working basis on “new generation” technologies in order to develop cost-effective sensors suitable for large-scale production. This deliverable will consist of an analysis of state-of-the-art solutions for the different sensors and data platforms related with COMMONSENSE project. An analysis of relevant technical issues and deficiencies of existing sensors and related initiatives currently set and working in marine environment will be performed. Existing solutions will be studied to determine the main limitations to be considered during novel sensor developments in further WP’s. Objectives & Rationale The objectives of deliverable 2.1 are: • To create a solid and robust basis for finding cheaper and innovative ways of gathering data. This is preparatory for the activities in other WPs: for WP4 (Transversal Sensor development and Sensor Integration), for WP(5-8) (Novel Sensors) to develop cost-effective sensors suitable for large-scale production, reducing costs of data collection (compared to commercially available sensors), increasing data access availability for WP9 (Field testing) when the deployment of new sensors will be drawn and then realized
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