149 research outputs found

    An investigation into the archaeological application of carbon stable isotope analysis used to establish crop water availability: solutions and ways forward

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    Carbon stable isotope analysis of charred cereal remains is a relatively new method employed by archaeological scientists to investigate ancient climate and irrigation regimes. The aim of this study was to assess the effect of environmental variables on carbon isotope discrimination (D) in multiple environments to develop the technique and its archaeological application, using crops grown at three experimental stations in Jordan. There are two key results: (1) as expected, there was a strong positive relationship between water availability and D; (2) site, not water input, was the most important factor in determining D. Future work should concentrate on establishing ways of correcting D for the influence of site specific environmental variables and on assessing how well carbon isotope discrimination values are preserved within the archaeological record

    Związek między inwazyjnością gruczolaków przysadki a indeksem proliferacyjnym mierzonym immunoekspresją topoizomerazy IIα

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    Introduction: Cavernous sinus invasion by pituitary adenoma affects surgical procedure radicality and consequently the postoperative course and prognosis in pituitary adenoma treatment. The search for pituitary adenoma aggressive behaviour markers is still a matter of debate. Material and methods: This study evaluates the relation of pituitary adenoma invasiveness to the expression of topoisomerase IIα in 72 patients who underwent transsphenoidal pituitary surgery. The assessment of tumour growth was conducted according to the Hardy scale as modified by Wilson and the Knosp scale. Topoisomerase IIα expression in tumour specimens was evaluated using immunohistochemical staining. Results: There was a correlation between the Knosp scale degree and the topoisomerase IIα expression (Spearman R = 0.3611, p &lt; 0.005). The Kruskal-Wallis H test (p = 0.0034) showed that there was a statistically significant topoisomerase IIα expression increase in tumours classified as grade E on the Hardy scale. The topoisomerase IIα expression correlated also with tumour size (Spearman R = 0.4117, p &lt; 0.001). Higher levels of expression were observed in macroadenomas, as compared to microadenomas (p &lt; 0.05, Mann-Whitney test). Topoisomerase IIα expression correlated with cavernous sinus invasion. Conclusions: The topoisomerase IIα expression correlated more with invasiveness than with extensiveness, which might make it an eminently useful marker in the assessment of aggressive pituitary adenoma behaviour.Introduction. Cavernous sinus invasion by pituitary adenoma affects surgical procedure radicality, and consequently the postoperative course and prognosis in pituitary adenomas treatment. The search for pituitary adenoma aggressive behaviour markers is still a matter of debate. Material and methods. This study evaluates the relation of pituitary adenoma invasiveness to the expression of topoisomerase IIα in 72 patients who underwent transsphenoidal pituitary surgery. The assessment of tumour growth was conducted according to the Hardy scale as modified by Wilson and the Knosp scale. Topoisomerase IIα expression in tumours specimens was evaluated using immunohistochemical staining. Results. There was a correlation between the Knosp scale degree and the topoisomerase IIα expression (Spearman R=0.3611, p < 0.005). The Kruskal-Wallis H test (p=0.0034) showed that there was a statistically significant topoisomerase IIα expression increase in tumours classified as grade E on the Hardy scale. The topoisomerase IIα expression correlated also with tumour size (Spearman R=0.4117, p < 0.001). Higher levels of expression were observed in macroadenomas, as compared to microadenomas (p < 0.05, Mann-Whitney test). Topoisomerase IIα expression correlated with cavernous sinus invasion. Conclusions. The topoisomerase IIα expression correlated more with invasiveness than with extensiveness, which might make it an eminently useful marker in the assessment of aggressive pituitary adenoma behaviour

    Ocena skuteczności profilaktyki jodowej w ciąży — analiza przeprowadzona w jednym z referencyjnych ośrodków ginekologiczno-położniczych w Polsce

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      Introduction: Iodine deficiency in pregnant women, even of a mild degree, may have adverse effects on both the mother and the foetus. Despite the obligatory model of functioning iodine prophylaxis in Poland, the iodine supply in women during pregnancy and physiological lactation is insufficient. Therefore, those groups should take additional iodine supplementation at a dose of 150–200 μg/day. The aim of this study was to examine the effectiveness of iodine prophylaxis in pregnant women in Poland. Material and methods: The assessment of iodine supply, urine iodine concentration (UIC) in the spot urine sample, as well as levels of TSH, fT4, thyroid antibodies, and thyroid volume, was performed at one time point in 115 women (7 in the 1st trimester, 61 in the 2nd trimester, and 47 in the 3rd trimester). Results: Only 45.2% of women were taking additional amounts of iodine at any time of pregnancy, and the median ioduria was 79.6 μg/L, which pointed to an insufficient supply of iodine. The percentage of women using iodine supplementation increased with the length of pregnancy, which indicates that the recommendations are implemented too late. In women who took iodine supplementation, ioduria was significantly higher than in those not applying iodine supplementation (median 129.4 μg/L vs. 73.0 μg/L; p &lt; 0.001); however, this was still below recommended values. Conclusions: The effectiveness of iodine prophylaxis in pregnant women in Poland, evaluated on the basis of the analysis of randomly chosen sample, is not satisfactory in terms of compliance with the recommendations and, possibly, the quality of supplementation. (Endokrynol Pol 2015; 66 (5): 404–411)    Wstęp: Niedobór jodu u kobiet w ciąży, nawet łagodnego stopnia, może powodować niekorzystne następstwa zarówno u matki, jak i u płodu. Mimo obligatoryjnego modelu profilaktyki jodowej funkcjonującego w Polsce, podaż jodu u kobiet w okresie ciąży i fizjologicznej laktacji jest niewystarczająca. Dlatego osoby te powinny przyjmować dodatkową suplementację jodu w dawce 150–200 μg/dobę. Celem pracy była ocena skuteczności profilaktyki jodowej u kobiet w ciąży w Polsce na podstawie analizy podaży jodu oraz jodurii. Materiał i metody: Oceniono w jednym punkcie czasowym podaż jodu, stężenie jodu w przygodnej próbce moczu, stężenia TSH, fT4 i przeciwciał przeciwtarczycowych oraz ultrasonograficznie objętość tarczycy u 115 kobiet (7 w pierwszym trymestrze, 61 w drugim trymestrze oraz 47 w trzecim trymestrze). Wyniki: Tylko 45,2% kobiet przyjmowało dodatkowe ilości jodu, a mediana jodurii u wszystkich kobiet wyniosła 79,6 μg/l, co wskazuje na niedostateczną podaż jodu. Odsetek kobiet przyjmujących suplementację jodu wzrastał wraz z długością ciąży, co sugeruje, że rekomendacje są wdrażane zbyt późno. U kobiet przyjmujących suplementację jodu, joduria była istotnie wyższa niż u kobiet nieprzyjmujących dodatkowych ilości jodu (mediana 129,4 μg/l vs. 73,0 μg/l; p&lt; 0,001), jednak wartości te wciąż były niższe niż rekomendowane. Wnioski: Skuteczność profilaktyki jodowej u kobiet w ciąży w Polsce, oceniona na podstawie analizy przekrojowej przypadkowo wybranych osób, nie jest zadowalająca pod względem przestrzegania zaleceń, i prawdopobnie, jakości prowadzonej suplementacji. (Endokrynol Pol 2015; 66 (5): 404–411)

    A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing

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    [EN] Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.This research was funded by the Spanish Ministerio de Economia, Industria y Competitividad, grant number TIN2016-79951-R (BigCLOE) and by the Vicerrectorado de Estudios, Calidad y Acreditacion of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29.Naranjo, DM.; Prieto, JR.; Moltó, G.; Calatrava Arroyo, A. (2019). A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing. Sensors. 19(13):1-15. https://doi.org/10.3390/s19132952S1151913Porter, W. W., Graham, C. R., Spring, K. A., & Welch, K. R. (2014). Blended learning in higher education: Institutional adoption and implementation. Computers & Education, 75, 185-195. doi:10.1016/j.compedu.2014.02.011Thai, N. T. T., De Wever, B., & Valcke, M. (2017). The impact of a flipped classroom design on learning performance in higher education: Looking for the best «blend» of lectures and guiding questions with feedback. Computers & Education, 107, 113-126. doi:10.1016/j.compedu.2017.01.003Chen, Y., Wang, Y., Kinshuk, & Chen, N.-S. (2014). Is FLIP enough? Or should we use the FLIPPED model instead? Computers & Education, 79, 16-27. doi:10.1016/j.compedu.2014.07.004Baepler, P., Walker, J. D., & Driessen, M. (2014). It’s not about seat time: Blending, flipping, and efficiency in active learning classrooms. Computers & Education, 78, 227-236. doi:10.1016/j.compedu.2014.06.006Molto, G., & Caballer, M. (2014). On using the cloud to support online courses. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. doi:10.1109/fie.2014.7044041González-Martínez, J. A., Bote-Lorenzo, M. L., Gómez-Sánchez, E., & Cano-Parra, R. (2015). Cloud computing and education: A state-of-the-art survey. Computers & Education, 80, 132-151. doi:10.1016/j.compedu.2014.08.017AWS Cloudtrailhttps://aws.amazon.com/cloudtrail/?nc1=h_lsFerguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. doi:10.1504/ijtel.2012.051816Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., … Dillenbourg, P. (2017). Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Transactions on Learning Technologies, 10(1), 30-41. doi:10.1109/tlt.2016.2599522Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, 105512. doi:10.1016/j.chb.2018.05.004Tabaa, Y., & Medouri, A. (2013). LASyM: A Learning Analytics System for MOOCs. International Journal of Advanced Computer Science and Applications, 4(5). doi:10.14569/ijacsa.2013.040516Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2013). Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing. doi:10.1007/s00779-013-0751-2Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330666Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470-489. doi:10.1016/j.compedu.2011.08.030Leony, D., Pardo, A., de la Fuente Valentín, L., de Castro, D. S., & Kloos, C. D. (2012). GLASS. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330642Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135. doi:10.1016/j.compedu.2018.03.018Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate. Proceedings of the 8th International Conference on Learning Analytics and Knowledge. doi:10.1145/3170358.3170421Amazon CloudWatchhttps://aws.amazon.com/cloudwatch/?nc1=h_lsSpectrumhttps://spectrumapp.io/Opsview Monitorhttps://www.opsview.com/SignalFxhttps://signalfx.com/AWS Cloud Monitoringhttps://www.solarwinds.com/topics/aws-monitoringLonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97. doi:10.1016/j.chb.2014.07.013Pintrich, P. R. (2004). A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students. Educational Psychology Review, 16(4), 385-407. doi:10.1007/s10648-004-0006-xButler, D. L., & Winne, P. H. (1995). Feedback and Self-Regulated Learning: A Theoretical Synthesis. Review of Educational Research, 65(3), 245-281. doi:10.3102/00346543065003245Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space. Journal of Learning Analytics, 1(2). doi:10.18608/jla.2014.12.3Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice. Lecture Notes in Computer Science, 82-96. doi:10.1007/978-3-319-66610-5_

    Does the Use of Learning Management Systems With Hypermedia Mean Improved Student Learning Outcomes?

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    Learning management systems (LMSs) that incorporate hypermedia Smart Tutoring Systems and personalized student feedback can increase self-regulated learning (SRL), motivation, and effective learning. These systems are studied with the following aims: (1) to verify whether the use of LMS with hypermedia Smart Tutoring Systems improves student learning outcomes; (2) to verify whether the learning outcomes will be grouped into performance clusters (Satisfactory, Good, and Excellent); and (3) to verify whether those clusters will group together the different learning outcomes assessed in four different evaluation procedures. Use of the LMS with hypermedia Smart Tutoring Systems was studied among students of Health Sciences, all of whom had similar test results in the use of metacognitive skills. It explained 38% of the variance in student learning outcomes in the evaluation procedures. Likewise, three clusters that grouped the learning outcomes in relation to the variable ‘Use of an LMS with hypermedia Smart Tutoring Systems vs. No use’ explained 60.4% of the variance. Each cluster grouped the learning outcomes in the different evaluation procedures. In conclusion, LMS with hypermedia Smart Tutoring Systems in Moodle increased the effectiveness of student learning outcomes, above all in the individual quiz-type tests. It also facilitated personalized learning and respect for the individual pace of student-learning. Hence, modules for the analysis of supervised, unsupervised and multivariate learning should be incorporated into the Moodle platform to provide teaching tools that will undoubtedly contribute to improvements in student learning outcomes.The Research Funding Program 2018 of the Vice-Rectorate for Research and Knowledge Transfer of the University of Burgos
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