Calcium/calmodulin-dependent kinases can regulate the TSH expression in the rat pituitary.

PURPOSE: The endocrine secretion of TSH is a finely orchestrated process controlled by the thyrotropin-releasing hormone (TRH). Its homeostasis and signaling rely on many calcium-binding proteins belonging to the "EF-hand" protein family. The Ca2+/calmodulin (CaM) complex is associated with Ca2+/CaM-dependent kinases (Ca2+/CaMK). We have investigated Ca2+/CaMK expression and regulation in the rat pituitary. METHODS: The expression of CaMKII and CaMKIV in rat anterior pituitary cells was shown by immunohistochemistry. Cultured anterior pituitary cells were stimulated by TRH in the presence and absence of KN93, the pharmacological inhibitor of CaMKII and CaMKIV. Western blotting was then used to measure the expression of these kinases and of the cAMP response element-binding protein (CREB). TSH production was measured by RIA after time-dependent stimulation with TRH. Cells were infected with a lentiviral construct coding for CaMKIV followed by measurement of CREB phosphorylation and TSH. RESULTS: Our study shows that two CaM kinases, CaMKII and CaMKII, are expressed in rat pituitary cells and their phosphorylation in response to TRH occurs at different time points, with CaMKIV being activated earlier than CaMKII. TRH induces CREB phosphorylation through the activity of both CaMKII and CaMKIV. The activation of CREB increases TSH gene expression. CaMKIV induces CREB phosphorylation while its dominant negative and KN93 exert the opposite effects. CONCLUSION: Our data indicate that the expression of Ca2+/CaMK in rat anterior pituitary are correlated to the role of CREB in the genetic regulation of TSH, and that TRH stimulation activates CaMKIV, which in turn phosphorylates CREB. This phosphorylation is linked to the production of thyrotropin

Chemists Atwitter

Twitter can be used to promote chemists, their work, and their events to other scientists and the general public. From checklists to timelines; how to use Twitter successfully as an individual or institution is discussed. This chapter includes: examples of how the authors have used Twitter, how to find and use common subject tags, tags most used when Tweeting about chemistry and science, and a discussion about measuring success. Knowing when and how to Tweet will help chemists communicate successfully with their peers and the general public in 280 characters or less

The effect of atomoxetine on directed and random exploration in humans

The adaptive regulation of the trade-off between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration) is critical for optimal performance. Theory and recent empirical work suggest that humans use at least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. Here we examined the hypothesis that random exploration is governed by the neuromodulatory locus coeruleus-norepinephrine system. We administered atomoxetine, a norepinephrine transporter blocker that increases extracellular levels of norepinephrine throughout the cortex, to 22 healthy human participants in a double-blind crossover design. We examined the effect of treatment on performance in a gambling task designed to produce distinct measures of directed exploration and random exploration. In line with our hypothesis we found an effect of atomoxetine on random, but not directed exploration. However, contrary to expectation, atomoxetine reduced rather than increased random exploration. We offer three potential explanations of our findings, involving the non-linear relationship between tonic NE and cognitive performance, the interaction of atomoxetine with other neuromodulators, and the possibility that atomoxetine affected phasic norepinephrine activity more so than tonic norepinephrine activity

The status of epidermal growth factor receptor in borderline ovarian tumours

The majority of borderline ovarian tumours (BOTs) behave in a benign fashion, but some may show aggressive behavior. The reason behind this has not been elucidated. The epidermal growth factor receptor (EGFR) is known to contribute to cell survival signals as well as metastatic potential of some tumours. EGFR expression and gene status have not been thoroughly investigated in BOTs as it has in ovarian carcinomas. In this study we explore protein expression as well as gene mutations and amplifications of EGFR in BOTs in comparison to a subset of other epithelial ovarian tumours. We studied 85 tumours, including 61 BOTs, 10 low grade serous carcinomas (LGSCs), 9 high grade serous carcinomas (HGSCs) and 5 benign epithelial tumours. EGFR protein expression was studied using immunohistochemistry. Mutations were investigated by Sanger sequencing exons 18-21 of the tyrosine kinase domain of EGFR. Cases with comparatively higher protein expression were examined for gene amplification by chromogenic in situ hybridization. We also studied the tumours for KRAS and BRAF mutations. Immunohistochemistry results revealed both cytoplasmic and nuclear EGFR expression with variable degrees between tumours. The level of nuclear localization was relatively higher in BOTs and LGSCs as compared to HGSCs or benign tumours. The degree of nuclear expression of BOTs showed no significant difference from that in LGSCs (mean ranks 36.48, 33.05, respectively, p=0.625), but was significantly higher than in HGSCs (mean ranks: 38.88, 12.61 respectively, p<0.001) and benign tumours (mean ranks: 35.18, 13.00 respectively, p=0.010). Cytoplasmic expression level was higher in LGSCs. No EGFR gene mutations or amplification were identified, yet different polymorphisms were detected. Five different types of point mutations in the KRAS gene and the V600E BRAF mutation were detected exclusively in BOTs and LGSCs. Our study reports for the first time nuclear localization of EGFR in BOTs. The nuclear localization similarities between BOTs and LGSCs and not HGSCs support the hypothesis suggesting evolution of LGSCs from BOTs. We also confirm that EGFR mutations and amplifications are not molecular events in the pathogenesis of BOTs

Beta receptor-mediated modulation of the oddball P3 but not err-related ERP components in humans

FSW - Self-regulation models for health behavior and psychopathology - ou

Catecholamine-mediated increases in neural gain improve the precision of cortical representations

Neurophysiological evidence suggests that neuromodulators, such as norepinephrine and dopamine, increase neural gain in target brain areas. Computational models and prominent theoretical frameworks indicate that this should enhance the precision of neural representations, but direct empirical evidence for this hypothesis is lacking. In two functional MRI studies, we examine the effect of baseline catecholamine levels (as indexed by pupil diameter and manipulated pharmacologically) on the precision of object representations in the human ventral temporal cortex using angular dispersion, a powerful, multivariate metric of representational similarity (precision). We first report the results of computational model simulations indicating that increasing catecholaminergic gain should reduce the angular dispersion, and thus increase the precision, of object representations from the same category, as well as reduce the angular dispersion of object representations from distinct categories when distinct-category representations overlap. In Study 1 (N = 24), we show that angular dispersion covaries with pupil diameter, an index of baseline catecholamine levels. In Study 2 (N = 24), we manipulate catecholamine levels and neural gain using the norepinephrine transporter blocker atomoxetine and demonstrate consistent, causal effects on angular dispersion and brain-wide functional connectivity. Despite the use of very different methods of examining the effect of baseline catecholamine levels, our results show a striking convergence and demonstrate that catecholamines increase the precision of neural representations

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

Do ResearchGate Scores create ghost academic reputations?

[EN] The academic social network site ResearchGate (RG) has its own indicator, RG Score, for its members. The high profile nature of the site means that the RG Score may be used for recruitment, promotion and other tasks for which researchers are evaluated. In response, this study investigates whether it is reasonable to employ the RG Score as evidence of scholarly reputation. For this, three different author samples were investigated. An outlier sample includes 104 authors with high values. A Nobel sample comprises 73 Nobel winners from Medicine and Physiology, Chemistry, Physics and Economics (from 1975 to 2015). A longitudinal sample includes weekly data on 4 authors with different RG Scores. The results suggest that high RG Scores are built primarily from activity related to asking and answering questions in the site. In particular, it seems impossible to get a high RG Score solely through publications. Within RG it is possible to distinguish between (passive) academics that interact little in the site and active platform users, who can get high RG Scores through engaging with others inside the site (questions, answers, social networks with influential researchers). Thus, RG Scores should not be mistaken for academic reputation indicators.Alberto Martin-Martin enjoys a four-year doctoral fellowship (FPU2013/05863) granted by the Ministerio de Educacion, Cultura, y Deporte (Spain). Enrique Orduna-Malea holds a postdoctoral fellowship (PAID-10-14), from the Polytechnic University of Valencia (Spain).Orduña Malea, E.; Martín-Martín, A.; Thelwall, M.; Delgado-López-Cózar, E. (2017). Do ResearchGate Scores create ghost academic reputations?. Scientometrics. 112(1):443-460. https://doi.org/10.1007/s11192-017-2396-9S4434601121Bosman, J. & Kramer, B. (2016). Innovations in scholarly communication—data of the global 2015–2016 survey. Available at: http://zenodo.org/record/49583 #. Accessed December 11, 2016.González-Díaz, C., Iglesias-García, M., & Codina, L. (2015). Presencia de las universidades españolas en las redes sociales digitales científicas: Caso de los estudios de comunicación. El profesional de la información, 24(5), 1699–2407.Goodwin, S., Jeng, W., & He, D. (2014). Changing communication on ResearchGate through interface updates. Proceedings of the American Society for Information Science and Technology, 51(1), 1–4.Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431.Hoffmann, C. P., Lutz, C., & Meckel, M. (2015). A relational altmetric? Network centrality on ResearchGate as an indicator of scientific impact. Journal of the Association for Information Science and Technology, 67(4), 765–775.Jiménez-Contreras, E., de Moya Anegón, F., & Delgado López-Cózar, E. (2003). The evolution of research activity in Spain: The impact of the National Commission for the Evaluation of Research Activity (CNEAI). Research Policy, 32(1), 123–142.Jordan, K. (2014a). Academics’ awareness, perceptions and uses of social networking sites: Analysis of a social networking sites survey dataset (December 3, 2014). Available at: http://dx.doi.org/10.2139/ssrn.2507318 . Accessed December 11, 2016.Jordan, K. (2014b). Academics and their online networks: Exploring the role of academic social networking sites. First Monday, 19(11). Available at: http://dx.doi.org/10.5210/fm.v19i11.4937 . Accessed December 11, 2016.Jordan, K. (2015). Exploring the ResearchGate score as an academic metric: reflections and implications for practice. Quantifying and Analysing Scholarly Communication on the Web (ASCW’15), 30 June 2015, Oxford. Available at: http://ascw.know-center.tugraz.at/wp-content/uploads/2015/06/ASCW15_jordan_response_kraker-lex.pdf . Accessed December 11, 2016.Kadriu, A. (2013). Discovering value in academic social networks: A case study in ResearchGate. Proceedings of the ITI 2013—35th Int. Conf. on Information Technology Interfaces Information Technology Interfaces, pp. 57–62.Kraker, P. & Lex, E. (2015). A critical look at the ResearchGate score as a measure of scientific reputation. Proceedings of the Quantifying and Analysing Scholarly Communication on the Web workshop (ASCW’15), Web Science conference 2015. Available at: http://ascw.know-center.tugraz.at/wp-content/uploads/2016/02/ASCW15_kraker-lex-a-critical-look-at-the-researchgate-score_v1-1.pdf . Accessed December 11, 2016.Li, L., He, D., Jeng, W., Goodwin, S. & Zhang, C. (2015). Answer quality characteristics and prediction on an academic Q&A Site: A case study on ResearchGate. Proceedings of the 24th International Conference on World Wide Web Companion, pp. 1453–1458.Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M. & Delgado López-Cózar, E. (2016). The counting house: measuring those who count. Presence of Bibliometrics, Scientometrics, Informetrics, Webometrics and Altmetrics in the Google Scholar Citations, ResearcherID, ResearchGate, Mendeley & Twitter. Available at: https://arxiv.org/abs/1602.02412 . Accessed December 11, 2016.Martín-Martín, A., Orduna-Malea, E. & Delgado López-Cózar, E. (2016). The role of ego in academic profile services: Comparing Google Scholar, ResearchGate, Mendeley, and ResearcherID. Researchgate, Mendeley, and Researcherid. The LSE Impact of Social Sciences blog. Available at: http://blogs.lse.ac.uk/impactofsocialsciences/2016/03/04/academic-profile-services-many-mirrors-and-faces-for-a-single-ego . Accessed December 11, 2016.Matthews, D. (2016). Do academic social networks share academics’ interests?. Times Higher Education. Available at: https://www.timeshighereducation.com/features/do-academic-social-networks-share-academics-interests . Accessed December 11, 2016.Memon, A. R. (2016). ResearchGate is no longer reliable: leniency towards ghost journals may decrease its impact on the scientific community. Journal of the Pakistan Medical Association, 66(12), 1643–1647.Mikki, S., Zygmuntowska, M., Gjesdal, Ø. L. & Al Ruwehy, H. A. (2015). Digital presence of norwegian scholars on academic network sites-where and who are they?. Plos One 10(11). Available at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142709 . Accessed December 11, 2016.Nicholas, D., Clark, D., & Herman, E. (2016). ResearchGate: Reputation uncovered. Learned Publishing, 29(3), 173–182.Orduna-Malea, E., Martín-Martín, A., & Delgado López-Cózar, E. (2016). The next bibliometrics: ALMetrics (Author Level Metrics) and the multiple faces of author impact. El profesional de la información, 25(3), 485–496.Ortega, Jose L. (2015). Relationship between altmetric and bibliometric indicators across academic social sites: The case of CSIC’s members. Journal of informetrics, 9(1), 39–49.Ortega, Jose L. (2016). Social network sites for scientists. Cambridge: Chandos.Ovadia, S. (2014). ResearchGate and Academia. edu: Academic social networks. Behavioral & Social Sciences Librarian, 33(3), 165–169.Thelwall, M., & Kousha, K. (2015). ResearchGate: Disseminating, communicating, and measuring Scholarship? Journal of the Association for Information Science and Technology, 66(5), 876–889.Thelwall, M. & Kousha, K. (2017). ResearchGate articles: Age, discipline, audience size and impact. Journal of the Association for Information Science and Technology, 68(2), 468–479.Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126–129.Wilsdon, J., Allen, L., Belfiore, E., Campbell, P., Curry, S., Hill, S. et al. (2015). The Metric Tide: Independent Review of the Role of Metrics in Research Assessment and Management. HEFCE. Available at: http://doi.org/10.13140/RG.2.1.4929.1363 . Accessed December 11, 2016