Pull-out and push-in tests of bonded steel strands

Martí Vargas, JR. (2013). Pull-out and push-in tests of bonded steel strands. Magazine of Concrete Research. 65(18):1128-1131. doi:10.1680/macr.13.00061S112811316518Balázs LG .Bond Model with Non-Linear Bond-Slip Law, 1987, Politecnico di Milano, Italy, 395–430, Studi e Ricerche, Post-Graduate Course for Reinforced Concrete Structures, V.8/86.Balazs, G. L. (1992). Transfer Control of Prestressing Strands. PCI Journal, 37(6), 60-71. doi:10.15554/pcij.11011992.60.71Balazs, G. L. (1993). Transfer Length of Prestressing Strand as a Function of Draw-In and Initial Prestress. PCI Journal, 38(2), 86-93. doi:10.15554/pcij.03011993.86.93Balázs, G. L. (2007). Connecting Reinforcement to Concrete by Bond. Beton- und Stahlbetonbau, 102(S1), 46-50. doi:10.1002/best.200710109Carmo RNF .Ancoragem de Armaduras Pré-Esforçadas por Pré-Tensão. MSc thesis, 1999, Faculdade de Ciências e Tecnologia, Universidade de Coimbra, Portugal, (in Portuguese).Faria, D. M. V., Lúcio, V. J. G., & Pinho Ramos, A. (2011). Pull-out and push-in tests of bonded steel strands. Magazine of Concrete Research, 63(9), 689-705. doi:10.1680/macr.2011.63.9.689Faria, D. M. V., Lúcio, V. J. G., & Ramos, A. P. (2011). Strengthening of flat slabs with post-tensioning using anchorages by bonding. Engineering Structures, 33(6), 2025-2043. doi:10.1016/j.engstruct.2011.02.039Faria, D. M. V., Lúcio, V. J. G., & Pinho Ramos, A. (2012). Post-punching behaviour of flat slabs strengthened with a new technique using post-tensioning. Engineering Structures, 40, 383-397. doi:10.1016/j.engstruct.2012.03.014Laldji S .Bond Characteristics of Prestressing Strand in Grout. MPhil thesis, 1987, University of Leicester, UK.Laldji, S., & Young, A. G. (1988). Bond between steel strand and cement grout in ground anchorages. Magazine of Concrete Research, 40(143), 90-98. doi:10.1680/macr.1988.40.143.90Lopes, S. M. R., & do Carmo, R. N. F. (2002). Bond of prestressed strands to concrete: transfer rate and relationship between transmission length and tendon draw-in. Structural Concrete, 3(3), 117-126. doi:10.1680/stco.2002.3.3.117Martí-Vargas, J. R., Serna-Ros, P., Fernández-Prada, M. A., Miguel-Sosa, P. F., & Arbeláez, C. A. (2006). Test method for determination of the transmission and anchorage lengths in prestressed reinforcement. Magazine of Concrete Research, 58(1), 21-29. doi:10.1680/macr.2006.58.1.21Marti-Vargas, J. R., Arbelaez, C. A., Serna-Ros, P., Navarro-Gregori, J., & Pallares-Rubio, L. (2007). Analytical model for transfer length prediction of 13 mm prestressing strand. Structural Engineering and Mechanics, 26(2), 211-229. doi:10.12989/sem.2007.26.2.211Palmer, K. D., & Schultz, A. E. (2011). Experimental investigation of the web-shear strength of deep hollow-core units. PCI Journal, 56(4), 83-104. doi:10.15554/pcij.09012011.83.10

Phenoloxidase activity acts as a mosquito innate immune response against infection with semliki forest virus

Several components of the mosquito immune system including the RNA interference (RNAi), JAK/STAT, Toll and IMD pathways have previously been implicated in controlling arbovirus infections. In contrast, the role of the phenoloxidase (PO) cascade in mosquito antiviral immunity is unknown. Here we show that conditioned medium from the Aedes albopictus-derived U4.4 cell line contains a functional PO cascade, which is activated by the bacterium Escherichia coli and the arbovirus Semliki Forest virus (SFV) (Togaviridae; Alphavirus). Production of recombinant SFV expressing the PO cascade inhibitor Egf1.0 blocked PO activity in U4.4 cell- conditioned medium, which resulted in enhanced spread of SFV. Infection of adult female Aedes aegypti by feeding mosquitoes a bloodmeal containing Egf1.0-expressing SFV increased virus replication and mosquito mortality. Collectively, these results suggest the PO cascade of mosquitoes plays an important role in immune defence against arboviruses

A Mathematical model for Astrocytes mediated LTP at Single Hippocampal Synapses

Many contemporary studies have shown that astrocytes play a significant role in modulating both short and long form of synaptic plasticity. There are very few experimental models which elucidate the role of astrocyte over Long-term Potentiation (LTP). Recently, Perea & Araque (2007) demonstrated a role of astrocytes in induction of LTP at single hippocampal synapses. They suggested a purely pre-synaptic basis for induction of this N-methyl-D- Aspartate (NMDA) Receptor-independent LTP. Also, the mechanisms underlying this pre-synaptic induction were not investigated. Here, in this article, we propose a mathematical model for astrocyte modulated LTP which successfully emulates the experimental findings of Perea & Araque (2007). Our study suggests the role of retrograde messengers, possibly Nitric Oxide (NO), for this pre-synaptically modulated LTP.Comment: 51 pages, 15 figures, Journal of Computational Neuroscience (to appear

Impact of a Prohibitive Versus Restrictive Tobacco Policy on Liver Transplant Candidate Outcomes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150602/1/lt25497_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150602/2/lt25497.pd

DNA aneuploidy as a topographic malignant transformation pattern in a pleomorphic adenoma of long-term evolution: a case report

<p>Abstract</p> <p>Introduction</p> <p>We present a case of long-term evolution of a submandibular pleomorphic adenoma. There is little information about topographic malignant transformation patterns of pleomorphic adenomas.</p> <p>Case presentation</p> <p>We extensively analyze a giant submandibular mixed tumor of 25-year evolution in a 57-year-old Caucasian woman. Deoxyribonucleic acid ploidy was evaluated in different superficial and deep areas using flow cytometry analysis and correlated with pathological and immunohistochemical characteristics. Superficial areas exhibited a typical histological pleomorphic adenoma pattern and were deoxyribonucleic acid diploid. Deep samples showed deoxyribonucleic acid aneuploidy, atypical histological benign features and expression of markers involved at an early-stage of malignant transformation, such as tumor protein 53 and antigen Ki67.</p> <p>Conclusion</p> <p>These findings revealed that deep tumor compartments may be involved in the initial stages of malignant transformation. Deoxyribonucleic acid ploidy analysis may provide an additional diagnosis tool and indicate 'uncertain' areas that require careful study to avoid diagnostic errors. Larger studies are needed to confirm our results and to evaluate the usefulness of the technique.</p

Domain Analysis Reveals That a Deubiquitinating Enzyme USP13 Performs Non-Activating Catalysis for Lys63-Linked Polyubiquitin

Deubiquitination is a reverse process of cellular ubiquitination important for many biological events. Ubiquitin (Ub)-specific protease 13 (USP13) is an ortholog of USP5 implicated in catalyzing hydrolysis of various Ub chains, but its enzymatic properties and catalytic regulation remain to be explored. Here we report studies of the roles of the Ub-binding domains of USP13 in regulatory catalysis by biochemical and NMR structural approaches. Our data demonstrate that USP13, distinct from USP5, exhibits a weak deubiquitinating activity preferring to Lys63-linked polyubiquitin (K63-polyUb) in a non-activation manner. The zinc finger (ZnF) domain of USP13 shares a similar fold with that of USP5, but it cannot bind with Ub, so that USP13 has lost its ability to be activated by free Ub. Substitution of the ZnF domain with that of USP5 confers USP13 the property of catalytic activation. The tandem Ub-associated (UBA) domains of USP13 can bind with different types of diUb but preferentially with K63-linked, providing a possible explanation for the weak activity preferring to K63-polyUb. USP13 can also regulate the protein level of CD3δ in cells, probably depending on its weak deubiquitinating activity and the Ub-binding properties of the UBA domains. Thus, the non-activating catalysis of USP13 for K63-polyUb chains implies that it may function differently from USP5 in cellular deubiquitination processes

Vascular Smooth Muscle Cell Stiffness and Adhesion to Collagen I Modified by Vasoactive Agonists

In vascular smooth muscle cells (VSMCs) integrin-mediated adhesion to extracellular matrix (ECM) proteins play important roles in sustaining vascular tone and resistance. The main goal of this study was to determine whether VSMCs adhesion to type I collagen (COL-I) was altered in parallel with the changes in the VSMCs contractile state induced by vasoconstrictors and vasodilators. VSMCs were isolated from rat cremaster skeletal muscle arterioles and maintained in primary culture without passage. Cell adhesion and cell E-modulus were assessed using atomic force microscopy (AFM) by repetitive nano-indentation of the AFM probe on the cell surface at 0.1 Hz sampling frequency and 3200 nm Z-piezo travelling distance (approach and retraction). AFM probes were tipped with a 5 μm diameter microbead functionalized with COL-I (1mg\ml). Results showed that the vasoconstrictor angiotensin II (ANG-II; 10−6 ) significantly increased (p<0.05) VSMC E-modulus and adhesion probability to COL-I by approximately 35% and 33%, respectively. In contrast, the vasodilator adenosine (ADO; 10−4 ) significantly decreased (p<0.05) VSMC E-modulus and adhesion probability by approximately −33% and −17%, respectively. Similarly, the NO donor (PANOate, 10−6 M), a potent vasodilator, also significantly decreased (p<0.05) the VSMC E-modulus and COL-I adhesion probability by −38% and −35%, respectively. These observations support the hypothesis that integrin-mediated VSMC adhesion to the ECM protein COL-I is dynamically regulated in parallel with VSMC contractile activation. These data suggest that the signal transduction pathways modulating VSMC contractile activation and relaxation, in addition to ECM adhesion, interact during regulation of contractile state

Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

[EN] Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.This study has been funded by Instituto de Salud Carlos III through the project PI17/00856 (Co-funded by the European Regional Development Fund, A way to make Europe). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Colás, A.; Vigil, L.; Vargas, B.; Cuesta Frau, D.; Varela, M. (2019). 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Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation. Circulation, 100(20), 2079-2084. doi:10.1161/01.cir.100.20.2079Wang, H., Naghavi, M., Allen, C., Barber, R. M., Bhutta, Z. A., Carter, A., … Coates, M. M. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1459-1544. doi:10.1016/s0140-6736(16)31012-1Saudek, C. D., Derr, R. L., & Kalyani, R. R. (2006). Assessing Glycemia in Diabetes Using Self-monitoring Blood Glucose and Hemoglobin A1c. JAMA, 295(14), 1688. doi:10.1001/jama.295.14.1688Monnier, L., Colette, C., & Owens, D. R. (2008). Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is it Important? How to Measure it? Journal of Diabetes Science and Technology, 2(6), 1094-1100. doi:10.1177/193229680800200618Abdul-Ghani, M. A., Tripathy, D., & DeFronzo, R. A. (2006). Contributions of  -Cell Dysfunction and Insulin Resistance to the Pathogenesis of Impaired Glucose Tolerance and Impaired Fasting Glucose. Diabetes Care, 29(5), 1130-1139. doi:10.2337/dc05-2179(2017). 2. Classification and Diagnosis of Diabetes:Standards of Medical Care in Diabetes—2018. Diabetes Care, 41(Supplement 1), S13-S27. doi:10.2337/dc18-s002Tabák, A. G., Herder, C., Rathmann, W., Brunner, E. J., & Kivimäki, M. (2012). Prediabetes: a high-risk state for diabetes development. The Lancet, 379(9833), 2279-2290. doi:10.1016/s0140-6736(12)60283-9DeFronzo, R. A., Banerji, M. A., Bray, G. A., Buchanan, T. A., Clement, S., … Tripathy, D. (2009). Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia, 53(3), 435-445. doi:10.1007/s00125-009-1614-2Nathan, D. M., Davidson, M. B., DeFronzo, R. A., Heine, R. J., Henry, R. R., Pratley, R., & Zinman, B. (2007). Impaired Fasting Glucose and Impaired Glucose Tolerance: Implications for care. Diabetes Care, 30(3), 753-759. doi:10.2337/dc07-9920Ogata, H., Tokuyama, K., Nagasaka, S., Tsuchita, T., Kusaka, I., Ishibashi, S., … Yamamoto, Y. (2012). The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism, 61(7), 1041-1050. doi:10.1016/j.metabol.2011.12.007Kohnert, K.-D. (2015). Utility of different glycemic control metrics for optimizing management of diabetes. World Journal of Diabetes, 6(1), 17. doi:10.4239/wjd.v6.i1.17García Maset, L., González, L. B., Furquet, G. L., Suay, F. M., & Marco, R. H. (2016). Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes. Diabetes Technology & Therapeutics, 18(11), 719-724. doi:10.1089/dia.2016.0208Service, F. J., O’Brien, P. C., & Rizza, R. A. (1987). Measurements of Glucose Control. Diabetes Care, 10(2), 225-237. doi:10.2337/diacare.10.2.225Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C.-K., & Stanley, H. E. (2002). Fractal dynamics in physiology: Alterations with disease and aging. Proceedings of the National Academy of Sciences, 99(Supplement 1), 2466-2472. doi:10.1073/pnas.012579499Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Platiša, M. M., Bojić, T., Pavlović, S. U., Radovanović, N. N., & Kalauzi, A. (2016). Generalized Poincaré Plots-A New Method for Evaluation of Regimes in Cardiac Neural Control in Atrial Fibrillation and Healthy Subjects. Frontiers in Neuroscience, 10. doi:10.3389/fnins.2016.00038García-Puig, J., Ruilope, L. M., Luque, M., Fernández, J., Ortega, R., & Dal-Ré, R. (2006). Glucose Metabolism in Patients with Essential Hypertension. The American Journal of Medicine, 119(4), 318-326. doi:10.1016/j.amjmed.2005.09.010Lepot, M., Aubin, J.-B., & Clemens, F. (2017). Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water, 9(10), 796. doi:10.3390/w9100796Eke, A., Hermán, P., Bassingthwaighte, J., Raymond, G., Percival, D., Cannon, M., … Ikrényi, C. (2000). Physiological time series: distinguishing fractal noises from motions. 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An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes