The inevitable youthfulness of known high-redshift radio galaxies

Radio galaxies can be seen out to very high redshifts, where in principle they can serve as probes of the early evolution of the Universe. Here we show that for any model of radio-galaxy evolution in which the luminosity decreases with time after an initial rapid increase (that is, essentially all reasonable models), all observable high-redshift radio-galaxies must be seen when the lobes are less than 10^7 years old. This means that high-redshift radio galaxies can be used as a high-time-resolution probe of evolution in the early Universe. Moreover, this result helps to explain many observed trends of radio-galaxy properties with redshift [(i) the `alignment effect' of optical emission along radio-jet axes, (ii) the increased distortion in radio structure, (iii) the decrease in physical sizes, (iv) the increase in radio depolarisation, and (v) the increase in dust emission] without needing to invoke explanations based on cosmology or strong evolution of the surrounding intergalactic medium with cosmic time, thereby avoiding conflict with current theories of structure formation.Comment: To appear in Nature. 4 pages, 2 colour figures available on request. Also available at http://www-astro.physics.ox.ac.uk/~km

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). Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics. PLoS ONE. 14(12):1-15. https://doi.org/10.1371/journal.pone.0225817S1151412Goldstein, B., Fiser, D. H., Kelly, M. M., Mickelsen, D., Ruttimann, U., & Pollack, M. M. (1998). Decomplexification in critical illness and injury: Relationship between heart rate variability, severity of illness, and outcome. Critical Care Medicine, 26(2), 352-357. doi:10.1097/00003246-199802000-00040Varela, M. (2008). The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 1, 3-11. doi:10.2147/dmso.s3812Vikman, S., Mäkikallio, T. H., Yli-Mäyry, S., Pikkujämsä, S., Koivisto, A.-M., Reinikainen, P., … Huikuri, H. V. (1999). 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. Pflügers Archiv - European Journal of Physiology, 439(4), 403-415. doi:10.1007/s004249900135Eke, A., Herman, P., Kocsis, L., & Kozak, L. R. (2002). Fractal characterization of complexity in temporal physiological signals. Physiological Measurement, 23(1), R1-R38. doi:10.1088/0967-3334/23/1/201King, A. B., Philis-Tsimikas, A., Kilpatrick, E. S., Langbakke, I. H., Begtrup, K., & Vilsbøll, T. (2017). A Fixed Ratio Combination of Insulin Degludec and Liraglutide (IDegLira) Reduces Glycemic Fluctuation and Brings More Patients with Type 2 Diabetes Within Blood Glucose Target Ranges. Diabetes Technology & Therapeutics, 19(4), 255-264. doi:10.1089/dia.2016.0405Colas, A., Vigil, L., Rodríguez de Castro, C., Vargas, B., & Varela, M. (2018). New insights from continuous glucose monitoring into the route to diabetes. Diabetes/Metabolism Research and Reviews, 34(5), e3002. doi:10.1002/dmrr.3002Henriques, T., Munshi, M. N., Segal, A. R., Costa, M. D., & Goldberger, A. L. (2014). «Glucose-at-a-Glance». Journal of Diabetes Science and Technology, 8(2), 299-306. doi:10.1177/1932296814524095Hinton, P. R. (2004). Statistics Explained. doi:10.4324/9780203496787Van Cauter, E., Blackman, J. D., Roland, D., Spire, J. P., Refetoff, S., & Polonsky, K. S. (1991). Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep. Journal of Clinical Investigation, 88(3), 934-942. doi:10.1172/jci115396Qian, J., & Scheer, F. A. J. L. (2016). Circadian System and Glucose Metabolism: Implications for Physiology and Disease. Trends in Endocrinology & Metabolism, 27(5), 282-293. doi:10.1016/j.tem.2016.03.00

Selective Serotonin Reuptake Inhibitor (SSRI) Antidepressants in Pregnancy and Congenital Anomalies: Analysis of Linked Databases in Wales, Norway and Funen, Denmark

Background: Hypothesised associations between in utero exposure to selective serotonin reuptake inhibitors (SSRIs) and congenital anomalies, particularly congenital heart defects (CHD), remain controversial. We investigated the putative teratogenicity of SSRI prescription in the 91 days either side of first day of last menstrual period (LMP). Methods and Findings: Three population-based EUROCAT congenital anomaly registries- Norway (2004–2010), Wales (2000–2010) and Funen, Denmark (2000–2010)—were linked to the electronic healthcare databases holding prospectively collected prescription information for all pregnancies in the timeframes available. We included 519,117 deliveries, including foetuses terminated for congenital anomalies, with data covering pregnancy and the preceding quarter, including 462,641 with data covering pregnancy and one year either side. For SSRI exposures 91 days either side of LMP, separately and together, odds ratios with 95% confidence intervals (ORs, 95%CI) for all major anomalies were estimated. We also explored: pausing or discontinuing SSRIs preconception, confounding, high dose regimens, and, in Wales, diagnosis of depression. Results were combined in meta-analyses. SSRI prescription 91 days either side of LMP was associated with increased prevalence of severe congenital heart defects (CHD) (as defined by EUROCAT guide 1.3, 2005) (34/12,962 [0.26%] vs. 865/506,155 [0.17%] OR 1.50, 1.06–2.11), and the composite adverse outcome of 'anomaly or stillbirth' (473/12962, 3.65% vs. 15829/506,155, 3.13%, OR 1.13, 1.03–1.24). The increased prevalence of all major anomalies combined did not reach statistical significance (3.09% [400/12,962] vs. 2.67% [13,536/506,155] OR 1.09, 0.99–1.21). Adjusting for socio-economic status left ORs largely unchanged. The prevalence of anomalies and severe CHD was reduced when SSRI prescriptions were stopped or paused preconception, and increased when >1 prescription was recorded, but differences were not statistically significant. The dose-response relationship between severe CHD and SSRI dose (meta-regression OR 1.49, 1.12–1.97) was consistent with SSRI-exposure related risk. Analyses in Wales suggested no associations between anomalies and diagnosed depression. Conclusion: The additional absolute risk of teratogenesis associated with SSRIs, if causal, is small. However, the high prevalence of SSRI use augments its public health importance, justifying modifications to preconception care

The PARAChute project: remote monitoring of posture and gait for fall prevention

Falls in the elderly are a major public health problem due to both their frequency and their medical and social consequences. In France alone, more than two million people aged over 65 years old fall each year, leading to more than 9 000 deaths, in particular in those over 75 years old (more than 8 000 deaths). This paper describes the PARAChute project, which aims to develop a methodology that will enable the detection of an increased risk of falling in community-dwelling elderly. The methods used for a remote noninvasive assessment for static and dynamic balance assessments and gait analysis are described. The final result of the project has been the development of an algorithm for movement detection during gait and a balance signature extracted from a force plate. A multicentre longitudinal evaluation of balance has commenced in order to validate the methodologies and technologies developed in the project

On the dynamics of the adenylate energy system: homeorhesis vs homeostasis.

Biochemical energy is the fundamental element that maintains both the adequate turnover of the biomolecular structures and the functional metabolic viability of unicellular organisms. The levels of ATP, ADP and AMP reflect roughly the energetic status of the cell, and a precise ratio relating them was proposed by Atkinson as the adenylate energy charge (AEC). Under growth-phase conditions, cells maintain the AEC within narrow physiological values, despite extremely large fluctuations in the adenine nucleotides concentration. Intensive experimental studies have shown that these AEC values are preserved in a wide variety of organisms, both eukaryotes and prokaryotes. Here, to understand some of the functional elements involved in the cellular energy status, we present a computational model conformed by some key essential parts of the adenylate energy system. Specifically, we have considered (I) the main synthesis process of ATP from ADP, (II) the main catalyzed phosphotransfer reaction for interconversion of ATP, ADP and AMP, (III) the enzymatic hydrolysis of ATP yielding ADP, and (IV) the enzymatic hydrolysis of ATP providing AMP. This leads to a dynamic metabolic model (with the form of a delayed differential system) in which the enzymatic rate equations and all the physiological kinetic parameters have been explicitly considered and experimentally tested in vitro. Our central hypothesis is that cells are characterized by changing energy dynamics (homeorhesis). The results show that the AEC presents stable transitions between steady states and periodic oscillations and, in agreement with experimental data these oscillations range within the narrow AEC window. Furthermore, the model shows sustained oscillations in the Gibbs free energy and in the total nucleotide pool. The present study provides a step forward towards the understanding of the fundamental principles and quantitative laws governing the adenylate energy system, which is a fundamental element for unveiling the dynamics of cellular life

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response

Diagnosis Of Malaria By Community Health Workers In Nigeria

Objective : The introduction of primary health care made Nigeria, a developing country, train and retrain community health workers to work all over the country especially in the rural communities where there is dearth of doctors. Despite their training and experience many people are skeptical of their competence to diagnose accurately what more treating endemic disease like malaria. The need to find out the diagnostic competence of the health workers in malaria control programme now in Nigeria necessitated this study.Method: A rural primary health centre, the sentinel site for malarial control programme investigation in Imo State of Nigeria was selected..The community health technician (CHT). was the health worker in charge. Those who were diagnosed as malaria patients by CHT were examined by a medical laboratory scientist (who was engaged specifically for this job) for malaria parasitaemia. The laboratory examination was Giamsa – stained thick blood from fingerprint. Those with positive parasite density count at 1000/μL and above were regarded as malaria patients. The study was from March – October 2007.Results: The number diagnosed as malaria patients on clinical grounds by CHT was 2512 while the number diagnosed by both clinical andlaboratory basis was 2490. The number of patients with wrong diagnosis of malaria by CHT was 22 (0.875%).Conclusion: The CHT is useful in the diagnosis and by extension in the control of such endemic disease as malaria where there is no laboratory facilities. Both the employers and populace should repose confidence in their services and in the area where they have been trained and acquired experienc

Trichomoniasis as an indicator for existing sexually transmitted infections in women in Aba, Nigeria

Background: Trichomoniasis is a common clinical problem. Many young women in Aba indulge in high-risk sexual behaviours. A large number of these young women are illiterates, and are in the habit of indiscriminate use of antibacterial agents at the slightest symptoms of a lower genital tract infection. Evaluation of bacterial agents associated with lower genital tract infections is therefore met with much frustration. The diagnosis of Trichomoniasis from lower genital tract is simple and its routine screening among women attending clinics would serve as an indicator for serious sexually transmitted infections in Aba.Methods: This study was undertaken among women attending a women hospital in Aba, Abia State, Nigeria (Princess Mary Hospital, Aba). In the study, 360 women who were attending the family and antenatal clinics were selected. Also, those with gynaecological problems, obvious symptoms of lower genital tract infections and those who visited the hospital for “well women examination” were included in the study population. High vaginal swabs collected from these women were examined microscopically by wet mount preparations and bacteriologically by cultures. Results: Out of 360 women screened for Trichomonas vaginalis through wet mount preparation, and other organisms by culture, 40 (11.1%) were positive for Trichomonas vaginalis, 6(1.7%), 48(13.3%) and 140(38.9%) were positive for Neisseria gonorrhoeae, Gadnerella vaginalis, and Candida albicans respectively. The difference in age specific distribution of Trichomoniasis was statistically significant using the chi-square (

High prevalence of asymptomatic plasmodium infection in a suburb of Aba town, Nigeria

Background: Malaria is endemic in many parts of the world. Various strategies have been planned to control malaria from time to time in many places. Whatever may be the strategy the prevalence of symptomatic and asymptomatic plasmodium parasitaemics has been of prime importance as useful parameter for its control. It is hoped that malaria control programme in Nigeria will benefit from prevalence of parasitaemic study such as this. Method: Ndiegoro flood disaster district was selected by stratified random sampling from 16 districts of ward 3 out of 12 wards in Aba South Local Government out of the 2 Local Governments of Aba Town. About three quarters of the houses were uninhabited as they were submerged at various depths of the selected district. The population who consented for the study was 257. Thick and thin blood films were studied by light microscopy for plasmodium parasitaemia. Results: The prevalence of plasmodium parasitaemics in the 257 studied population was very high (45.1%). The asymptomatic parasitaemics were about three times as many as symptomatic parasitaemics (73.2% and 26.7% respectively). This difference is statistically significant (