Interpreting intraplate tectonics for seismic hazard : a UK historical perspective

It is notoriously difficult to construct seismic source models for probabilistic seismic hazard assessment in intraplate areas on the basis of geological information, and many practitioners have given up the task in favour of purely seismicity-based models. This risks losing potentially valuable information in regions where the earthquake catalogue is short compared to the seismic cycle. It is interesting to survey how attitudes to this issue have evolved over the past 30 years. This paper takes the UK as an example, and traces the evolution of seismic source models through generations of hazard studies. It is found that in the UK, while the earliest studies did not consider regional tectonics in any way, there has been a gradual evolution towards more tectonically based models. Experience in other countries, of course, may differ

Big Data Analytics. Analyse der prädiktiven Fähigkeit von Twitter-Sentiments auf die Entwicklung des Börsenkurses von Technologieunternehmen

Die Datenmengen vervielfachen sich in der heutigen Zeit konstant, was zum Begriff Big Data geführt hat. Durch diese Datenmengen entsteht ein neues Potenzial, Fragen zu beantworten. Eine dieser Fragestellungen, welche mithilfe von Big Data untersucht werden kann, ist, inwiefern die Social-Media-Daten die Veränderung von Börsenkursen voraussagen können. Diese Studie untersucht die prädiktive Fähigkeit von Twitter-Nachrichten im Zusammenhang mit einem Technologieunternehmen und dessen Börsenkurs anhand von zwei Anwendungsfällen. Konkret wird anhand der Twitter-Nachrichten mithilfe einer Sentimentanalyse die Stimmung der Twitter-Nutzer mit den Veränderungen des Börsenkurses verglichen. Diese Analyse wird anhand der Technologieunternehmen Facebook und Amazon vorgenommen. In einem ersten Schritt wird untersucht, ob eine Beziehung zwischen den Twitter-Sentiments und dem Börsenkurs besteht. In einem zweiten Schritt, ob die Twitter-Sentiments eine Voraussagekraft für die Veränderung des Börsenkurses haben. Die Auswertung zeigt bei beiden Unternehmen eine positive Korrelation der Twitter-Sentiments und des Börsenkurses auf. Weiter konnte mithilfe der Granger-Analyse eine signifikante Voraussagekraft der Twitter-Sentiments für die Börsenkurse beider Unternehmen ermittelt werden. Die Twitter-Sentiments können die Börsenkurse 13 h voraussagen

Statistical modeling of ground motion relations for seismic hazard analysis

We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics. Therein, we understand a GMR as a random function. We derive mathematically the principle of area-equivalence; wherein two alternative GMRs have an equivalent influence on the hazard if these GMRs have equivalent area functions. This includes local biases. An interpretation of the difference between these GMRs (an actual and a modeled one) as a random component leads to a general overestimation of residual variance and hazard. Beside this, we discuss important aspects of classical approaches and discover discrepancies with the state of the art of stochastics and statistics (model selection and significance, test of distribution assumptions, extreme value statistics). We criticize especially the assumption of logarithmic normally distributed residuals of maxima like the peak ground acceleration (PGA). The natural distribution of its individual random component (equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized extreme value. We show by numerical researches that the actual distribution can be hidden and a wrong distribution assumption can influence the PSHA negatively as the negligence of area equivalence does. Finally, we suggest an estimation concept for GMRs of PSHA with a regression-free variance estimation of the individual random component. We demonstrate the advantages of event-specific GMRs by analyzing data sets from the PEER strong motion database and estimate event-specific GMRs. Therein, the majority of the best models base on an anisotropic point source approach. The residual variance of logarithmized PGA is significantly smaller than in previous models. We validate the estimations for the event with the largest sample by empirical area functions. etc

Safety during the monitoring of diabetic patients: trial teaching course on health professionals and diabetics - SEGUDIAB study

<p>Abstract</p> <p>Background</p> <p>Safety for diabetic patients means providing the most suitable treatment for each type of diabetic in order to improve monitoring and to prevent the adverse effects of drugs and complications arising from the disease. The aim of this study is to analyze the effect of imparting educational interventions to health professionals regarding the safety of patients with Diabetes Mellitus (DM).</p> <p>Methods</p> <p><it>Design</it>: A cluster randomized trial with a control group.</p> <p><it>Setting and sample</it>: The study analyzed ten primary healthcare centres (PHC) covering approximately 150,000 inhabitants. Two groups of 5 PHC were selected on the basis of their geographic location (urban, semi-urban and rural), their socio-economic status and the size of their PHC, The interventions and control groups were assigned at random. The study uses computerized patient records to individually assess subjects aged 45 to 75 diagnosed with type 1 and type 2 DM, who met the inclusion conditions and who had the variables of particular interest to the study.</p> <p><it>Trial</it>: The educational interventions consisted of a standardized teaching course aimed at doctors and nurses. The course lasted 6 hours and was split into three 2-hour blocks with subsequent monthly refresher courses.</p> <p><it>Measurement</it>: For the health professionals, the study used the <it>Diabetes Attitude Scale </it>(DAS-3) to assess their attitudes and motivation when monitoring diabetes. For the patients, the study assessed factors related to their degree of control over the disease at onset, 6, 12 and 24 months.</p> <p><it>Main variables</it>: levels of HbA1c.</p> <p><it>Analysis</it>: The study analyzed the effect of the educational interventions both on the attitudes and motivations of health professionals and on the degree of control over the diabetes in both groups.</p> <p>Discussion</p> <p>Imparting educational interventions to health professionals would improve the monitoring of diabetic patients. The most effective model involves imparting the course to both doctors and nurses. However, these models have not been tested on our Spanish population within the framework of primary healthcare.</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01087541">NCT01087541</a></p

Axis I comorbidity in adolescent inpatients referred for treatment of substance use disorders

<p>Abstract</p> <p>Background</p> <p>To assess comorbid DSM-IV-TR Axis I disorders in adolescent inpatients referred for treatment of substance use disorders.</p> <p>Methods</p> <p>151 patients (mean age 16.95 years, SD = 1.76; range 13 - 22) were consecutively assessed with the Composite International Diagnostic Interview (CIDI) and standardized clinical questionnaires to assess mental disorders, symptom distress, psychosocial variables and detailed aspects of drug use. A consecutively referred subgroup of these 151 patients consisting of 65 underage patients (mean age 16.12, SD = 1.10; range 13 - 17) was additionally assessed with the modules for attention-deficit/hyperactivity disorder (ADHD) and conduct disorder (CD) using The Schedule for Affective Disorders and Schizophrenia for school-aged children (K-SADS-PL).</p> <p>Results</p> <p>128 (84.8%) of the 151 patients were dependent on at least one substance, the remaining patients fulfilled diagnostic criteria for abuse only. 40.5% of the participants fulfilled criteria for at least one comorbid present Axis I disorder other than substance use disorders (67.7% in the subgroup additionally interviewed with the K-SADS-PL). High prevalences of present mood disorder (19.2%), somatoform disorders (9.3%), and anxiety disorders (22.5%) were found. The 37 female participants showed a significantly higher risk for lifetime comorbid disorders; the gender difference was significantly pronounced for anxiety and somatoform disorders. Data from the underage subgroup revealed a high prevalence for present CD (41.5%). 33% of the 106 patients (total group) who were within the mandatory school age had not attended school for at least a two-month period prior to admission. In addition, 51.4% had been temporarily expelled from school at least once.</p> <p>Conclusions</p> <p>The present data validates previous findings of high psychiatric comorbidity in adolescent patients with substance use disorders. The high rates of school refusal and conduct disorder indicate the severity of psychosocial impairment.</p

Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

BACKGROUND Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC. METHODS Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals). RESULTS Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation. CONCLUSION This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts