Impact of a stress coping strategy on perceived stress levels and performance during a simulated cardiopulmonary resuscitation: a randomized controlled trial

Background Cardiopulmonary resuscitation (CPR) causes significant stress, which may cause deficiencies in attention and increase distractibility. This may lead to misjudgements of priorities and delays in CPR performance, which may further increase mental stress (vicious cycle). This study assessed the impact of a task-focusing strategy on perceived stress levels and performance during a simulated CPR scenario. Methods This prospective, randomized-controlled trial was conducted at the simulator-center of the University Hospital Basel, Switzerland. A total of 124 volunteer medical students were randomized to receive a 10 minute instruction to cope with stress by loudly posing two taskfocusing questions (“what is the patient’s condition?”, “what immediate action is needed?”) when feeling overwhelmed by stress (intervention group) or a control group. The primary outcome was the perceived levels of stress and feeling overwhelmed (stress/overload); secondary outcomes were hands-on time, time to start CPR and number of leadership statements. Results Participants in the intervention group reported significantly less stress/overload levels compared to the control group (mean difference: -0.6 (95%CI −1.3, -0.1), p=0.04). Higher stress/overload was associated with less hands-on time. Leadership statements did not differ between groups, but the number of leadership statements did relate to performance. Hands-on time was longer in the intervention- group, but the difference was not statistically different (difference 5.5 (95%CI −3.1, 14.2), p=0.2); there were no differences in time to start CPR (difference −1.4 (95%CI −8.4, 5.7), p=0.71). Conclusions A brief stress-coping strategy moderately decreased perceived stress without significantly affecting performance in a simulated CPR. Further studies should investigate more intense interventions for reducing stress.

Simple Estimation of Incident HIV Infection Rates in Notification Cohorts Based on Window Periods of Algorithms for Evaluation of Line-Immunoassay Result Patterns

Background Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. Methods We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship ‘Prevalence = Incidence x Duration’ in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship ‘incident = true incident + false incident’ and also to the IIR derived from the BED incidence assay. Results Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R2 = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. Conclusions IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

Cancer-related therapies at the end of life in hospitalized cancer patients from four Swiss cantons: SAKK 89/09.

The use of cancer-related therapies in cancer patients hospitalized at the end of life has increased in many countries over time. Given the scarcity of published Swiss data, the objective of this study was to evaluate the influence of hospital type and other factors on the delivery of health care during the last month before death. Claims data were used to assess health care utilization of cancer patients (identified by cancer registry data of four participating Swiss cantons) who deceased between 2006 and 2008. Primary endpoints were delivery of cancer-related therapies during the last 30 days before death. Multivariate logistic regression assessed the explanatory value of hospital type, patient and geographic characteristics. Of 3,809 identified cancer patients in the claims database, 2,086 patients dying from cancer were hospitalized during the last 30 days before death, generating 2,262 inpatient episodes. Anticancer drug therapy was given in 22.2% and radiotherapy in 11.7% of episodes. Besides age and cancer type, the canton of residence and hospital type showed independent, statistically significant associations with intensity of care, which was highest in university hospitals. These results should initiate a discussion among oncologists in Switzerland and may question the compliance with standard of care guidelines for terminal cancer patients.

Differentiation between Staphylococcus aureus and coagulase-negative Staphylococcus species by real-time PCR including detection of methicillin resistants in comparison to conventional microbiology testing.

BACKGROUND Staphylococcus aureus has long been recognized as a major pathogen. Methicillin-resistant strains of S. aureus (MRSA) and methicillin-resistant strains of S. epidermidis (MRSE) are among the most prevalent multiresistant pathogens worldwide, frequently causing nosocomial and community-acquired infections. METHODS In the present pilot study, we tested a polymerase chain reaction (PCR) method to quickly differentiate Staphylococci and identify the mecA gene in a clinical setting. RESULTS Compared to the conventional microbiology testing the real-time PCR assay had a higher detection rate for both S. aureus and coagulase-negative Staphylococci (CoNS; 55 vs. 32 for S. aureus and 63 vs. 24 for CoNS). Hands-on time preparing DNA, carrying out the PCR, and evaluating results was less than 5 h. CONCLUSIONS The assay is largely automated, easy to adapt, and has been shown to be rapid and reliable. Fast detection and differentiation of S. aureus, CoNS, and the mecA gene by means of this real-time PCR protocol may help expedite therapeutic decision-making and enable earlier adequate antibiotic treatment.

Effects of congressional modernization laws on public access to innovative medical device technologies

The Federal Food and Drug Administration (FDA) and the Centers for Medicare and Medicaid (CMS) play key roles in making Class III, medical devices available to the public, and they are required by law to meet statutory deadlines for applications under review. Historically, both agencies have failed to meet their respective statutory requirements. Since these failures affect patient access and may adversely impact public health, Congress has enacted several “modernization” laws. However, the effectiveness of these modernization laws has not been adequately studied or established for Class III medical devices. ^ The aim of this research study was, therefore, to analyze how these modernization laws may have affected public access to medical devices. Two questions were addressed: (1) How have the FDA modernization laws affected the time to approval for medical device premarket approval applications (PMAs)? (2) How has the CMS modernization law affected the time to approval for national coverage decisions (NCDs)? The data for this research study were collected from publicly available databases for the period January 1, 1995, through December 31, 2008. These dates were selected to ensure that a sufficient period of time was captured to measure pre- and post-modernization effects on time to approval. All records containing original PMAs were obtained from the FDA database, and all records containing NCDs were obtained from the CMS database. Source documents, including FDA premarket approval letters and CMS national coverage decision memoranda, were reviewed to obtain additional data not found in the search results. Analyses were conducted to determine the effects of the pre- and post-modernization laws on time to approval. Secondary analyses of FDA subcategories were conducted to uncover any causal factors that might explain differences in time to approval and to compare with the primary trends. The primary analysis showed that the FDA modernization laws of 1997 and 2002 initially reduced PMA time to approval; after the 2002 modernization law, the time to approval began increasing and continued to increase through December 2008. The non-combined, subcategory approval trends were similar to the primary analysis trends. The combined, subcategory analysis showed no clear trends with the exception of non-implantable devices, for which time to approval trended down after 1997. The CMS modernization law of 2003 reduced NCD time to approval, a trend that continued through December 2008. This study also showed that approximately 86% of PMA devices do not receive NCDs. ^ As a result of this research study, recommendations are offered to help resolve statutory non-compliance and access issues, as follows: (1) Authorities should examine underlying causal factors for the observed trends; (2) Process improvements should be made to better coordinate FDA and CMS activities to include sharing data, reducing duplication, and establishing clear criteria for “safe and effective” and “reasonable and necessary”; (3) A common identifier should be established to allow tracking and trending of applications between FDA and CMS databases; (4) Statutory requirements may need to be revised; and (5) An investigation should be undertaken to determine why NCDs are not issued for the majority of PMAs. Any process improvements should be made without creating additional safety risks and adversely impacting public health. Finally, additional studies are needed to fully characterize and better understand the trends identified in this research study.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Strontium isotopic data for calcareous ooze, chalk, and pore waters from DSDP Hole 590B

A detailed study of strontium isotope variations in Neogene marine carbonate sediments from Deep Sea Drilling Project Site 590B, using techniques that allow the 87Sr/86Sr ratio to be determined to better than +/-0.00001, gives a high-resolution record of the Sr isotopic evolution of seawater. The data show that the rate of change of the marine 87Sr/86Sr ratio has varied significantly even on time scales as short as 1 m.y. Periods of particularly rapid growth appear to follow major marine regressions and probably reflect an increase in the delivery of radiogenic Sr from the continents coupled with a decreased submarine carbonate dissolution rate (greater carbonate compensation depth). Periods of relatively slowly changing 87Sr/86Sr follow major marine transgressions. On the basis of correlations with the marine oxygen isotope record and the times of major continental glacier growth, it is inferred that the effects of sea-level variations are modified by climatic factors that affect the intensity of continental weathering and runoff. The effects of sea-floor generation rate variations are not discernible for the Neogene. The maximum attainable stratigraphic resolution using Sr isotopes is between 0.1 and 2 m.y. for this time period.

Test Analysis Tool (TATo). A new tool for standardization of time series in maritime and port environment

During the last decades, Puertos del Estado and research groups have developed a huge effort on numerical an physical monitoring. This effort has led to the necessity to implement a tool to standardize, store and process all gathered data. The Test Analysis Tool (TATo) is described in the paper.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.

El Niño influence on potential maize yield in Iberian Peninsula

El Niño phenomenon is the leading mode of sea surface temperature interannual variability. It can affect weather patterns worldwide and therefore crop production. Crop models are useful tools for impact and predictability applications, allowing to obtain long time series of potential and attainable crop yield, unlike to available time series of observed crop yield for many countries. Using this tool, crop yield variability in a location of Iberia Peninsula (IP) has been previously studied, finding predictability from Pacific El Niño conditions. Nevertheless, the work has not been done for an extended area. The present work carries out an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The potential usefulness of this study is to apply the relationships found to improving crop forecasting in IP.

Irreversible inactivation of interleukin 2 in a pump-based delivery environment.

The physical stability of pharmaceutical proteins in delivery environments is a critical determinant of biological potency and treatment efficacy, and yet it is often taken for granted. We studied both the bioactivity and physical stability of interleukin 2 upon delivery via continuous infusion. We found that the biological activity of the delivered protein was dramatically reduced by approximately 90% after a 24-hr infusion program. Only a portion of these losses could be attributed to direct protein deposition on the delivery surfaces. Analysis of delivered protein by size exclusion chromatography gave no indication of insulin-like, surface-induced aggregation phenomena. Examination of the secondary and tertiary structure of both adsorbed and delivered protein via Fourier-transform infrared spectroscopy, circular dichroism, and fluorescence spectroscopy indicated that transient surface association of interleukin 2 with the catheter tubing resulted in profound, irreversible structural changes that were responsible for the majority of the biological activity losses.

Diplomatic Spin: EU3+3 talks on Iran’s nuclear file. CEPS Commentary, 21 November 2014

The Joint Plan of Action agreed upon with Iran on 24 November 2013 gave negotiators one year to forge a comprehensive agreement that restricts the country’s ability to militarise its nuclear programme. That deadline will lapse in the next few days and diplomats involved in the talks have been trying to rein in expectations that a deal will be struck on time. Satisfying domestic constituencies in Iran and the US is what makes the politics of dealing with the nuclear file so much harder than the physics of slowing down the nuclear programme. Any future deal will have to stand on its own merits, enabling Iran and the EU3+3 to cooperate on the other geopolitical challenges they face. Both parties should therefore balance their demands with what they can realistically offer and make concessions to reach a compromise. The author of this CEPS Commentary argues that if no deal is reached on November 24th, then diplomacy should be allowed to keep on spinning for a few more months.

At any price: Russia is embarking on the construction of South Stream. OSW Commentary No. 98, 2012-12-07

Gazprom is determined to continue its efforts to build the South Stream gas pipeline regardless of the slump on the European gas market and the fact that there is sufficient capacity already in the existing transport infrastructure. The official inauguration of the maritime section of South Stream was held on 7 December this year, but the construction itself will commence in 2014. The agreements concluded so far, both intergovernmental and between corporations, are necessary for the launch of the construction of the new pipeline, but still do not guarantee that the project will be completed on time. First of all, some legal problems have yet to be resolved, such as the evaluation of the compliance of the planned actions with the ‘third energy package’ or the fact that ecological surveys required under European law need to be carried out. Secondly, given the present situation on the European gas market and medium-term forecasts, the high cost of implementation of this project and the maintenance expenses of existing pipelines – which are not being used to full capacity – the new project seems to be unfeasible. However, Gazprom’s determination in its efforts to build the pipeline proves that Russia is ready to take a high economic risk to maintain its dominant position on the European gas supply market; it will restrict the possibilities of alternative infrastructural projects being implemented (above all, the EU’s Southern Corridor) and use the construction of new pipelines as an instrument of political pressure on the present transit countries (especially Ukraine).

A Multiplex Real-Time PCR with High Resolution Melting Analysis for the Characterization of Antimicrobial Resistance in Neisseria gonorrhoeae.

Resistance to antibiotics used against Neisseria gonorrhoeae infections is a major public health concern. Antimicrobial resistance (AMR) testing relies on time-consuming culture-based methods. Development of rapid molecular tests for detecting AMR determinants could provide valuable tools for surveillance, epidemiological studies and to inform individual case management. We developed a fast (<1.5 hrs) SYBR-green based real-time PCR method with high resolution melting (HRM) analysis. One triplex and three duplex reactions included two sequences for N. gonorrhoeae identification and seven determinants of resistance to extended-spectrum cephalosporins (ESCs), azithromycin, ciprofloxacin, and spectinomycin. The method was validated by testing 39 previously fully-characterized N. gonorrhoeae strains, 19 commensal Neisseria spp., and an additional panel of 193 gonococcal isolates. Results were compared with culture-based AMR determination. The assay correctly identified N. gonorrhoeae and the presence or absence of the seven AMR determinants. There was some cross-reactivity with non-gonococcal Neisseria species and the detection limit was 10(3)-10(4) gDNA copies/reaction. Overall, the platform accurately detected resistance to ciprofloxacin (sensitivity and specificity, 100%), ceftriaxone (sensitivity 100%, specificity 90%), cefixime (sensitivity 92%, specificity 94%), azithromycin and spectinomycin (both sensitivity and specificity, 100%). In conclusion, our methodology accurately detects mutations generating resistance to antibiotics used to treat gonorrhea. Low assay sensitivity prevents direct diagnostic testing of clinical specimens but this method can be used to screen collections of gonococcal isolates for AMR more quickly than with current culture-based AMR testing.