Effects of short- and long-term variations in RLS severity on perceived health status - the COR-study.

In a cohort study among 2751 members (71.5% females) of the German and Swiss RLS patient organizations changes in restless legs syndrome (RLS) severity over time was assessed and the impact on quality of life, sleep quality and depressive symptoms was analysed. A standard set of scales (RLS severity scale IRLS, SF-36, Pittsburgh Sleep Quality Index and the Centre for Epidemiologic Studies Depression Scale) in mailed questionnaires was repeatedly used to assess RLS severity and health status over time and a 7-day diary once to assess short-term variations. A clinically relevant change of the RLS severity was defined by a change of at least 5 points on the IRLS scale. During 36 months follow-up minimal improvement of RLS severity between assessments was observed. Men consistently reported higher severity scores. RLS severity increased with age reaching a plateau in the age group 45-54 years. During 3 years 60.2% of the participants had no relevant (±5 points) change in RLS severity. RLS worsening was significantly related to an increase in depressive symptoms and a decrease in sleep quality and quality of life. The short-term variation showed distinctive circadian patterns with rhythm magnitudes strongly related to RLS severity. The majority of participants had a stable course of severe RLS over three years. An increase in RLS severity was accompanied by a small to moderate negative, a decrease by a small positive influence on quality of life, depressive symptoms and sleep quality.

Long-Term Effects of the Treatment of Depressive Female Inpatients in a Naturalistic Study: Is Early Improvement a Valid Predictor of Outcome?

Objectives: To examine the predictive value of early improvement for short- and long-term outcome in the treatment of depressive female inpatients and to explore the influence of comorbid disorders (CD). Methods: Archival data of a naturalistic sample of 277 female inpatients diagnosed with a depressive disorder was analyzed assessing the BDI at baseline, after 20 days and 30 days, posttreatment, and after 3 to 6 months at follow-up. Early improvement, defined as a decrease in the BDI score of at least 30% after 20 and after 30 days, and CD were analyzed using binary logistic regression. Results: Both early improvement definitions were predictive of remission at posttreatment. Early improvement after 30 days showed a sustained treatment effect in the follow-up phase, whereas early improvement after 20 days failed to show a persistent effect regarding remission at follow-up. CD were not significantly related neither at posttreatment nor at follow-up. At no time point CD moderated the prediction by early improvement. Conclusions: We show that early improvement is a valid predictor for short-term remission and at follow-up in an inpatient setting. CD did not predict outcome. Further studies are needed to identify patient subgroups amenable to more tailored treatments.

Do avoidance goals always impair performance? How neuroticism moderates the short- and long-term effects of avoidance versus approach goals on performance

Previous research agrees that approach goals have positive effects whereas avoidance goals have negative effects on performance. By contrast, the present chapter looks at the conditions under which even avoidance goals may have positive effects on performance. We will first review the previous research that supports the positive consequences of avoidance goals. Then we will argue that the positive and negative consequences of approach and avoidance goals on performance depend on an individual‘s neuroticism level and the time frame of their goal striving. Because neuroticism is positively related to avoidance goals, we assume that individuals with high levels of neuroticism may derive some benefits from avoidance goals. We have specified this assumption by hypothesizing that the fit between an individual‘s level of neuroticism and their avoidance goals leads to favorable consequences in the short term – but to negative outcomes in the long run. A short-term, experimental study with employees and a long-term correlative field study with undergraduate students were conducted to test whether neuroticism moderates the short- and long-term effects of avoidance versus approach goals on performance. Experimental study 1 showed that individuals with a high level of neuroticism performed best in the short term when they were assigned to avoidance goals, whereas individuals with a low level of neuroticism performed best when pursuing approach goals. However, study 2 indicated that in the long run individuals with a high level of neuroticism performed worse when striving for avoidance goals, whereas individuals with a low level of neuroticism were not impaired at all by avoidance goals. In summary, the pattern of results supports the hypothesis that a fit between a high level of neuroticism and avoidance goals has positive consequences in the short term, but leads to negative outcomes in the long run. We strongly encourage further research to investigate short- and long-term effects of approach and avoidance goals on performance in conjunction with an individual‘s personality, which may moderate these effects.

Program evaluation for a summer science internship: Short and long term benefits as reported by interns

Undergraduate research programs have been used as a tool to attract and retain student interest in science careers. This study evaluates the short and long-term benefits of a Summer Science Internship (SSI) at the University of Texas Health Science Center at Houston– School of Public Health – in Brownsville, Texas, by analyzing survey data from alumni. Questions assessing short-term program impact were aimed at three main topics, student: satisfaction with program, self-efficacy for science after completing the program, and perceived benefits. Long-term program impact was assessed by looking at student school attendance and college majors along with perceived links between SSI and future college plans. Students reported high program satisfaction, a significant increase in science self-efficacy and high perceived benefits. At the time data were collected for the study, one-hundred percent of alumni were enrolled in school (high school or college). The majority of students indicated they were interested in completing a science major/career, heavily influenced by their participation in the program.^

Short- versus long-term responses to changing CO2 in a coastal dinoflagellate bloom

Increasing pCO2 (partial pressure of CO2 ) in an "acidified" ocean will affect phytoplankton community structure, but manipulation experiments with assemblages briefly acclimated to simulated future conditions may not accurately predict the long-term evolutionary shifts that could affect inter-specific competitive success. We assessed community structure changes in a natural mixed dinoflagellate bloom incubated at three pCO2 levels (230, 433, and 765 ppm) in a short-term experiment (2 weeks). The four dominant species were then isolated from each treatment into clonal cultures, and maintained at all three pCO2 levels for approximately 1 year. Periodically (4, 8, and 12 months), these pCO2 -conditioned clones were recombined into artificial communities, and allowed to compete at their conditioning pCO2 level or at higher and lower levels. The dominant species in these artificial communities of CO2 -conditioned clones differed from those in the original short-term experiment, but individual species relative abundance trends across pCO2 treatments were often similar. Specific growth rates showed no strong evidence for fitness increases attributable to conditioning pCO2 level. Although pCO2 significantly structured our experimental communities, conditioning time and biotic interactions like mixotrophy also had major roles in determining competitive outcomes. New methods of carrying out extended mixed species experiments are needed to accurately predict future long-term phytoplankton community responses to changing pCO2 .

Short- and long-term conditioning of a temperate marine diatom community to acidification and warming

Ocean acidification and greenhouse warming will interactively influence competitive success of key phytoplankton groups such as diatoms, but how long-term responses to global change will affect community structure is unknown. We incubated a mixed natural diatom community from coastal New Zealand waters in a short-term (two-week) incubation experiment using a factorial matrix of warming and/or elevated pCO2 and measured effects on community structure. We then isolated the dominant diatoms in clonal cultures and conditioned them for 1 year under the same temperature and pCO2 conditions from which they were isolated, in order to allow for extended selection or acclimation by these abiotic environmental change factors in the absence of interspecific interactions. These conditioned isolates were then recombined into 'artificial' communities modelled after the original natural assemblage and allowed to compete under conditions identical to those in the short-term natural community experiment. In general, the resulting structure of both the unconditioned natural community and conditioned 'artificial' community experiments was similar, despite differences such as the loss of two species in the latter. pCO2 and temperature had both individual and interactive effects on community structure, but temperature was more influential, as warming significantly reduced species richness. In this case, our short-term manipulative experiment with a mixed natural assemblage spanning weeks served as a reasonable proxy to predict the effects of global change forcing on diatom community structure after the component species were conditioned in isolation over an extended timescale. Future studies will be required to assess whether or not this is also the case for other types of algal communities from other marine regimes.

Use of prediction methods for patent and trademark applications in Spain

Patent and trademark offices which run according to principles of new management have an inherent need for dependable forecasting data in planning capacity and service levels. The ability of the Spanish Office of Patents and Trademarks to carry out efficient planning of its resource needs requires the use of methods which allow it to predict the changes in the number of patent and trademark applications at different time horizons. The approach for the prediction of time series of Spanish patents and trademarks applications (1979e2009) was based on the use of different techniques of time series prediction in a short-term horizon. The methods used can be grouped into two specifics areas: regression models of trends and time series models. The results of this study show that it is possible to model the series of patents and trademarks applications with different models, especially ARIMA, with satisfactory model adjustment and relatively low error.

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Effects of Short- and Long-Term Elevated CO2 on the Expression of Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase Genes and Carbohydrate Accumulation in Leaves of Arabidopsis thaliana (L.) Heynh.1

To investigate the proposed molecular characteristics of sugar-mediated repression of photosynthetic genes during plant acclimation to elevated CO2, we examined the relationship between the accumulation and metabolism of nonstructural carbohydrates and changes in ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) gene expression in leaves of Arabidopsis thaliana exposed to elevated CO2. Long-term growth of Arabidopsis at high CO2 (1000 μL L−1) resulted in a 2-fold increase in nonstructural carbohydrates, a large decrease in the expression of Rubisco protein and in the transcript of rbcL, the gene encoding the large subunit of Rubisco (approximately 35–40%), and an even greater decline in mRNA of rbcS, the gene encoding the small subunit (approximately 60%). This differential response of protein and mRNAs suggests that transcriptional/posttranscriptional processes and protein turnover may determine the final amount of leaf Rubisco protein at high CO2. Analysis of mRNA levels of individual rbcS genes indicated that reduction in total rbcS transcripts was caused by decreased expression of all four rbcS genes. Short-term transfer of Arabidopsis plants grown at ambient CO2 to high CO2 resulted in a decrease in total rbcS mRNA by d 6, whereas Rubisco content and rbcL mRNA decreased by d 9. Transfer to high CO2 reduced the maximum expression level of the primary rbcS genes (1A and, particularly, 3B) by limiting their normal pattern of accumulation through the night period. The decreased nighttime levels of rbcS mRNA were associated with a nocturnal increase in leaf hexoses. We suggest that prolonged nighttime hexose metabolism resulting from exposure to elevated CO2 affects rbcS transcript accumulation and, ultimately, the level of Rubisco protein.

Leading edge forecasting techniques for exchange rate prediction

This paper describes how modern machine learning techniques can be used in conjunction with statistical methods to forecast short term movements in exchange rates, producing models suitable for use in trading. It compares the results achieved by two different techniques, and shows how they can be used in a complementary fashion. The paper draws on experience of both inter- and intra-day forecasting taken from earlier studies conducted by Logica and Chemical Bank Quantitative Research and Trading (QRT) group's experience in developing trading models.

Short- and long-term changes in corneal aberrations and axial length induced by orthokeratology in children are not correlated

PURPOSE: To assess the correlation between changes in corneal aberrations and the 2-year change in axial length in children fitted with orthokeratology (OK) contact lenses. METHODS: Thirty-one subjects 6 to 12 years of age and with myopia −0.75 to −4.00DS and astigmatism ≤1.00DC were fitted with OK. Measurements of axial length and corneal topography were taken at regular intervals over a 2-year period. Corneal topography at baseline and after 3 and 24 months of OK lens wear was used to derive higher-order corneal aberrations (HOA) that were correlated with OK-induced axial length changes at 2 years. RESULTS: Significant changes in C3, C4, C4, root mean square (RMS) secondary astigmatism and fourth and total HOA were found with both 3 and 24 months of OK lens wear in comparison with baseline (all P0.05). Coma angle of orientation changed significantly pre-OK in comparison with 3 and 24 months post-OK as well as secondary astigmatism angle of orientation pre-OK in comparison with 24 months post-OK (all P0.05). DISCUSSION: Short-term and long-term OK lens wear induces significant changes in corneal aberrations that are not significantly correlated with changes in axial elongation after 2-years.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Arctic ecosystem responses to changes in water availability and warming: Short and long-term responses

Arctic soils store close to 14% of the global soil carbon. Most of arctic carbon is stored below ground in the permafrost. With climate warming the decomposition of the soil carbon could represent a significant positive feedback to global greenhouse warming. Recent evidence has shown that the temperature of the Arctic is already increasing, and this change is associated mostly with anthropogenic activities. Warmer soils will contribute to permafrost degradation and accelerate organic matter decay and thus increase the flux of carbon dioxide and methane into the atmosphere. Temperature and water availability are also important drivers of ecosystem performance, but effects can be complex and in opposition. Temperature and moisture changes can affect ecosystem respiration (ER) and gross primary productivity (GPP) independently; an increase in the net ecosystem exchange can be a result of either a decrease in ER or an increase in GPP. Therefore, understanding the effects of changes in ecosystem water and temperature on the carbon flux components becomes key to predicting the responses of the Arctic to climate change. The overall goal of this work was to determine the response of arctic systems to simulated climate change scenarios with simultaneous changes in temperature and moisture. A temperature and hydrological manipulation in a naturally-drained lakebed was used to assess the short-term effect of changes in water and temperature on the carbon cycle. Also, as part of International Tundra Experiment Network (ITEX), I determined the long-term effect of warming on the carbon cycle in a natural hydrological gradient established in the mid 90's. I found that the carbon balance is highly sensitive to short-term changes in water table and warming. However, over longer time periods, hydrological and temperature changed soil biophysical properties, nutrient cycles, and other ecosystem structural and functional components that down regulated GPP and ER, especially in wet areas.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Long-term modifications of coastal defences enhance marine biodiversity

Realization that hard coastal infrastructures support lower biodiversity than natural habitats has prompted a wealth of research seeking to identify design enhancements offering ecological benefits. Some studies showed that artificial structures could be modified to increase levels of diversity. Most studies, however, only considered the short-term ecological effects of such modifications, even though reliance on results from short-term studies may lead to serious misjudgements in conservation. In this study, a seven-year experiment examined how the addition of small pits to otherwise featureless seawalls may enhance the stocks of a highly-exploited limpet. Modified areas of the seawall supported enhanced stocks of limpets seven years after the addition of pits. Modified areas of the seawall also supported a community that differed in the abundance of littorinids, barnacles and macroalgae compared to the controls. Responses to different treatments (numbers and size of pits) were species-specific and, while some species responded directly to differences among treatments, others might have responded indirectly via changes in the distribution of competing species. This type of habitat enhancement can have positive long-lasting effects on the ecology of urban seascapes. Understanding of species interactions could be used to develop a rule-based approach to enhance biodiversity.