Empirical development of a scale of patience

Patience is a construct not directly studied in the literature. Studies in the psychological literature have typically spoken of patience only as the converse of impatience. The assumption of these studies is that patience exists in the absence of impatience. However, other research proposes a multidimensional model of patience based on qualitative studies. It follows from the multidimensional model that patience exists on a continuum with the potential for different levels or amounts of patience across different situations. The purpose of this study was to develop an objective measure of patience.;To develop a measure of patience an item pool was constructed and reviewed, and then 347 undergraduate students completed items. Factor analysis of this initial administration identified nine factors. A final measure was developed and administered to 312 undergraduate students. To assess validity of the patience scale, students completed the Boredom Proneness Scale, the Student Version of the Jenkins Activity Scale, and a modified version of the Questionnaire Measure of Emotional Empathy in addition to the patience measure. Forty undergraduate students completed the measures at a four-week interval to assess temporal stability. Factor analysis utilized the Scree test and Kaiser eigenvalue rule in determining the number of factors to retain. Equamax rotation was the orthogonal method of factor rotation.;A six-factor model of patience was found with strong reliability for the measure as a whole (alpha = .7993) and adequate for individual factors (alpha = .7334--.5226). The six factors explained 48.282 percent of the variance. Temporal stability was high (r = .893). Support was found for convergent validity. Factor labels are postponement, even-tempered, composure, time abundance, tolerance, and limits of patience.;The Patience Scale is discussed in comparison to a sociotemporal model of patience and the other measures used in the study. Future directions for the use of the scale are discussed

Geodiversity influences limnological conditions and freshwater ostracode species distributions across broad spatial scales in the northern Neotropics

Geodiversity is recognized as one of the most important drivers of ecosystem characteristics and biodiversity globally. However, in the northern Neotropics, the contribution of highly diverse landscapes, environmental conditions, and geological history in structuring large-scale patterns of aquatic environments and aquatic species associations remains poorly understood. We evaluated the relationships among geodiversity, limnological conditions, and freshwater ostracodes from southern Mexico to Nicaragua. A cluster analysis (CA), based on geological, geochemical, mineralogical, and water-column physical and chemical characteristics of 76 aquatic ecosystems (karst, volcanic, tectonic) revealed two main limnological regions: (1) karst plateaus of the Yucatán Peninsula and northern Guatemala, and (2) volcanic terrains of the Guatemalan highlands, mid-elevation sites in El Salvador and Honduras, and the Nicaraguan lowlands. In addition, seven subregions were recognized, demonstrating a high heterogeneity of aquatic environments. Principal component analysis (PCA) identified water chemistry (ionic composition) and mineralogy as most influential for aquatic ecosystem classification. Multi-parametric analyses, based on biological data, revealed that ostracode species associations represent disjunct faunas. Five species associations, distributed according to limnological regions, were recognized. Structural equation modeling (SEM) revealed that geodiversity explains limnological patterns of the study area. Limnology further explained species composition, but not species richness. The influence of conductivity and elevation were individually evaluated in SEM and were statistically significant for ostracode species composition, though not for species richness. We conclude that geodiversity has a central influence on the limnological conditions of aquatic systems, which in turn influence ostracode species composition in lakes of the northern Neotropical region

Simulated leakage of high pCO2 water negatively impacts bivalve dominated infaunal communities from the Western Baltic Sea

Carbon capture and storage is promoted as a mitigation method counteracting the increase of atmospheric CO2 levels. However, at this stage, environmental consequences of potential CO2 leakage from sub-seabed storage sites are still largely unknown. In a 3-month-long mesocosm experiment, this study assessed the impact of elevated pCO2 levels (1,500 to 24,400 μatm) on Cerastoderma edule dominated benthic communities from the Baltic Sea. Mortality of C. edule was significantly increased in the highest treatment (24,400 μatm) and exceeded 50%. Furthermore, mortality of small size classes (0–1 cm) was significantly increased in treatment levels ≥6,600 μatm. First signs of external shell dissolution became visible at ≥1,500 μatm, holes were observed at >6,600 μatm. C. edule body condition decreased significantly at all treatment levels (1,500–24,400 μatm). Dominant meiofauna taxa remained unaffected in abundance. Densities of calcifying meiofauna taxa (i.e. Gastropoda and Ostracoda) decreased in high CO2 treatments (>6,600 μatm), while the non - calcifying Gastrotricha significantly increased in abundance at 24,400 μatm. In addition, microbial community composition was altered at the highest pCO2 level. We conclude that strong CO2 leakage can alter benthic infauna community composition at multiple trophic levels, likely due to high mortality of the dominant macrofauna species C. edule

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Non-parametric Methods for Correlation Analysis in Multivariate Data with Applications in Data Mining

In this thesis, we develop novel methods for correlation analysis in multivariate data, with a special focus on mining correlated subspaces. Our methods handle major open challenges arisen when combining correlation analysis with subspace mining. Besides traditional correlation analysis, we explore interaction-preserving discretization of multivariate data and causality analysis. We conduct experiments on a variety of real-world data sets. The results validate the benefits of our methods

Spiking neural models & machine learning for systems neuroscience: Learning, Cognition and Behavior.

Learning, cognition and the ability to navigate, interact and manipulate the world around us by performing appropriate behavior are hallmarks of artificial as well as biological intelligence. In order to understand how intelligent behavior can emerge from computations of neural systems, this thesis suggests to consider and study learning, cognition and behavior simultaneously to obtain an integrative understanding. This involves building detailed functional computational models of nervous systems that can cope with sensory processing, learning, memory and motor control to drive appropriate behavior. The work further considers how the biological computational substrate of neurons, dendrites and action potentials can be successfully used as an alternative to current artificial systems to solve machine learning problems. It challenges the simplification of currently used rate-based artificial neurons, where computational power is sacrificed by mathematical convenience and statistical learning. To this end, the thesis explores single spiking neuron computations for cognition and machine learning problems as well as detailed functional networks thereof that can solve the biologically relevant foraging behavior in flying insects. The obtained results and insights are new and relevant for machine learning, neuroscience and computational systems neuroscience. The thesis concludes by providing an outlook how application of current machine learning methods can be used to obtain a statistical understanding of larger scale brain systems. In particular, by investigating the functional role of the cerebellar-thalamo-cortical system for motor control in primates

MICROBIAL ECOLOGY AND ENDOLITH COLONIZATION: SUCCESSION AT A GEOTHERMAL SPRING IN THE HIGH ARCTIC

A critical question in microbial ecology concerns how environmental conditions affect community makeup. Arctic thermal springs enable study of this question due to steep environmental gradients that impose strong selective pressures. I use microscopic and molecular methods to quantify community makeup at Troll Springs on Svalbard in the high arctic. Troll has two ecosystems, aquatic and terrestrial, in proximity, shaped by different environmental factors. Microorganisms exist in warm water as periphyton, in moist granular materials, and in cold, dry rock as endoliths. Environmental conditions modulate community composition. The strongest relationships of environmental parameters to composition are pH and temperature in aquatic samples, and water content in terrestrial samples. Periphyton becomes trapped by calcite precipitation, and is a precursor for endolithic communities. Microbial succession takes place at Troll in response to incremental environmental disturbances. Photosynthetic organisms are dominantly eukaryotic algae in the wet, high-illumination environments, and Cyanobacteria in the drier, lower-illumination endolithic environments. Periphyton communities vary strongly from pool to pool, with a few dominant taxa. Endolithic communities are more even, with bacterial taxa and cyanobacterial diversity similar to alpine and other Arctic endoliths. Richness and evenness increase with successional age, except in the most mature endolith where they diminish because of sharply reduced resource and niche availability. Evenness is limited in calcite-poor environments by competition with photosynthetic eukaryotes, and in the driest endolith by competition for water. Richness is influenced by availability of physical niches, increasing as calcite grain surfaces become available for colonization, and then decreasing as pore volume decreases. In most endoliths, rock predates microbial colonization; the reverse is true at Troll. The harsh Arctic environment likely imposes a lifestyle in which microbes survive best in embedded formats, and to preserve live inocula for regrowth. ARISA is commonly used to assess variations in microbial community structure. Applying a uniform threshold across a sample set, as is normally done, treats samples non-optimally and unequally. I present an algorithm for optimal threshold selection that maximizes similarity between replicate pairs, improving results

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement