Increasing the career choice readiness of young adolescents: an evaluation study

A career workshop that applies models of the Cognitive Information Processing Approach (Sampson, Reardon, Peterson, & Lenz, 2004) and incorporates critical ingredients (Brown and Ryan Krane, 2000) to promote the career choice readiness of young adolescents was developed and evaluated with 334 Swiss students in seventh grade applying a Solomon four group design with a three-month follow-up. Participants significantly increased their performance in terms of career decidedness, career planning, career exploration, and vocational identity. Implications for evaluation research and counselling practice are presented.

The relation of secondary student's career choice readiness to a six-phase model of career decision-making

Based on common aspects of recent models of career decision-making (CDM) a sixphase model of CDM for secondary students is presented and empirically evaluated. The study tested the hypothesis that students who are in later phases possess more career choice readiness and consider different numbers of career alternatives. 266 Swiss secondary students completed measures tapping phase of CDM, career choice readiness, and number of considered career options. Career choice readiness showed an increase with phase of CDM. Later phases were generally associated with a larger increase in career choice readiness. Number of considered career options showed a curve-linear development with fewer options considered at the beginning and at the end of the process. Male students showed a larger variability in their distribution among the process with more male than female students in the first and last phase of the process. Implications for theory and practice are presented.

Direct mate choice maintains diversity among sympatric cichlids in Lake Victoria

Mate choice may play an important role in animal speciation. The haplochromine cichlids of Lake Victoria are suitable to test this hypothesis. Diversity in ecology, coloration and anatomy evolved in these fish faster than postzygotic barriers to gene flow, and little is known about how this diversity is maintained. It was tested whether recognizable forms are selection-maintained morphs or reproductively isolated species by investigating in the field reproductive timing, location of spawning sites, and mate choice behaviour. There was a large interspecific overlap in timing of breeding and location of spawning sites, which was largest in members of the same genus. Behavioural mate choice of such closely related taxa was highly assortative, such that it is likely that they are sexually isolated species and that direct mate choice is the major force that directs gene flow and maintains form diversity. The results differ from what is known about recent radiations of other lacustrine fish groups where speciation seems to be driven by diverging microhabitat preferences or diverging timing of reproduction, but are in agreement with predictions from models of speciation by diverging mate preferences.

A common mechanism underlying food choice and social decisions

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others’ benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making.

Wound models for periodontal and bone regeneration: the role of biologic research

The ultimate goals of periodontal therapy remain the complete regeneration of those periodontal tissues lost to the destructive inflammatory-immune response, or to trauma, with tissues that possess the same structure and function, and the re-establishment of a sustainable health-promoting biofilm from one characterized by dysbiosis. This volume of Periodontology 2000 discusses the multiple facets of a transition from therapeutic empiricism during the late 1960s, toward regenerative therapies, which is founded on a clearer understanding of the biophysiology of normal structure and function. This introductory article provides an overview on the requirements of appropriate in vitro laboratory models (e.g. cell culture), of preclinical (i.e. animal) models and of human studies for periodontal wound and bone repair. Laboratory studies may provide valuable fundamental insights into basic mechanisms involved in wound repair and regeneration but also suffer from a unidimensional and simplistic approach that does not account for the complexities of the in vivo situation, in which multiple cell types and interactions all contribute to definitive outcomes. Therefore, such laboratory studies require validatory research, employing preclinical models specifically designed to demonstrate proof-of-concept efficacy, preliminary safety and adaptation to human disease scenarios. Small animal models provide the most economic and logistically feasible preliminary approaches but the outcomes do not necessarily translate to larger animal or human models. The advantages and limitations of all periodontal-regeneration models need to be carefully considered when planning investigations to ensure that the optimal design is adopted to answer the specific research question posed. Future challenges lie in the areas of stem cell research, scaffold designs, cell delivery and choice of growth factors, along with research to ensure appropriate gingival coverage in order to prevent gingival recession during the healing phase.

Habitat Choice and Female Preference in a Polymorphic Stickleback Population

Background: A small pond, c. 90 years old, near Bern, Switzerland contains a population of threespine stickleback (Gasterosteus aculeatus) with two distinct male phenotypes. Males of one type are large, and red, and nest in the shallow littoral zone. The males of the other are small and orange, and nest offshore at slightly greater depth. The females in this population are phenotypically highly variable but cannot easily be assigned to either male type. Question: Is the existence of two sympatric male morphs maintained by substrate-associated male nest site choice and facilitated by female mate preferences? Organisms: Male stickleback caught individually at their breeding sites. Females caught with minnow traps. Methods: In experimental tanks, we simulated the slope and substrate of the two nesting habitats. We then placed individual males in a tank and observed in which habitat the male would build his nest. In a simultaneous two-stimulus choice design, we gave females the choice between a large, red male and a small, orange one. We measured female morphology and used linear mixed effect models to determine whether female preference correlated with female morphology. Results: Both red and orange males preferred nesting in the habitat that simulated the slightly deeper offshore condition. This is the habitat occupied by the small, orange males in the pond itself. The proportion of females that chose a small orange male was similar to that which chose a large red male. Several aspects of female phenotype correlated with the male type that a female preferred.

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Assessing the improved discriminatory power of a new biomarker in prognostic models

Although the area under the receiver operating characteristic (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new biomarker in the model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this dissertation, we have extended the NRI and IDI to survival analysis settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies were conducted to compare the performance of the time-dependent NRI and IDI with Pencina’s NRI and IDI. For illustration, we have applied the proposed method to a breast cancer study.^ Key words: Prognostic model, Discrimination, Time-dependent NRI and IDI ^

Patients' preferences for a prostate cancer consultation, a discrete choice experiment

Four basic medical decision making models are commonly discussed in the literature in reference to physician-patient interactions. All fall short in their attempt to capture the nuances of physician-patient interactions, and none satisfactorily address patients' preferences for communication and other attributes of care. Prostate cancer consultations are one setting where preferences matter and are likely to vary among patients. Fortunately, discrete choice experiments are capable of casting light on patients' preferences for communication and other attributes of value that make up a consultation before the consultation occurs, which is crucial if patients are to derive the most utility from the process of reaching a decision as well as the decision itself. The results of my dissertation provide strong support to the notion that patients, at least in the hypothetical setting of a DCE, have identifiable preferences for the attributes of a prostate cancer consultation and that those preferences are capable of being elicited before a consultation takes place. Further, patients' willingness-to-pay for the non-cost attributes of the consultation is surprisingly robust to a variety of individual level variables of interest. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Pitfalls of location choice analysis : the finished goods producer versus the intermediate goods producer

In literature related to firm location choice, estimation equations are derived from the model of finished goods producers, but producer types are generally not considered. Research presented in this paper shows that the use of equations derived from such models against intermediate goods producers results in several problems.

Chapter 4 The Challenges in Developing Realistic NEG Simulation Models for East Asia

This chapter attempts to identify some important issues in developing realistic simulation models based on new economic geography, and it suggests a direction for solving the difficulties. Specifically, adopting the IDE Geographical Simulation Model (IDE-GSM) as an example, we discuss some problems in developing a realistic simulation model for East Asia. The first and largest problem in this region is the lack of reliable economic datasets at the sub-national level, and this issue needs to be resolved in the long term. However, to deal with the existing situation in the short term, we utilize some techniques to produce more realistic and reliable simulation models. One key compromise is to use a 'topology' representation of geography, rather than a 'mesh' or 'grid' representation or simple 'straight lines' connecting each city which are used in many other models. In addition to this, a modal choice model that takes into consideration both money and time costs seems to work well.

Behavioral characteristics of applied general equilibrium models with an Armington-Krugman-Melitz encompassing module

This paper explore how simulation results change with different choice of trade specification, and the strength of preference for traded variety by economic agent differs, utilizing two types of three-region, three-sector AGE model that includes the Armington-Krugman-Melitz Encompassing module based on Dixon and Rimmer (2012). Simulation experiments reveal that: (1) the Melitz-type specification does not always enhance effectiveness of a certain policy change more than the one obtained with the Krugman-type, especially when economic agents' preference for traded variety is not so strong; (2) there are likely to be points where the volumes of effects obtained with the Melitz-type exceed the ones with the Krugman-type; and (3) the preference of the producers, those who are in the sectors that exhibit increasing returns to scale, for traded variety might be the engine of explosive effects as suggested by Fujita, et al. (2000).

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Analysis of Chromatic Aberration Effects in Triple-Junction Solar Cells Using Advanced Distributed Models

The consideration of real operating conditions for the design and optimization of a multijunction solar cell receiver-concentrator assembly is indispensable. Such a requirement involves the need for suitable modeling and simulation tools in order to complement the experimental work and circumvent its well-known burdens and restrictions. Three-dimensional distributed models have been demonstrated in the past to be a powerful choice for the analysis of distributed phenomena in single- and dual-junction solar cells, as well as for the design of strategies to minimize the solar cell losses when operating under high concentrations. In this paper, we present the application of these models for the analysis of triple-junction solar cells under real operating conditions. The impact of different chromatic aberration profiles on the short-circuit current of triple-junction solar cells is analyzed in detail using the developed distributed model. Current spreading conditions the impact of a given chromatic aberration profile on the solar cell I-V curve. The focus is put on determining the role of current spreading in the connection between photocurrent profile, subcell voltage and current, and semiconductor layers sheet resistance.