Well-being and learning in school

Although there are various definitions for the term “well-being,” it is agreed that well-being in school represents a set of subjective feelings and attitudes toward school. Moreover, enjoyment (some use the term “happiness”) is recognized as a core element of well-being in general as well as at school. Well-being in school is defined as an indicator of the quality of scholastic life, and contributes to students’ physical and psychological health and development. As such it is strongly connected to learning. Well-being in school consists of cognitive, emotional, and physical components, i.e., a learner’s thoughts, feelings, and bodily sensations. Consequently, it differs significantly from an individual’s cognitive appraisals like satisfaction, or from discrete positive emotions like enjoyment. Well-being in school can be described through the relationship of positive and negative aspects of school life

The Role of BDNF, Leptin and Catecholamines in Reward Learning in Bulimia Nervosa

Background: A relationship between bulimia nervosa (BN) and reward-related behavior is supported by several lines of evidence. The dopaminergic dysfunctions in the processing of reward-related stimuli have been shown to be modulated by the neurotrophin brain derived neurotrophic factor (BDNF) and the hormone leptin. Methods: Using a randomized, double-blind, placebo-controlled, crossover design, a reward learning task was applied to study the behavior of 20 female subjects with remitted BN (rBN) and 27 female healthy controls under placebo and catecholamine depletion with alpha-methyl-para-tyrosine (AMPT). The plasma levels of BDNF and leptin were measured twice during the placebo and the AMPT condition, immediately before and 1 h after a standardized breakfast. Results: AMPT-induced differences in plasma BDNF levels were positively correlated with the AMPT-induced differences in reward learning in the whole sample (p = 0.05). Across conditions, plasma BDNF levels were higher in rBN subjects compared to controls (diagnosis effect; p = 0.001). Plasma BDNF and leptin levels were higher in the morning before compared to after a standardized breakfast across groups and conditions (time effect; p < 0.0001). The plasma leptin levels were higher under catecholamine depletion compared to placebo in the whole sample (treatment effect; p = 0.0004). Conclusions: This study reports on preliminary findings that suggest a catecholamine-dependent association of plasma BDNF and reward learning in subjects with rBN and controls. A role of leptin in reward learning is not supported by this study. However, leptin levels were sensitive to a depletion of catecholamine stores in both rBN and controls.

THEODORE, a two-step heating system for the EC/OC determination of radiocarbon (14C) in the environment

Measurements of 14C in the organic carbon (OC) and elemental carbon (EC) fractions, respectively, of fine aerosol particles bear the potential to apportion anthropogenic and biogenic emission sources. For this purpose, the system THEODORE (two-step heating system for the EC/OC determination of radiocarbon in the environment) was developed. In this device, OC and EC are transformed into carbon dioxide in a stream of oxygen at 340 and 650 �C, respectively, and reduced to filamentous carbon. This is the target material for subsequent accelerator mass spectrometry (AMS) 14C measurements, which were performed on sub-milligram carbon samples at the PSI/ETH compact 500 kV AMS system. Quality assurance measurements of SRM 1649a, Urban Dust, yielded a fraction of modern fM in total carbon (TC) of 0.522 ±0.018 (n ¼ 5, 95% confidence level) in agreement with reported values. The results for OC and EC are 0.70± 0.05 (n ¼ 3) and 0.066 ± 0.020 (n ¼ 4), respectively.

Learning in the cerebellar nucleus is necessary for acquisition of Pavlovian eyelid conditioning

The cumulative work presented here supports the hypothesis that plasticity in the cerebellar cortex and cerebellar nuclei mediates a simple associative form of motor teaming-Pavlovian eyelid conditioning. It was previously demonstrated that focal ablative lesions of cerebellar anterior lobe or pharmacological block of the cerebellar cortex output disrupted the timing of the conditioned eyeblink response, unmasking a response with a relatively fixed and very short latency to onset. The results of this thesis demonstrate that the short-latency responses are due to associative learning. Unpaired training does not support the acquisition of short-latency responses while the rate of acquisition of short-latency responses during paired training is approximately the same as that of timed conditioned responses. The acquisition of short-latency responses is dependent on an intact cerebellar cortex. Both ablative lesions of the cerebellar cortex and inactivation of cerebellar cortex output with picrotoxin block the acquisition of short-latency responses. However, once the short-latency responses are acquired neither disconnection of cerebellar cortex nor inactivation of the cerebellar nucleus block reacquisition. The results are consistent with the proposal that plasticity in the cerebellar cortex is necessary for learning the timing of conditioned responses, plasticity in the interpositus nucleus mediates the short latency responses, and cerebellar cortical output and mossy fiber input are necessary for the acquisition of short latency responses. ^

Cognition, Redundancy, and Learning in Organizations

No abstract available.

Motivation and Learning in Mathematics Pre-service Teachers

Based on a review of literature of conceptual and procedural knowledge in relation to intrinsic and extrinsic motivation, the purpose of this study was to test the relationship between conceptual and procedural knowledge and intrinsic and extrinsic motivation. Thirty-eight education students with a mathematics focus (elementary or secondary) in their junior, senior, or fifth year completed a survey with a Likert scale measuring their preference to learning (conceptual or procedural) and their motivation type (intrinsic or extrinsic). Findings showed that secondary mathematics focused students were more likely to prefer learning mathematics conceptually than elementary mathematics focused students. However, secondary and elementary mathematics focused students showed an equal preference for learning mathematics procedurally and sequentially. Elementary and secondary students reported similar intrinsic and extrinsic motivation. Extrinsically motivated students preferred procedural learning more than conceptual learning. While there was no statistically significant preference with intrinsically motivated students, there was a trend favoring preference of conceptual learning over procedural learning. These results tend to support the hypothesis that mathematics focused students who prefer conceptual learning are more intrinsically motivated, and mathematics focused students who prefer procedural learning are more extrinsically motivated.

A descriptive study of the sociodemographic, health and developmental factors associated with slow learning in children from upper to middle social class families in Texas

"Slow Learners" is a term used to describe children with an IQ range of 70-89 on a standardized individual intelligence test (i.e. with a standard deviation of either 15 or 16). They have above retarded, but below average intelligence and potential to learn. If the factors associated with the etiology of slow learning in children can be identified, it may be possible to hypothesize causal relationships which can be tested by intervention studies specifically designed to prevent slow learning. If effective, these may ultimately reduce the incidence of school dropouts and their cost to society. To date, there is little information about variables which may be etiologically significant. In an attempt to identify such etiologic factors this study examines the sociodemographic characteristics, prenatal history (hypertension, smoking, infections, medication, vaginal bleeding, etc.), natal history (length of delivery, Apgar score, birth trauma, resuscitation, etc.), neonatal history (infections, seizures, head trauma, etc.), developmental history (health problems, developmental milestones and growth during infancy and early childhood), and family history (educational level of the parents, occupation, history of similar condition in the family, etc.) of a series of children defined as slow learners. The study is limited to children from middle to high socioeconomic families in order to exclude the possible confounding variable of low socioeconomic status, and because a descriptive study of this group has not been previously reported. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Active Learning in Nursing Education

ALINE is a pedagogical model developed to aid nursing faculty transition from passive to active learning. Based on constructionist theory, ALINE serves as a tool for organizing curriculum for online and classroom based interaction and permits positioning the student as the active player and the instructor, the facilitator to nursing competency.

(Table 1) Metal contents in marine green algae (Chlorophyta) with background and enhanced element concentrations in the environment

Character of metal accumulation in fractions of thalli of four species of marine green benthos algae under background and enhanced (0.3 mg/l) element concentrations in the environment was studied in short-term 24-hour experiments. Algae were shown to hold polysaccharide and protein mechanisms of metal accumulation. Variance analysis was applied to evaluate taxonomic and ecological features of metal distribution in fractions of thalli.

Effects of ocean acidification on learning in coral reef fishes

Ocean acidification has the potential to cause dramatic changes in marine ecosystems. Larval damselfish exposed to concentrations of CO2 predicted to occur in the mid- to late-century show maladaptive responses to predator cues. However, there is considerable variation both within and between species in CO2 effects, whereby some individuals are unaffected at particular CO2 concentrations while others show maladaptive responses to predator odour. Our goal was to test whether learning via chemical or visual information would be impaired by ocean acidification and ultimately, whether learning can mitigate the effects of ocean acidification by restoring the appropriate responses of prey to predators. Using two highly efficient and widespread mechanisms for predator learning, we compared the behaviour of pre-settlement damselfish Pomacentrus amboinensis that were exposed to 440 µatm CO2 (current day levels) or 850 µatm CO2, a concentration predicted to occur in the ocean before the end of this century. We found that, regardless of the method of learning, damselfish exposed to elevated CO2 failed to learn to respond appropriately to a common predator, the dottyback, Pseudochromis fuscus. To determine whether the lack of response was due to a failure in learning or rather a short-term shift in trade-offs preventing the fish from displaying overt antipredator responses, we conditioned 440 or 700 µatm-CO2 fish to learn to recognize a dottyback as a predator using injured conspecific cues, as in Experiment 1. When tested one day post-conditioning, CO2 exposed fish failed to respond to predator odour. When tested 5 days post-conditioning, CO2 exposed fish still failed to show an antipredator response to the dottyback odour, despite the fact that both control and CO2-treated fish responded to a general risk cue (injured conspecific cues). These results indicate that exposure to CO2 may alter the cognitive ability of juvenile fish and render learning ineffective.