Rapid Bidirectional Switching of Synaptic NMDA Receptors

SummarySynaptic NMDA-type glutamate receptors (NMDARs) play important roles in synaptic plasticity, brain development, and pathology. In the last few years, the view of NMDARs as relatively fixed components of the postsynaptic density has changed. A number of studies have now shown that both the number of receptors and their subunit compositions can be altered. During development, the synaptic NMDARs subunit composition changes, switching from predominance of NR2B-containing to NR2A-containing receptors, but little is known about the mechanisms involved in this developmental process. Here, we report that, depending on the pattern of NMDAR activation, the subunit composition of synaptic NMDARs is under extremely rapid, bidirectional control at neonatal synapses. This switching, which is at least as rapid as that seen with AMPARs, will have immediate and dramatic consequences on the integrative capacity of the synapse

An examination of predictors that increase educational aspiration to attend university: a longitudinal study of high school students from low socioeconomic backgrounds

An examination of student educational aspirations, low socioeconomic student attrition and retention and intervention programs designed to increase university participation

UV Shielding of Bacillus pumilus SAFR-032 Endospores by Martian Regolith Simulants

As exploration of the solar system advances with life detection missions on the horizon, the concern for planetary protection has grown considerably. When attempting to detect extraterrestrial life, the likelihood of false positives from terrestrial contamination must be minimized. The Exposing Microorganisms in the Stratosphere (E-MIST) balloon project aims to evaluate whether resilient terrestrial bacteria can survive stressors in a Mars-like environment. This is accomplished by sending Bacillus pumilus SAFR-032, an endospore-forming bacterial isolate from a spacecraft assembly facility, to the Earth's middle stratosphere (30-38 kilometers), where low temperature and pressure and high radiation and dryness conditions are similar to the surface of Mars. Previous ground and flight tests showed that the vast majority of SAFR-032 spores (99.99 percent) were inactivated by direct sunlight due to ultraviolet (UV) radiation. This observation led us to explore the role of dust shielding in changing microbial survivorship outcomes. To determine the dust particle distributions and density for potentially shielding microbes from UV radiation, samples of a Martian dust simulant were mixed with SAFR-032 spores. The dry heat sterilized simulant used was JSC MARS-1, weathered volcanic ash from Hawaii that displays many chemical and physical properties similar to the Martian soil as characterized by the Viking Lander 1, including reflectance spectrum, chemical composition, mineralogy, grain size, specific gravity, and magnetic properties. First, scanning electron microscopy was undertaken to visualize the aggregation of the spores with dust particles (i.e., shading effects), and samples of varying dust concentrations were subsequently irradiated with UVC light to test survivorship outcomes. After a relationship between dust concentration and spore survivorship was determined, a solar simulator capable of irradiating samples with a fuller UV spectrum (less than 280-400 nanometers) was used to perform a more robust middle stratosphere simulation. Taken together, we will use results from the ground-based irradiation studies to feed into experimental designs for the next E-MIST ultra-long duration polar balloon flight launched by NASA

An examination of predictors that increase educational aspiration to attend university: a longitudinal study of high school students from low socioeconomic backgrounds

Considerable resources have been funnelled into designing and implementing effective intervention programs aimed at reducing student attrition. However there is a lack of knowledge regarding the impacts of these programs. There are numerous studies on outreach programs designed to widen student participation; however, these have been criticised for failing to demonstrate independence and that they are limited to qualitative analysis and small sample sizes. Additionally, effective intervention programs that address socioeconomic deficits in educational attainment are inadequate. Moreover, the psychometrically- evaluated measures designed to assess high school student educational aspiration lack the appropriate rigour in relation to randomised designs utilising treatment and control groups. In response to these challenges, this thesis had four aims. Aim 1 was to design a survey to measure high school student educational aspiration and related student characteristics. Aim 2 was to assess the correlations between educational aspiration and relevant student characteristics (i.e., educational engagement, educational self-efficacy, achievement goal setting, perceptions of school quality, school friendships and life satisfaction). Aim 3 was to assess the effectiveness of differing university-high school partnership intervention programs, using pre-post treatment-control designs. Aim 4 was to measure how educational aspiration and student characteristics changed over the first four years of high school. To achieve these aims, a series of five studies were conducted. Addressing Aims 1 and 2, Study 1 involved the development and refinement of a measurement tool that assessed factors related to student attrition, retention, and educational aspiration. This resulted in the development of six student scales measuring student characteristics that were subsequently correlated with educational aspiration. Factor analysis, reliability analysis, as well as qualitative assessment of items, were used to refine the set of items used to measure the six scales. Addressing Aim 3, Studies 2, 3 and 4 assessed the effectiveness of three Year 7 intervention programs designed to increase low socioeconomic high school students’ educational aspiration to complete school and attend university. Participants were assigned to a treatment or control group, with the measure developed in Study 1 administered before and after the intervention. Analyses indicated that none of the interventions had a significant effect on educational aspiration or the other measured student characteristics. Addressing Aim 3 and 4, Study 5 used a longitudinal design to examine four intervention programs and the cumulative effects of these on one student cohort tracked over 4 years from Year 7 to Year 10 of high school. This study also sought to examine how student characteristics (i.e., educational engagement, educational self-efficacy, achievement goal setting, perceptions of school quality, school friendships and life satisfaction) changed over this period. Results showed student characteristics and aspiration levels declined as students progressed through high school. The greatest declines occurred at the start of high school and tended to plateau around Year 8, 9, with small increases in Year 10. The interventions showed no significant influence on student characteristics and there was no evidence of a cumulative effect of these interventions. In summary, these five studies formed a four-year longitudinal examination of the educational aspirations of students at low socioeconomic high schools in Australia, Victoria, within the Melbourne and the Greater Geelong area. Taken together, these five studies make an important contribution to the national and international literature on educational aspiration. First, the need to develop a psychometrically sound instrument was identified. Second, significant moderate correlational relationships were found between educational aspirations and key predictors of educational aspiration. Third, although no positive effects were found from the intervention programs, these studies demonstrated that simple and relatively short interventions such as the ones examined are often insufficient to lead to lasting aspirational change for students. Fourth, although educational aspiration and the predictors of educational aspirations did not increase as students progressed through high school, this study provided a detailed picture of how educational aspiration and related student characteristics changed from Year 7 to Year 10 in a low socioeconomic school environment. A valuable contribution was made to research pertaining to educational aspirations, predictors of educational aspirations and intervention programs aimed at increasing the educational aspirations of low socioeconomic students. Although no positive effects were found from the intervention programs offered, these five studies contributed to our understanding of which interventions work and how best to design and implement future intervention programs such as these. Furthermore, this series of studies increased our understanding of student characteristics predictive of educational aspiration, in addition to how these characteristics change over the trajectory of high school. It was found that simple intervention programs were insufficient in leading to lasting aspirational change for students. These findings, therefore, inform on intervention design and implementation

Integrated population models poorly estimate the demographic contribution of immigration

Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated population models (IPMs) offer a possibility to estimate the contributions of additional demographic parameters, for which no data have been explicitly collected-typically immigration. Such parameters are often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated. To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using northern wheatear Oenanthe oenanthe population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration and sample size. We also used empirical data on populations with known rates of immigration: Soay sheep Ovis aries and Mauritius kestrel Falco punctatus with zero immigration and grey wolf Canis lupus in Scandinavia with near-zero immigration. IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near-zero (kestrel 19.1%-98.2%, sheep 4.2%-36.1% and wolf 84.0%-99.2%). Although the estimation of the contribution of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically, large sample sizes may be required to estimate the contribution of immigration well. To minimize the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (a) look for evidence of variation in immigration before investigating its contribution to population growth, (b) simulate and model data for comparison to the real data and (c) use explicit data on immigration when possible

Integrated population models poorly estimate the demographic contribution of immigration

1. Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated population models (IPMs) offer a possibility to estimate the contributions of additional demographic parameters, for which no data have been explicitly collected— typically immigration. Such parameters are often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated. 2. To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using northern wheatear Oenanthe oenanthe population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration and sample size. We also used empirical data on populations with known rates of immigration: Soay sheep Ovis aries and Mauritius kestrel Falco punctatus with zero immigration and grey wolf Canis lupus in Scandinavia with near-zero immigration. 3. IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near-zero (kestrel 19.1%–98.2%, sheep 4.2%– 36.1% and wolf 84.0%–99.2%). Although the estimation of the contribution of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically, large sample sizes may be required to estimate the contribution of immigration well. 4. To minimize the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (a) look for evidence of variation in immigration before investigating its contribution to population growth, (b) simulate and model data for comparison to the real data and (c) use explicit data on immigration when possible

Integrated population models poorly estimate the demographic contribution of immigration

Abstract Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated population models (IPMs) offer a possibility to estimate the contributions of additional demographic parameters, for which no data have been explicitly collected—typically immigration. Such parameters are often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated. To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using northern wheatear Oenanthe oenanthe population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration and sample size. We also used empirical data on populations with known rates of immigration: Soay sheep Ovis aries and Mauritius kestrel Falco punctatus with zero immigration and grey wolf Canis lupus in Scandinavia with near‐zero immigration. IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near‐zero (kestrel 19.1%–98.2%, sheep 4.2%–36.1% and wolf 84.0%–99.2%). Although the estimation of the contribution of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically, large sample sizes may be required to estimate the contribution of immigration well. To minimize the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (a) look for evidence of variation in immigration before investigating its contribution to population growth, (b) simulate and model data for comparison to the real data and (c) use explicit data on immigration when possible