Micro-geographic risk factors for malarial infection.

BACKGROUND: Knowledge of geography is integral to the study of insect-borne infectious disease such as malaria. This study was designed to evaluate whether geographic parameters are associated with malarial infection in the East Sepik province of Papua New Guinea (PNG), a remote area where malaria is a major cause of morbidity and mortality. METHODS: A global positioning system (GPS) unit was used at each village to collect elevation, latitude and longitude data. Concurrently, a sketch map of each village was generated and the villages were sub-divided into regions of roughly equal populations. Blood samples were taken from subjects in each region using filter paper collection. The samples were later processed using nested PCR for qualitative determination of malarial infection. The area was mapped using the GPS-information and overlaid with prevalence data. Data tables were examined using traditional chi square statistical techniques. A logistic regression analysis was then used to determine the significance of geographic risk factors including, elevation, distance from administrative centre and village of residence. RESULTS: Three hundred and thirty-two samples were included (24% of the total estimated population). Ninety-six were positive, yielding a prevalence of 29%. Chi square testing within each village found a non-random distribution of cases across sub-regions (p < 0.05). Multivariate logistic regression techniques suggested malarial infection changed with elevation (OR = 0.64 per 10 m, p < 0.05) and distance from administrative centre (OR = 1.3 per 100 m, p < 0.05). CONCLUSION: These results suggest that malarial infection is significantly and independently associated with lower elevation and greater distance from administrative centre in a rural area in PNG. This type of analysis can provide information that may be used to target specific areas in developing countries for malaria prevention and treatment

Developing and Researching PhET simulations for Teaching Quantum Mechanics

Quantum mechanics is difficult to learn because it is counterintuitive, hard to visualize, mathematically challenging, and abstract. The Physics Education Technology (PhET) Project, known for its interactive computer simulations for teaching and learning physics, now includes 18 simulations on quantum mechanics designed to improve learning of this difficult subject. Our simulations include several key features to help students build mental models and intuitions about quantum mechanics: visual representations of abstract concepts and microscopic processes that cannot be directly observed, interactive environments that directly couple students' actions to animations, connections to everyday life, and efficient calculations so students can focus on the concepts rather than the math. Like all PhET simulations, these are developed using the results of education research and feedback from educators, and are tested in student interviews and classroom studies. This article provides an overview of the PhET quantum simulations and their development. We also describe research demonstrating their effectiveness and share some insights about student thinking that we have gained from our research on quantum simulations.Comment: accepted by American Journal of Physics; v2 includes an additional study, more explanation of research behind claims, clearer wording, and more reference

What's important when caring for a loved one? Population-based preference weights for the Adult Social Care Outcomes Toolkit for informal carers (ASCOT-Carer) for Austria.

PURPOSE: The Adult Social Care Outcomes Toolkit for informal carers (ASCOT-Carer) can be used to assess long-term care-related quality of life (LTC-QoL) of adult informal carers of persons using LTC services. The ASCOT-Carer instrument has been translated into several languages, but preference weights reflecting the relative importance of different outcome states are only available for England so far. In this paper, we estimated preference weights for the German version of the ASCOT-Carer for Austria and investigated the value people place on different QoL-outcome states. METHODS: We used data from a best-worst scaling (BWS) experiment and estimated a scale-adjusted multinomial logit (S-MNL) model to elicit preference weights for the ASCOT-Carer domain-levels. Data were collected using an online survey of the Austrian general population (n = 1001). RESULTS: Top levels in the domains of 'Space and time to be yourself', 'Occupation' and 'Control over daily life' were perceived as providing the highest utility, and states with high needs in the same domains seen as particularly undesirable. 'Personal safety' was the only domain where levels were roughly equidistant. In all other domains, the difference between the top two levels ('ideal state' and 'no needs') was very small. CONCLUSION: The paper provides preference weights for the German version of ASCOT-Carer to be used in Austrian populations. Furthermore, the results give insight into which LTC-QoL-outcomes are seen as particularly (un)desirable, and may therefore help to better tailor services directed at informal carers and the persons they care for

Minimum Decision Cost for Quantum Ensembles

For a given ensemble of $N$ independent and identically prepared particles, we calculate the binary decision costs of different strategies for measurement of polarised spin 1/2 particles. The result proves that, for any given values of the prior probabilities and any number of constituent particles, the cost for a combined measurement is always less than or equal to that for any combination of separate measurements upon sub-ensembles. The Bayes cost, which is that associated with the optimal strategy (i.e., a combined measurement) is obtained in a simple closed form.Comment: 11 pages, uses RevTe

Use of near infrared reflectance spectroscopy to predict nitrogen uptake by winter wheat within fields with high variability in organic matter

In this study, the ability to predict N-uptake in winter wheat crops using NIR-spectroscopy on soil samples was evaluated. Soil samples were taken in unfertilized plots in one winter wheat field during three years (1997-1999) and in another winter wheat field nearby in one year (2000). Soil samples were analyzed for organic C content and their NIR-spectra. N-uptake was measured as total N-content in aboveground plant materials at harvest. Models calibrated to predict N-uptake were internally cross validated and validated across years and across fields. Cross-validated calibrations predicted N-uptake with an average error of 12.1 to 15.4 kg N ha-1. The standard deviation divided by this error (RPD) ranged between 1.9 and 2.5. In comparison, the corresponding calibrations based on organic C alone had an error from 11.7 to 28.2 kg N ha-1 and RPDs from 1.3 to 2.5. In three of four annual calibrations within a field, the NIR-based calibrations worked better than the organic C based calibrations. The prediction of N-uptake across years, but within a field, worked slightly better with an organic C based calibration than with a NIR based one, RPD = 1.9 and 1.7 respectively. Across fields, the corresponding difference was large in favour of the NIR-calibration, RPD = 2.5 for the NIR-calibration and 1.5 for the organic C calibration. It was concluded that NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore has a good potential to predict complex functions of soils such as N-mineralization. A relatively good agreement of spectral relationships to parameters related to the N-mineralization of datasets across the world suggests that more general models can be calibrated

Do You Prefer Safety to Social Participation? Finnish Population-Based Preference Weights for the Adult Social Care Outcomes Toolkit (ASCOT) for Service Users

Introduction. The Adult Social Care Outcomes Toolkit (ASCOT) was developed in England to measure people’s social care–related quality of life (SCRQoL). Objectives. The aim of this article is to estimate preference weights for the Finnish ASCOT for service users (ASCOT). In addition, we tested for learning and fatigue effects in the choice experiment used to elicit the preference weights. Methods. The analysis data (n = 1000 individuals) were obtained from an online survey sample of the Finnish adult general population using gender, age, and region as quotas. The questionnaire included a best-worst scaling (BWS) experiment using ASCOT. Each respondent sequentially selected four alternatives (best, worst; second-best, second-worst) for eight BWS tasks (n = 32,000 choice observations). A scale multinomial logit model was used to estimate the preference parameters and to test for fatigue and learning. Results. The most and least preferred attribute-levels were “I have as much control over my daily life as I want” and “I have no control over my daily life.” The preference weights were not on a cardinal scale. The ordering effect was related to the second-best choices. Learning effect was in the last four tasks. Conclusions. This study has developed a set of preference weights for the ASCOT instrument in Finland, which can be used for investigating outcomes of social care interventions on adult populations. The learning effect calls for the development of study designs that reduce possible bias relating to preference uncertainty at the beginning of sequential BWS tasks. It also supports the adaptation of a modelling strategy in which the sequence of tasks is explicitly modelled as a scale factor

Valuing informal carers' quality of life using best-worst scaling-Finnish preference weights for the Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer)

This study developed Finnish preference weights for the seven-attribute Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer) and investigated survey fatigue and learning in best-worst scaling (BWS) experiments. An online survey that included a BWS experiment using the ASCOT-Carer was completed by a sample from the general population in Finland. A block of eight BWS profiles describing different states from the ASCOT-Carer were randomly assigned to each respondent, who consecutively made four choices (best, worst, second best and second worst) per profile. The analysis panel data had 32,160 choices made by 1005 respondents. A scale multinomial logit (S-MNL) model was used to estimate preference weights for 28 ASCOT-Carer attribute levels. Fatigue and learning effects were examined as scale heterogeneity. Several specifications of the generalised MNL model were employed to ensure the stability of the preference estimates. The most and least-valued states were the top and bottom levels of the control over daily life attribute. The preference weights were not on a cardinal scale. We observed the position effect of the attributes on preferences associated with the best or second-best choices. A learning effect was found. The established preference weights can be used in evaluations of the effects of long-term care services and interventions on the quality of life of service users and caregivers. The learning effect implies a need to develop study designs that ensure equal consideration to all profiles (choice tasks) in a sequential choice experiment

Distinct Effects on Diversifying Selection by Two Mechanisms of Immunity Against Streptococcus pneumoniae

Antigenic variation to evade host immunity has long been assumed to be a driving force of diversifying selection in pathogens. Colonization by Streptococcus pneumoniae, which is central to the organism's transmission and therefore evolution, is limited by two arms of the immune system: antibody- and T cell- mediated immunity. In particular, the effector activity of CD4+ TH17 cell mediated immunity has been shown to act in trans, clearing co-colonizing pneumococci that do not bear the relevant antigen. It is thus unclear whether TH17 cell immunity allows benefit of antigenic variation and contributes to diversifying selection. Here we show that antigen-specific CD4+ TH17 cell immunity almost equally reduces colonization by both an antigen-positive strain and a co-colonized, antigen-negative strain in a mouse model of pneumococcal carriage, thus potentially minimizing the advantage of escape from this type of immunity. Using a proteomic screening approach, we identified a list of candidate human CD4+ TH17 cell antigens. Using this list and a previously published list of pneumococcal Antibody antigens, we bioinformatically assessed the signals of diversifying selection among the identified antigens compared to non-antigens. We found that Antibody antigen genes were significantly more likely to be under diversifying selection than the TH17 cell antigen genes, which were indistinguishable from non-antigens. Within the Antibody antigens, epitopes recognized by human antibodies showed stronger evidence of diversifying selection. Taken together, the data suggest that TH17 cell-mediated immunity, one form of T cell immunity that is important to limit carriage of antigen-positive pneumococcus, favors little diversifying selection in the targeted antigen. The results could provide new insight into pneumococcal vaccine design