A pedagogic appraisal of the Priority Heuristic

We have explored how science and mathematics teachers made decisions when confronted with a dilemma in which a fictitious young woman, Deborah, may choose to have an operation that might address a painful spinal condition. We sought to explore the extent to which psychological heuristic models, in particular the Priority Heuristic, might successfully describe the decision-making process of these teachers and how an analysis of the role of personal and emotional factors in shaping the decision-making process might inform pedagogical design. A novel aspect of this study is that the setting in which the decision-making process is examined contrasts sharply with those used in psychological experiments. We found that to some extent, even in this contrasting setting, the Priority Heuristic could describe these teachers' decision-making. Further analysis of the transcripts yielded some insights into limitations on scope as well the richness and complexity in how personal factors were brought to bear. We see these limitations as design opportunities for educational intervention

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

BACKGROUND. Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS. We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION. The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.Italian Ministry of University and Scientific Research; National Institutes of Health & National Human Genome Research Institute (HG003354-01A2); Collegio Ghislieri, Pavia Italy fellowshi

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome

Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives

Arterial oxygen content is precisely maintained by graded erythrocytotic responses in settings of high/normal serum iron levels, and predicts exercise capacity: an observational study of hypoxaemic patients with pulmonary arteriovenous malformations.

Oxygen, haemoglobin and cardiac output are integrated components of oxygen transport: each gram of haemoglobin transports 1.34 mls of oxygen in the blood. Low arterial partial pressure of oxygen (PaO2), and haemoglobin saturation (SaO2), are the indices used in clinical assessments, and usually result from low inspired oxygen concentrations, or alveolar/airways disease. Our objective was to examine low blood oxygen/haemoglobin relationships in chronically compensated states without concurrent hypoxic pulmonary vasoreactivity.165 consecutive unselected patients with pulmonary arteriovenous malformations were studied, in 98 cases, pre/post embolisation treatment. 159 (96%) had hereditary haemorrhagic telangiectasia. Arterial oxygen content was calculated by SaO2 x haemoglobin x 1.34/100.There was wide variation in SaO2 on air (78.5-99, median 95)% but due to secondary erythrocytosis and resultant polycythaemia, SaO2 explained only 0.1% of the variance in arterial oxygen content per unit blood volume. Secondary erythrocytosis was achievable with low iron stores, but only if serum iron was high-normal: Low serum iron levels were associated with reduced haemoglobin per erythrocyte, and overall arterial oxygen content was lower in iron deficient patients (median 16.0 [IQR 14.9, 17.4]mls/dL compared to 18.8 [IQR 17.4, 20.1]mls/dL, p<0.0001). Exercise tolerance appeared unrelated to SaO2 but was significantly worse in patients with lower oxygen content (p<0.0001). A pre-defined athletic group had higher Hb:SaO2 and serum iron:ferritin ratios than non-athletes with normal exercise capacity. PAVM embolisation increased SaO2, but arterial oxygen content was precisely restored by a subsequent fall in haemoglobin: 86 (87.8%) patients reported no change in exercise tolerance at post-embolisation follow-up.Haemoglobin and oxygen measurements in isolation do not indicate the more physiologically relevant oxygen content per unit blood volume. This can be maintained for SaO2 ≥78.5%, and resets to the same arterial oxygen content after correction of hypoxaemia. Serum iron concentrations, not ferritin, seem to predict more successful polycythaemic responses

New mutations at the imprinted Gnas cluster show gene dosage effects of Gsα in postnatal growth and implicate XLαs in bone and fat metabolism, but not in suckling

The imprinted Gnas cluster is involved in obesity, energy metabolism, feeding behavior, and viability. Relative contribution of paternally expressed proteins XLαs, XLN1, and ALEX or a double dose of maternally expressed Gsα to phenotype has not been established. In this study, we have generated two new mutants (Ex1A-T-CON and Ex1A-T) at the Gnas cluster. Paternal inheritance of Ex1A-T-CON leads to loss of imprinting of Gsα, resulting in preweaning growth retardation followed by catch-up growth. Paternal inheritance of Ex1A-T leads to loss of imprinting of Gsα and loss of expression of XLαs and XLN1. These mice have severe preweaning growth retardation and incomplete catch-up growth. They are fully viable probably because suckling is unimpaired, unlike mutants in which the expression of all the known paternally expressed Gnasxl proteins (XLαs, XLN1 and ALEX) is compromised. We suggest that loss of ALEX is most likely responsible for the suckling defects previously observed. In adults, paternal inheritance of Ex1A-T results in an increased metabolic rate and reductions in fat mass, leptin, and bone mineral density attributable to loss of XLαs. This is, to our knowledge, the first report describing a role for XLαs in bone metabolism. We propose that XLαs is involved in the regulation of bone and adipocyte metabolism

Radiosurgery for pituitary adenomas: evaluation of its efficacy and safety

<p>Abstract</p> <p>Object</p> <p>To assess the effects of radiosurgery (RS) on the radiological and hormonal control and its toxicity in the treatment of pituitary adenomas.</p> <p>Methods</p> <p>Retrospective analysis of 42 patients out of the first 48 consecutive patients with pituitary adenomas treated with RS between 1999 and 2008 with a 6 months minimum follow-up. RS was delivered with Gamma Knife as a primary or adjuvant treatment. There were 14 patients with non-secretory adenomas and, among functioning adenomas, 9 were prolactinomas, 9 were adrenocorticotropic hormone-secreting and 10 were growth hormone-secreting tumors. Hormonal control was defined as hormonal response (decline of more than 50% from the pre-RS levels) and hormonal normalization. Radiological control was defined as stasis or shrinkage of the tumor. Hypopituitarism and visual deficit were the morbidity outcomes. Hypopituitarism was defined as the initiation of any hormone replacement therapy and visual deficit as loss of visual acuity or visual field after RS.</p> <p>Results</p> <p>The median follow-up was 42 months (6-109 months). The median dose was 12,5 Gy (9 - 15 Gy) and 20 Gy (12 - 28 Gy) for non-secretory and secretory adenomas, respectively. Tumor growth was controlled in 98% (41 in 42) of the cases and tumor shrinkage ocurred in 10% (4 in 42) of the cases. The 3-year actuarial rate of hormonal control and normalization were 62,4% and 37,6%, respectively, and the 5-year actuarial rate were 81,2% and 55,4%, respectively. The median latency period for hormonal control and normalization was, respectively, 15 and 18 months. On univariate analysis, there were no relationships between median dose or tumoral volume and hormonal control or normalization. There were no patients with visual deficit and 1 patient had hypopituitarism after RS.</p> <p>Conclusions</p> <p>RS is an effective and safe therapeutic option in the management of selected patients with pituitary adenomas. The short latency of the radiation response, the highly acceptable radiological and hormonal control and absence of complications at this early follow-up are consistent with literature.</p

ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets

Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions

Psychological treatment of depression: A meta-analytic database of randomized studies

Abstract Background A large number of randomized controlled studies have clearly demonstrated that psychological interventions are effective in the treatment of depression. The number of studies in this area is increasing rapidly. In this paper, we present a database of controlled and comparative outcome studies on psychological treatments of depression, based on a series of meta-analyses published by our group. The database can be accessed freely through the Internet. Description We conducted a comprehensive literature search of the major bibliographical databases (Pubmed; Psycinfo; Embase; Cochrane Central Register of Controlled Trials) and we examined the references of 22 earlier meta-analyses of psychological treatment of depression. We included randomized studies in which the effects of a psychological therapy on adults with depression were compared to a control condition, another psychological intervention, or a combined treatment (psychological plus pharmacological). We conducted nine meta-analyses of subgroups of studies taken from this dataset. The 149 studies included in these 9 meta-analyses are included in the current database. In the 149 included studies, a total of 11,369 patients participated. In the database, we present selected characteristics of each study, including characteristics of the patients (the study population, recruitment method, definition of depression); characteristics of the experimental conditions and interventions (the experimental conditions, N per condition, format, number of sessions); and study characteristics (measurement times, measures used, attrition, type of analysis and country). Conclusion The data on the 149 included studies are presented in order to give other researchers access to the studies we collected, and to give background information about the meta-analyses we have published using this dataset. The number of studies examining the effects of psychological treatments of depression has increased considerably in the past decades, and this will continue in the future. The database we have presented in this paper can help to integrate the results of these studies in future meta-analyses and systematic reviews on psychological treatments for depression

The relationship between risk factors for falling and the quality of life in older adults

BACKGROUND: Falls are one of the major health problems that effect the quality of life among older adults. The aim of this study was to explore the relationship between quality of life (Short Form-12) and the risk factors of falls (balance, functional mobility, proprioception, muscle strength, flexibility and fear of falling) in older adults. METHODS: One hundred sixteen people aged 65 or older and living in the T.C. Emekli Sandigi Narlidere nursing home participated in the study. Balance (Berg Balance test), functional mobility (Timed Up and Go), proprioception (joint position sense), muscle strength (back/leg dynamometer), flexibility (sit and reach) and fear of falling (Visual Analogue Scale) were assessed as risk factors for falls. The quality of life was measured by Short Form-12 (SF-12). RESULTS: A strong positive correlation was observed between Physical Health Component Summary of SF-12, General Health Perception and balance, muscle strength. Proprioception and flexibility did not correlated with SF-12 (p > 0.05). There was negative correlation between Physical Health Component Summary of SF-12, General Health Perception and fear of falling, functional mobility (p < 0.05). CONCLUSION: We concluded that the risk factors for falls (balance, functional mobility, muscle strength, fear of falling) in older adults are associated with quality of life while flexibility and proprioception are not