Rarely selected distractors in high stakes medical multiple-choice examinations and their recognition by item authors: A simulation and survey

Background Many medical exams use 5 options for multiple choice questions (MCQs), although the literature suggests that 3 options are optimal. Previous studies on this topic have often been based on non-medical examinations, so we sought to analyse rarely selected, 'non-functional' distractors (NF-D) in high stakes medical examinations, and their detection by item authors as well as psychometric changes resulting from a reduction in the number of options. Methods Based on Swiss Federal MCQ examinations from 2005-2007, the frequency of NF-D (selected by <1% or <5% of the candidates) was calculated. Distractors that were chosen the least or second least were identified and candidates who chose them were allocated to the remaining options using two extreme assumptions about their hypothetical behaviour: In case rarely selected distractors were eliminated, candidates could randomly choose another option - or purposively choose the correct answer, from which they had originally been distracted. In a second step, 37 experts were asked to mark the least plausible options. The consequences of a reduction from 4 to 3 or 2 distractors - based on item statistics or on the experts' ratings - with respect to difficulty, discrimination and reliability were modelled. Results About 70% of the 5-option-items had at least 1 NF-D selected by <1% of the candidates (97% for NF-Ds selected by <5%). Only a reduction to 2 distractors and assuming that candidates would switch to the correct answer in the absence of a 'non-functional' distractor led to relevant differences in reliability and difficulty (and to a lesser degree discrimination). The experts' ratings resulted in slightly greater changes compared to the statistical approach. Conclusions Based on item statistics and/or an expert panel's recommendation, the choice of a varying number of 3-4 (or partly 2) plausible distractors could be performed without marked deteriorations in psychometric characteristics

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging

Inference of action potentials (‘spikes’) from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals (‘ground truth’). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation

Das Kopernikus-Projekt ENavi - Die Transformation des Stromsystems mit Fokus Kohleausstieg

In diesem Bericht wird die Transformation des Stromsystems als zentrale Stellschraube zur Erreichung der Klimaziele analysiert. Dabei wird die Dekarbonisierung, insbesondere der Ausstieg aus der Kohleverstromung, in den Fokus gerückt. Anhand einer systematischen Vorgehensweise werden Transformationsszenarien für das deutsche Energiesystem identifiziert, analysiert und bewertet. Die Analyse erfolgt mithilfe unterschiedlicher computergestützter Modelle, um die Auswirkungen im gesamten System abschätzen zu können. Es werden sowohl Wechselwirkungen im Stromsystem und im Energiesystem, als auch im Wirtschaftssystem und im Bereich Ressourcen und Umwelt untersucht

Fine-Tuning Cardiac Insulin-Like Growth Factor 1 Receptor Signaling to Promote Health and Longevity

Background: The insulin-like growth factor 1 (IGF1) pathway is a key regulator of cellular metabolism and aging. Although its inhibition promotes longevity across species, the effect of attenuated IGF1 signaling on cardiac aging remains controversial. Methods: We performed a lifelong study to assess cardiac health and lifespan in 2 cardiomyocyte-specific transgenic mouse models with enhanced versus reduced IGF1 receptor (IGF1R) signaling. Male mice with human IGF1R overexpression or dominant negative phosphoinositide 3-kinase mutation were examined at different life stages by echocardiography, invasive hemodynamics, and treadmill coupled to indirect calorimetry. In vitro assays included cardiac histology, mitochondrial respiration, ATP synthesis, autophagic flux, and targeted metabolome profiling, and immunoblots of key IGF1R downstream targets in mouse and human explanted failing and nonfailing hearts, as well. Results: Young mice with increased IGF1R signaling exhibited superior cardiac function that progressively declined with aging in an accelerated fashion compared with wild-type animals, resulting in heart failure and a reduced lifespan. In contrast, mice with low cardiac IGF1R signaling exhibited inferior cardiac function early in life, but superior cardiac performance during aging, and increased maximum lifespan, as well. Mechanistically, the late-life detrimental effects of IGF1R activation correlated with suppressed autophagic flux and impaired oxidative phosphorylation in the heart. Low IGF1R activity consistently improved myocardial bioenergetics and function of the aging heart in an autophagy-dependent manner. In humans, failing hearts, but not those with compensated hypertrophy, displayed exaggerated IGF1R expression and signaling activity. Conclusions: Our findings indicate that the relationship between IGF1R signaling and cardiac health is not linear, but rather biphasic. Hence, pharmacological inhibitors of the IGF1 pathway, albeit unsuitable for young individuals, might be worth considering in older adults

Stratifying patients with peripheral neuropathic pain based on sensory profiles : algorithm and sample size recommendations

In a recent cluster analysis, it has been shown that patients with peripheral neuropathic pain can be grouped into 3 sensory phenotypes based on quantitative sensory testing profiles, which are mainly characterized by either sensory loss, intact sensory function and mild thermal hyperalgesia and/or allodynia, or loss of thermal detection and mild mechanical hyperalgesia and/or allodynia. Here, we present an algorithm for allocation of individual patients to these subgroups. The algorithm is nondeterministic-ie, a patient can be sorted to more than one phenotype-and can separate patients with neuropathic pain from healthy subjects (sensitivity: 78%, specificity: 94%). We evaluated the frequency of each phenotype in a population of patients with painful diabetic polyneuropathy (n = 151), painful peripheral nerve injury (n = 335), and postherpetic neuralgia (n = 97) and propose sample sizes of study populations that need to be screened to reach a subpopulation large enough to conduct a phenotype-stratified study. The most common phenotype in diabetic polyneuropathy was sensory loss (83%), followed by mechanical hyperalgesia (75%) and thermal hyperalgesia (34%, note that percentages are overlapping and not additive). In peripheral nerve injury, frequencies were 37%, 59%, and 50%, and in postherpetic neuralgia, frequencies were 31%, 63%, and 46%. For parallel study design, either the estimated effect size of the treatment needs to be high (> 0.7) or only phenotypes that are frequent in the clinical entity under study can realistically be performed. For crossover design, populations under 200 patients screened are sufficient for all phenotypes and clinical entities with a minimum estimated treatment effect size of 0.5.Peer reviewe

GiSAO.db: a database for ageing research

<p>Abstract</p> <p>Background</p> <p>Age-related gene expression patterns of <it>Homo sapiens </it>as well as of model organisms such as <it>Mus musculus</it>, <it>Saccharomyces cerevisiae</it>, <it>Caenorhabditis elegans </it>and <it>Drosophila melanogaster </it>are a basis for understanding the genetic mechanisms of ageing. For an effective analysis and interpretation of expression profiles it is necessary to store and manage huge amounts of data in an organized way, so that these data can be accessed and processed easily.</p> <p>Description</p> <p>GiSAO.db (Genes involved in senescence, apoptosis and oxidative stress database) is a web-based database system for storing and retrieving ageing-related experimental data. Expression data of genes and miRNAs, annotation data like gene identifiers and GO terms, orthologs data and data of follow-up experiments are stored in the database. A user-friendly web application provides access to the stored data. KEGG pathways were incorporated and links to external databases augment the information in GiSAO.db. Search functions facilitate retrieval of data which can also be exported for further processing.</p> <p>Conclusions</p> <p>We have developed a centralized database that is very well suited for the management of data for ageing research. The database can be accessed at <url>https://gisao.genome.tugraz.at</url> and all the stored data can be viewed with a guest account.</p