On the center of mass of Ising vectors

We show that the center of mass of Ising vectors that obey some simple constraints, is again an Ising vector.Comment: 8 pages, 3 figures, LaTeX; Claims in connection with disordered systems have been withdrawn; More detailed description of the simulations; Inset added to figure

Mutation Testing as a Safety Net for Test Code Refactoring

Refactoring is an activity that improves the internal structure of the code without altering its external behavior. When performed on the production code, the tests can be used to verify that the external behavior of the production code is preserved. However, when the refactoring is performed on test code, there is no safety net that assures that the external behavior of the test code is preserved. In this paper, we propose to adopt mutation testing as a means to verify if the behavior of the test code is preserved after refactoring. Moreover, we also show how this approach can be used to identify the part of the test code which is improperly refactored

Considering Polymorphism in Change-Based Test Suite Reduction

With the increasing popularity of continuous integration, algorithms for selecting the minimal test-suite to cover a given set of changes are in order. This paper reports on how polymorphism can handle false negatives in a previous algorithm which uses method-level changes in the base-code to deduce which tests need to be rerun. We compare the approach with and without polymorphism on two distinct cases ---PMD and CruiseControl--- and discovered an interesting trade-off: incorporating polymorphism results in more relevant tests to be included in the test suite (hence improves accuracy), however comes at the cost of a larger test suite (hence increases the time to run the minimal test-suite).Comment: The final publication is available at link.springer.co

An experimental model to measure the ability of headphones with active noise control to reduce patient's exposure to noise in an intensive care unit.

BACKGROUND: Defining the association between excessive noise in intensive care units, sleep disturbance and morbidity, including delirium, is confounded by the difficulty of implementing successful strategies to reduce patient's exposure to noise. Active noise control devices may prove to be useful adjuncts but there is currently little to quantify their ability to reduce noise in this complex environment. METHODS: Sound meters were embedded in the auditory meatus of three polystyrene model heads with no headphones (control), with headphones alone and with headphones using active noise control and placed in patient bays in a cardiac ICU. Ten days of recording sound levels at a frequency of 1 Hz were performed, and the noise levels in each group were compared using repeated measures MANOVA and subsequent pairwise testing. RESULTS: Multivariate testing demonstrated that there is a significant difference in the mean noise exposure levels between the three groups (p < 0.001). Subsequent pairwise testing between the three groups shows that the reduction in noise is greatest with headphones and active noise control. The mean reduction in noise exposure between the control and this group over 24 h is 6.8 (0.66) dB. The use of active noise control was also associated with a reduction in the exposure to high-intensity sound events over the course of the day. CONCLUSIONS: The use of active noise cancellation, as delivered by noise-cancelling headphones, is associated with a significant reduction in noise exposure in our model of noise exposure in a cardiac ICU. This is the first study to look at the potential effectiveness of active noise control in adult patients in an intensive care environment and shows that active noise control is a candidate technology to reduce noise exposure levels the patients experience during stays on intensive care

Peroxisome Proliferator-Activated Receptor gamma enhances the activity of a insulin degrading enzyme-like metalloprotease for amyloid-beta clearance.

Peroxisome proliferator-activated receptor gamma (PPARgamma) activation results in an increased rate of amyloid-beta (Abeta) clearance from the media of diverse cells in culture, including primary neurons and glial cells. Here, we further investigate the mechanism for Abeta clearance and found that PPARgamma activation modulates a cell surface metalloprotease that can be inhibited by metalloprotease inhibitors, like EDTA and phenanthroline, and also by the peptide hormones insulin and glucagon. The metalloprotease profile of the Abeta-degrading mechanism is surprisingly similar to insulin-degrading enzyme (IDE). This mechanism is maintained in hippocampal and glia primary cultures from IDE loss-of-function mice. We conclude that PPARgamma activates an IDE-like Abeta degrading activity. Our work suggests a drugable pathway that can clear Abeta peptide from the brain

Diagnostic accuracy and usability of the EMBalance decision support system for vestibular disorders in primary care: proof of concept randomised controlled study results

BACKGROUND: Dizziness and imbalance are common symptoms that are often inadequately diagnosed or managed, due to a lack of dedicated specialists. Decision Support Systems (DSS) may support first-line physicians to diagnose and manage these patients based on personalised data. AIM: To examine the diagnostic accuracy and application of the EMBalance DSS for diagnosis and management of common vestibular disorders in primary care. METHODS: Patients with persistent dizziness were recruited from primary care in Germany, Greece, Belgium and the UK and randomised to primary care clinicians assessing the patients with (+ DSS) versus assessment without (- DSS) the EMBalance DSS. Subsequently, specialists in neuro-otology/audiovestibular medicine performed clinical evaluation of each patient in a blinded way to provide the "gold standard" against which the + DSS, - DSS and the DSS as a standalone tool (i.e. without the final decision made by the clinician) were validated. RESULTS: One hundred ninety-four participants (age range 25-85, mean = 57.7, SD = 16.7 years) were assigned to the + DSS (N = 100) and to the - DSS group (N = 94). The diagnosis suggested by the + DSS primary care physician agreed with the expert diagnosis in 54%, compared to 41.5% of cases in the - DSS group (odds ratio 1.35). Similar positive trends were observed for management and further referral in the + DSS vs. the - DSS group. The standalone DSS had better diagnostic and management accuracy than the + DSS group. CONCLUSION: There were trends for improved vestibular diagnosis and management when using the EMBalance DSS. The tool requires further development to improve its diagnostic accuracy, but holds promise for timely and effective diagnosis and management of dizzy patients in primary care. TRIAL REGISTRATION NUMBER: NCT02704819 (clinicaltrials.gov)

Multinational development and validation of an early prediction model for delirium in ICU patients

Rationale Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. Purpose To develop and validate a model based on data available at ICU admission to predict delirium development during a patient’s complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development. Methods Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. Results In total, 2914 patients were included. Delirium incidence was 23.6 %. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95 % confidence interval (CI) 0.73–0.77] in the development dataset and 0.75 (95 % CI 0.71–0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95 % CI 0.67–0.74), for delirium that developed 6 days. Conclusion Patients’ delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium