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An analysis of teaching practices highlighting the central role of modelling when combining a Socially Vital Issue (SVI), “Chocolate”, (Simonneaux & al., 2017) and the development of complex thinking skills. This article reports how two kindergarten teachers applied the modelling process through an interdisciplinary investigative approach (Roy and Gremaud, 2017) to capture the complexity (Hertig, 2018) of a commonplace (concrete world) subject, “chocolate”, by transforming it into an interdisciplinary investigative subject (conceptual world). The chocolate industry, a subject of study at times controversial due to its modes of consumption and its production chain, was approached in the classroom with the objective to form future reflective and critical thinking citizens. The creation of an Interdisciplinary community of Practice and Discourse (Roy, Gremaud and Jenni, in press) was present to support the two educators. It focused upon two didactic concepts: the interdisciplinary investigative approach and the modelling of complex thinking.

Cet article consiste en une réflexion conceptuelle d’ordre épistémologique et didactique sur un enseignement de la technologie centré sur le processus de conception technique. En vue de construire une communauté discursive interdisciplinaire scolaire dans ce domaine, nous convions les enseignant·e·s à faire acquérir aux élèves six pratiques épistémiques d’ingénierie : définir un problème technique, générer et sélectionner des solutions en recourant à une approche systématique et itérative, analyser et modéliser, expérimenter et évaluer, communiquer et prendre des décisions. Ces pratiques discursives interdisciplinaires caractérisant la pensée design des élèves sont cohérentes avec les pratiques sociotechniques de référence et jouent un rôle central dans le développement d’une culture technique authentique.

It is increasingly clear that nicotinic acetylcholine receptors (nAChRs) are involved in immune regulation, and that their activation can protect against inflammatory diseases. Previous data have shown that nicotine diminishes the numbers of peripheral monocytes and macrophages, especially those of the pro-inflammatory phenotype. The goal of the present study was to determine if nicotine modulates the production of bone marrow -derived monocytes/macrophages. In this study, we first found that murine bone marrow cells express multiple nAChR subunits, and that the α7 and α9 nAChRs most predominant subtypes found in immune cells and their precursors. Using primary cultures of murine bone marrow cells, we then determined the effect of nicotine on monocyte colony-stimulating factor and interferon gamma (IFNγ)-induced monocyte production. We found that nicotine lowered the overall number of monocytes, and more specifically, inhibited the IFNγ-induced increase in pro-inflammatory monocytes by reducing cell proliferation and viability. These data suggested that nicotine diminishes the ratio of pro-inflammatory versus anti-inflammatory monocyte produced in the bone marrow. We thus confirmed this hypothesis by measuring cytokine expression, where we found that nicotine inhibited the production of the pro-inflammatory cytokines TNFα, IL-1β and IL-12, while stimulating the secretion of IL-10, an anti-inflammatory cytokine. Finally, nicotine also reduced the number of pro-inflammatory monocytes in the bone marrow of LPS-challenged mice. Overall, our data demonstrate that both α7 and α9 nAChRs are involved in the regulation of pro-inflammatory M1 monocyte numbers.

ObjectiveTo determine if a single brain biopsy utilizing a freeze-core needle harvest system Cassi II under ultrasound guidance provides a diagnostic sample; to evaluate the technique's efficacy in procuring diagnostic samples in comparison with “open” surgical biopsies; and to describe intraoperative complications associated with the technique.Study designExperimental clinical study.AnimalsSeventeen dogs and four cats with magnetic resonance imaging (MRI) diagnoses of readily surgically accessible intracranial masses.MethodsImmediately prior to surgical biopsy (SB), freeze-core biopsy (FCB) sample was obtained from each patient under ultrasound guidance.ResultsHistopathology results from single FCB samples were found to be in 100% agreement with the SB samples. Freezing artifact was minimal and did not interfere with histopathologic interpretation. There were no intraoperative complications specifically attributable to the use of the FCB system.ConclusionBased on the results of this small experimental study, the FCB system is expected to safely yield diagnostic quality intracranial masses biopsy specimens.Clinical significanceThis system has the potential of obtaining diagnostic biopsies of more deeply seated brain lesions (i.e., intra-axial tumors considered inaccessible or with large risks/difficulties by standard surgical means) which would provide a definitive diagnosis to guide appropriate therapy.

The extent of the damage to Puerto Rico from Hurricane Maria in September 2017 led to outages in electricity service that persisted for months. Power system operators attempting to restore critical facilities faced challenges on almost every front, from supply chain interruptions to the inaccessibility of key assets. After a disaster of this magnitude, it is critical, but challenging, to prioritize how limited resources are directed toward rebuilding and fortifying the electric power system. To inform these decisions, the U.S. Department of Energy funded efforts investigating methodologies to identify critical vulnerabilities to the Puerto Rican power system, and to provide data-driven recommendations on how to harden and operate the system for greater resilience. This work presents the Risk-based Contingency Analysis Tool (RCAT), a framework developed as a part of that resilience initiative. The framework can qualitatively and quantitatively describe the most critical system vulnerabilities with an understanding of both likelihood of occurrence and impact. It evaluates the effectiveness of candidate remediation strategies in reducing overall risk to the system from future hurricane events. This paper will describe RCAT, with an emphasis on how different modeling capabilities have been integrated along with probabilistic methods and analytical metrics to better describe risk.

Abstract Background In clinical trials, there is considerable interest in investigating whether a treatment effect is similar in all patients, or that one or more prognostic variables indicate a differential response to treatment. To examine this, a continuous predictor is usually categorised into groups according to one or more cutpoints. Several weaknesses of categorization are well known. To avoid the disadvantages of cutpoints and to retain full information, it is preferable to keep continuous variables continuous in the analysis. To handle this issue, the Subpopulation Treatment Effect Pattern Plot (STEPP) was proposed about two decades ago, followed by the multivariable fractional polynomial interaction (MFPI) approach. Provided individual patient data (IPD) from several studies are available, it is possible to investigate for treatment heterogeneity with meta-analysis techniques. Meta-STEPP was recently proposed and in patients with primary breast cancer an interaction of estrogen receptors with chemotherapy was investigated in eight randomized controlled trials (RCTs). Methods We use data from eight randomized controlled trials in breast cancer to illustrate issues from two main tasks. The first task is to derive a treatment effect function (TEF), that is, a measure of the treatment effect on the continuous scale of the covariate in the individual studies. The second is to conduct a meta-analysis of the continuous TEFs from the eight studies by applying pointwise averaging to obtain a mean function. We denote the method metaTEF. To improve reporting of available data and all steps of the analysis we introduce a three-part profile called MethProf-MA. Results Although there are considerable differences between the studies (populations with large differences in prognosis, sample size, effective sample size, length of follow up, proportion of patients with very low estrogen receptor values) our results provide clear evidence of an interaction, irrespective of the choice of the FP function and random or fixed effect models. Conclusions In contrast to cutpoint-based analyses, metaTEF retains the full information from continuous covariates and avoids several critical issues when performing IPD meta-analyses of continuous effect modifiers in randomised trials. Early experience suggests it is a promising approach. Trial registration Not applicable.

Background Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

Mapping and quantifying hydrologic exchange flows (HEFs) is critical to environmental monitoring and remediation at contaminated sites; however, these objectives are challenging in highly dynamic systems, e.g., along dam-regulated rivers, where HEFs vary rapidly. Direct seepage measurements are labor-intensive and difficult to automate, whereas indirect (e.g., thermal) and remote sensing methods have potential to allow continuous monitoring with limited field effort. We present a preliminary assessment of a multi-scale temperature-based strategy for monitoring HEFs along the Hanford Reach of the Columbia River, in eastern WA, United States. Five thermal methods were assessed. First, a vertical temperature profile (VTP) was installed into the streambed. The VTP data were analyzed using a data assimilation algorithm designed for automated real-time estimation in dynamic systems. Second, a thermal infrared (TIR) camera was used in roving surveys to identify seeps. Third, a TIR camera was stationed at the VTP site to collect images at 1-h intervals. Together, the two TIR datasets provided a basis to assess the potential for drone-based TIR. Fourth, temperature was measured at the sediment/water interface to assess fiber-optic distributed temperature sensing. Fifth, imagery from the ECOSTRESS satellite mission was acquired to assess the potential of spaceborne thermal monitoring. Based on our preliminary assessment, VTP, TIR, and bed temperature measurements provide complementary spatial coverage, temporal sampling, and resolution; these methods have potential for long-term, automated monitoring of HEFs. The publicly available spaceborne imagery, however, proved inadequate because of insufficient spatial resolution and data gaps resulting from cloud cover and revisit frequency.

Abstract Background The logrank test is routinely applied to design and analyse randomized controlled trials (RCTs) with time-to-event outcomes. Sample size and power calculations assume the treatment effect follows proportional hazards (PH). If the PH assumption is false, power is reduced and interpretation of the hazard ratio (HR) as the estimated treatment effect is compromised. Using statistical simulation, we investigated the type 1 error and power of the logrank (LR)test and eight alternatives. We aimed to identify test(s) that improve power with three types of non-proportional hazards (non-PH): early, late or near-PH treatment effects. Methods We investigated weighted logrank tests (early, LRE; late, LRL), the supremum logrank test (SupLR) and composite tests (joint, J; combined, C; weighted combined, WC; versatile and modified versatile weighted logrank, VWLR, VWLR2) with two or more components. Weighted logrank tests are intended to be sensitive to particular non-PH patterns. Composite tests attempt to improve power across a wider range of non-PH patterns. Using extensive simulations based on real trials, we studied test size and power under PH and under simple departures from PH comprising pointwise constant HRs with a single change point at various follow-up times. We systematically investigated the influence of high or low control-arm event rates on power. Results With no preconceived type of treatment effect, the preferred test is VWLR2. Expecting an early effect, tests with acceptable power are SupLR, C, VWLR2, J, LRE and WC. Expecting a late effect, acceptable tests are LRL, VWLR, VWLR2, WC and J. Under near-PH, acceptable tests are LR, LRE, VWLR, C, VWLR2 and SupLR. Type 1 error was well controlled for all tests, showing only minor deviations from the nominal 5%. The location of the HR change point relative to the cumulative proportion of control-arm events considerably affected power. Conclusions Assuming ignorance of the likely treatment effect, the best choice is VWLR2. Several non-standard tests performed well when the correct type of treatment effect was assumed. A low control-arm event rate reduced the power of weighted logrank tests targeting early effects. Test size was generally well controlled. Further investigation of test characteristics with different types of non-proportional hazards of the treatment effect is warranted.

Abstract Background How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc ‘traditional’ approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics. Methods We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling. Results Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research. Conclusions Selection of variables and of functional forms are important topics in multivariable analysis. To define a state of the art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge, further comparative research is required.

Abstract Background The logrank test and the Cox proportional hazards model are routinely applied in the design and analysis of randomised controlled trials (RCTs) with time-to-event outcomes. Usually, sample size and power calculations assume proportional hazards (PH) of the treatment effect, i.e. the hazard ratio is constant over the entire follow-up period. If the PH assumption fails, the power of the logrank/Cox test may be reduced, sometimes severely. It is, therefore, important to understand how serious this can become in real trials, and for a proven, alternative test to be available to increase the robustness of the primary test. Methods We performed a systematic search to identify relevant articles in four leading medical journals that publish results of phase 3 clinical trials. Altogether, 50 articles satisfied our inclusion criteria. We digitised published Kaplan–Meier curves and created approximations to the original times to event or censoring at the individual patient level. Using the reconstructed data, we tested for non-PH in all 50 trials. We compared the results from the logrank/Cox test with those from the combined test recently proposed by Royston and Parmar. Results The PH assumption was checked and reported only in 28% of the studies. Evidence of non-PH at the 0.10 level was detected in 31% of comparisons. The Cox test of the treatment effect was significant at the 0.05 level in 49% of comparisons, and the combined test in 55%. In four of five trials with discordant results, the interpretation would have changed had the combined test been used. The degree of non-PH and the dominance of the p value for the combined test were strongly associated. Graphical investigation suggested that non-PH was mostly due to a treatment effect manifesting in an early follow-up and disappearing later. Conclusions The evidence for non-PH is checked (and, hence, identified) in only a small minority of RCTs, but non-PH may be present in a substantial fraction of such trials. In our reanalysis of the reconstructed data from 50 trials, the combined test outperformed the Cox test overall. The combined test is a promising approach to making trial design and analysis more robust.

Status epilepticus (SE) and cluster seizures (CS) are common occurrences in veterinary neurology and frequent reasons of admission to veterinary hospitals. With prolonged seizure activity, gamma amino-butyric acid (GABA) receptors (GABAa receptors) become inactive, leading to a state of pharmacoresistance to benzodiazepines and other GABAergic medications, which is called refractory status epilepticus (RSE). Prolonged seizure activity is also associated with overexpression of N-methyl-D-aspartic (NMDA) receptors. Rodent models have shown the efficacy of ketamine (KET) in treating RSE, and its use has been reported in one canine case of RSE. Boluses of KET 5 mg/kg IV have become the preferred treatment for RSE in our hospital. A retrospective study was performed to evaluate and report our experience with KET IV bolus to treat prolonged and/or repeated seizure activity in cases of canine CS, SE, and RSE. A total of 15 dogs were retrieved, for 20 hospitalizations and 28 KET IV injections over 3 years. KET IV boluses were used 12 times for RSE (9 generalized seizures, 3 focal seizures) and KET terminated the episode of RSE 12/12 times (100%); however, seizures recurred 4/12 times (33%) within ≤6 h of KET IV bolus. When used for CS apart from episodes of RSE, KET IV bolus was associated with termination of the CS episode only 4/14 times (29%). Only 4/28 (14%) KET IV boluses were associated with adverse effects imputable only to the use of KET. One dog experienced a short, self-limited seizure activity during administration of KET IV, which was most likely related to a pre-mature use of KET IV (i.e., before GABAergic resistance and NMDA receptor overexpression had taken place). This study indicates that KET 5 mg/kg IV bolus may be successful for the treatment of RSE in dogs.

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Abstract Background Most randomized controlled trials with a time-to-event outcome are designed assuming proportional hazards (PH) of the treatment effect. The sample size calculation is based on a logrank test. However, non-proportional hazards are increasingly common. At analysis, the estimated hazards ratio with a confidence interval is usually presented. The estimate is often obtained from a Cox PH model with treatment as a covariate. If non-proportional hazards are present, the logrank and equivalent Cox tests may lose power. To safeguard power, we previously suggested a ‘joint test’ combining the Cox test with a test of non-proportional hazards. Unfortunately, a larger sample size is needed to preserve power under PH. Here, we describe a novel test that unites the Cox test with a permutation test based on restricted mean survival time. Methods We propose a combined hypothesis test based on a permutation test of the difference in restricted mean survival time across time. The test involves the minimum of the Cox and permutation test P-values. We approximate its null distribution and correct it for correlation between the two P-values. Using extensive simulations, we assess the type 1 error and power of the combined test under several scenarios and compare with other tests. We investigate powering a trial using the combined test. Results The type 1 error of the combined test is close to nominal. Power under proportional hazards is slightly lower than for the Cox test. Enhanced power is available when the treatment difference shows an ‘early effect’, an initial separation of survival curves which diminishes over time. The power is reduced under a ‘late effect’, when little or no difference in survival curves is seen for an initial period and then a late separation occurs. We propose a method of powering a trial using the combined test. The ‘insurance premium’ offered by the combined test to safeguard power under non-PH represents about a single-digit percentage increase in sample size. Conclusions The combined test increases trial power under an early treatment effect and protects power under other scenarios. Use of restricted mean survival time facilitates testing and displaying a generalized treatment effect.

Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of the many options available with ice. We remark brie y on the new databasearchitecture and procedures for multiple imputation introduced in releases 11 and 12 of Stata.