User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages

User’s Guide to For2R: A Module of Fortran 95 Output Routines Compatible with the R Statistics Language

For2R is a collection of Fortran routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 For2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the For2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer For2R to others in the hope that they will find it useful. (PDF contains 31 pages

Data needs and spatial structure considerations in stock assessments with regional differences in recruitment and exploitation

This study uses a simulation experiment to demonstrate that bias in estimates of spawning biomass is influenced by the spatial configuration of a stock assessment model, whether survey data are used, and whether an environmental index is available to inform the spatial distribution of recruitment. Stocks with limited movement of post-settlement fish may be spatially structured due to environmental forces that affect larval dispersal and recruitment distribution or from non-uniform spatial exploitation. Data are frequently aggregated across space in stock assessments, thus disregarding this complex spatial structure and possibly introducing bias into estimates of stock status. An operating model (OM) is created which simulates data that are used in a set of estimation models to assess bias. The following experimental factors are considered: (a) using survey data and environmental indices in the assessment; (b) using disaggregated data (two regions, as generated by the OM) or aggregated data (one region); and (c) incorporating different patterns in the OMâ s regional exploitation and environmentally driven recruitment distribution.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Overexpression of Yeast Hsp110 Homolog Sse1p Suppresses ydj1-151 Thermosensitivity and Restores Hsp90-dependent Activity

The Saccharomyces cerevisiae heat-shock protein (Hsp)40, Ydj1p, is involved in a variety of cellular activities that control polypeptide fate, such as folding and translocation across intracellular membranes. To elucidate the mechanism of Ydj1p action, and to identify functional partners, we screened for multicopy suppressors of the temperature-sensitive ydj1-151 mutant and identified a yeast Hsp110, SSE1. Overexpression of Sse1p also suppressed the folding defect of v-Src kinase in the ydj1-151 mutant and partially reversed the α-factor translocation defect. SSE1-dependent suppression of ydj1-151 thermosensitivity required the wild-type ATP-binding domain of Sse1p. However, the Sse1p mutants maintained heat-denatured firefly luciferase in a folding-competent state in vitro and restored human androgen receptor folding in sse1 mutant cells. Because the folding of both v-Src kinase and human androgen receptor in yeast requires the Hsp90 complex, these data suggest that Ydj1p and Sse1p are interacting cochaperones in the Hsp90 complex and facilitate Hsp90-dependent activity

Evolution over Time of Ventilatory Management and Outcome of Patients with Neurologic Disease∗

OBJECTIVES: To describe the changes in ventilator management over time in patients with neurologic disease at ICU admission and to estimate factors associated with 28-day hospital mortality. DESIGN: Secondary analysis of three prospective, observational, multicenter studies. SETTING: Cohort studies conducted in 2004, 2010, and 2016. PATIENTS: Adult patients who received mechanical ventilation for more than 12 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among the 20,929 patients enrolled, we included 4,152 (20%) mechanically ventilated patients due to different neurologic diseases. Hemorrhagic stroke and brain trauma were the most common pathologies associated with the need for mechanical ventilation. Although volume-cycled ventilation remained the preferred ventilation mode, there was a significant (p < 0.001) increment in the use of pressure support ventilation. The proportion of patients receiving a protective lung ventilation strategy was increased over time: 47% in 2004, 63% in 2010, and 65% in 2016 (p < 0.001), as well as the duration of protective ventilation strategies: 406 days per 1,000 mechanical ventilation days in 2004, 523 days per 1,000 mechanical ventilation days in 2010, and 585 days per 1,000 mechanical ventilation days in 2016 (p < 0.001). There were no differences in the length of stay in the ICU, mortality in the ICU, and mortality in hospital from 2004 to 2016. Independent risk factors for 28-day mortality were age greater than 75 years, Simplified Acute Physiology Score II greater than 50, the occurrence of organ dysfunction within first 48 hours after brain injury, and specific neurologic diseases such as hemorrhagic stroke, ischemic stroke, and brain trauma. CONCLUSIONS: More lung-protective ventilatory strategies have been implemented over years in neurologic patients with no effect on pulmonary complications or on survival. We found several prognostic factors on mortality such as advanced age, the severity of the disease, organ dysfunctions, and the etiology of neurologic disease