OpenDF - A Dataflow Toolset for Reconfigurable Hardware and Multicore Systems

This paper presents the OpenDF framework and recalls that dataflow programming was once invented to address the problem of parallel computing. We discuss the problems with an imperative style, von Neumann programs, and present what we believe are the advantages of using a dataflow programming model. The CAL actor language is briefly presented and its role in the ISO/MPEG standard is discussed. The Dataflow Interchange Format (DIF) and related tools can be used for analysis of actors and networks, demonstrating the advantages of a dataflow approach. Finally, an overview of a case study implementing an MPEG-4 decoder is given

Induction of severe hypoxemia and low lung recruitability for the evaluation of therapeutic ventilation strategies: a translational model of combined surfactant-depletion and ventilator-induced lung injury

Background: Models of hypoxemic lung injury caused by lavage-induced pulmonary surfactant depletion are prone to prompt recovery of blood oxygenation following recruitment maneuvers and have limited translational validity. We hypothesized that addition of injurious ventilation following surfactant-depletion creates a model of the acute respiratory distress syndrome (ARDS) with persistently low recruitability and higher levels of titrated "best" positive end-expiratory pressure (PEEP) during protective ventilation. Methods: Two types of porcine lung injury were induced by lung lavage and 3 h of either protective or injurious ventilation, followed by 3 h of protective ventilation (N = 6 per group). Recruitment maneuvers (RM) and decremental PEEP trials comparing oxygenation versus dynamic compliance were performed after lavage and at 3 h intervals of ventilation. Pulmonary gas exchange function, respiratory mechanics, and ventilator-derived parameters were assessed after each RM to map the course of injury severity and recruitability. Results: Lung lavage impaired respiratory system compliance (C-rs) and produced arterial oxygen tensions (PaO2) of 84 +/- 13 and 80 +/- 15 (FIO2 = 1.0) with prompt increase after RM to 270-395 mmHg in both groups. After subsequent 3 h of either protective or injurious ventilation, PaO2/FIO2 was 104 +/- 26 vs. 154 +/- 123 and increased to 369 +/- 132 vs. 167 +/- 87 mmHg in response to RM, respectively. After additional 3 h of protective ventilation, PaO2/FIO2 was 120 +/- 15 vs. 128 +/- 37 and increased to 470 +/- 68 vs. 185 +/- 129 mmHg in response to RM, respectively. Subsequently, decremental PEEP titration revealed that C-rs peaked at 36 +/- 10 vs. 25 +/- 5 ml/cm H2O with PEEP of 12 vs. 16 cmH(2)O, and PaO2/FIO2 peaked at 563 +/- 83 vs. 334 +/- 148 mm Hg with PEEP of 16 vs. 22 cmH(2)O in the protective vs. injurious ventilation groups, respectively. The large disparity of recruitability between groups was not reflected in the C-rs nor the magnitude of mechanical power present after injurious ventilation, once protective ventilation was resumed. Conclusion: Addition of transitory injurious ventilation after lung lavage causes prolonged acute lung injury with diffuse alveolar damage and low recruitability yielding high titrated PEEP levels. Mimicking lung mechanical and functional characteristics of ARDS, this porcine model rectifies the constraints of single-hit lavage models and may enhance the translation of experimental research on mechanical ventilation strategies

Trials

After publication of the original article [1], the authors have notified us of an additional acknowledgement they wish to bring for their paper

Trials

BACKGROUND: The aim of this open-label, randomized controlled trial conducted in four African countries (Madagascar, Niger, Central African Republic, and Senegal) is to compare three strategies of renutrition for moderate acute malnutrition (MAM) in children based on modulation of the gut microbiota with enriched flours alone, enriched flours with prebiotics or enriched flours coupled with antibiotic treatment. METHODS: To be included, children aged between 6 months and 2 years are preselected based on mid-upper-arm circumference (MUAC) and are included based on a weight-for-height Z-score (WHZ) between - 3 and - 2 standard deviations (SD). As per current protocols, children receive renutrition treatment for 12 weeks and are assessed weekly to determine improvement. The primary endpoint is recovery, defined by a WHZ >/= - 1.5 SD after 12 weeks of treatment. Data collected include clinical and socioeconomic characteristics, side effects, compliance and tolerance to interventions. Metagenomic analysis of gut microbiota is conducted at inclusion, 3 months, and 6 months. The cognitive development of children is evaluated in Senegal using only the Developmental Milestones Checklist II (DMC II) questionnaire at inclusion and at 3, 6, and 9 months. The data will be correlated with renutrition efficacy and metagenomic data. DISCUSSION: This study will provide new insights for the treatment of MAM, as well as original data on the modulation of gut microbiota during the renutrition process to support (or not) the microbiota hypothesis of malnutrition. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT03474276 Last update 28 May 2018

Automated extraction of scenario sequences from disciplined dataflow networks.

Analysing deadlock-freedom, boundedness and realtime constraints are crucial steps in the design of embedded streaming applications. Dataflow models of computation are often used to analyse such properties at design-time. To that end, scenario-based dataflow techniques isolate the individual operating scenarios of a dynamic application and analyse the executions of the possible scenario sequences. These techniques have rigorous analytical methods to verify consistency and realtime constraints. To exploit these benefits, identification of all scenarios and scenario sequences is required. This is challenging because of the large number of possible scenarios in modern-day dynamic applications. Manual construction is generally time-consuming and error-prone. In this paper, we address this challenge with an automated approach that extracts a scenario-based analysis model for a class of parallel implementations, which we call Disciplined Dataflow Network (DDN). DDN always guarantees construction of a scenario-based analysis model and enables automating the extraction process. The extraction process identifies all possible scenarios of a DDN and employs state-space enumeration to determine all possible sequences of executions of these scenarios. The approach is demonstrated for the CAL actor language and has been implemented in an openly available CAL compiler. Case studies are presented for the RVC-MPEG video decoder and WLAN 802.11a baseband processing. The case studies show the benefits of automated scenario extraction for efficient design-time analysis of dynamic streaming applications

Automated extraction of scenario sequences from disciplined dataflow networks.

Analysing deadlock-freedom, boundedness and realtime constraints are crucial steps in the design of embedded streaming applications. Dataflow models of computation are often used to analyse such properties at design-time. To that end, scenario-based dataflow techniques isolate the individual operating scenarios of a dynamic application and analyse the executions of the possible scenario sequences. These techniques have rigorous analytical methods to verify consistency and realtime constraints. To exploit these benefits, identification of all scenarios and scenario sequences is required. This is challenging because of the large number of possible scenarios in modern-day dynamic applications. Manual construction is generally time-consuming and error-prone. In this paper, we address this challenge with an automated approach that extracts a scenario-based analysis model for a class of parallel implementations, which we call Disciplined Dataflow Network (DDN). DDN always guarantees construction of a scenario-based analysis model and enables automating the extraction process. The extraction process identifies all possible scenarios of a DDN and employs state-space enumeration to determine all possible sequences of executions of these scenarios. The approach is demonstrated for the CAL actor language and has been implemented in an openly available CAL compiler. Case studies are presented for the RVC-MPEG video decoder and WLAN 802.11a baseband processing. The case studies show the benefits of automated scenario extraction for efficient design-time analysis of dynamic streaming applications