Challenges In The Simultaneous Development And Deployment Of A Large Integrated Modelling System

Many of our natural resource management issues cannot be adequately informed by a single discipline or sub-discipline, and require an integration of information from multiple natural and human systems. As we are unable to observe and monitor more than a few important indicators there is a strong reliance on supplementing observed information with modelled information. Following a period of record drought in the 1990’s, the Australian government recognised the need for better quality, more integrated, and nationally consistent water information. The Australian Water Resources Assessment system (AWRA) is an integrated hydrological modelling system developed by CSIRO and Australian Bureau of Meteorology (the Bureau) as part of the Water Information Research and Development Alliance (WIRADA) to support the development of two new water information products produced by the Bureau. This paper outlines the informatics, systems implementation and integration challenges in the development and deployment of the proto-operational AWRA system. A key challenge of model integration is how you access and repurpose data, how you reconcile semantic differences between both models and disparate input data sources, how you translate terms when passing between often conceptually different modelling components and how you ensure consistent identity between real world objects. The rapid development of AWRA and simultaneous transfer to an operational environment also raised many additional challenges, such as supporting multiple technologies and differing development rates of each model component, while still maintaining a working system. Additionally the continentally sized model extent, combined with techniques relatively new to the hydrologic domain, such as data assimilation and continental calibration, have introduced significant computational overheads. While an in-house fit for purpose operational build of AWRA is currently under development within the Bureau, the research challenges undertaken early in AWRA’s development still hold many valuable lessons. We have found that the use of file standards such as NetCDF, services-based modelling, and scientific workflow technologies such as ‘The WorkBench’ combined with strong model governance has mostly reduced the burden of system development and deployment and exposes some important lessons for future integrated modelling and systems integration efforts

Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics

L

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs

Who is my leader? A case study from a hospital disaster scenario in a less developed country

Introduction: A paucity of literature exists pertaining to the role of leaders during the health response to disasters. The minimal published literature regarding disaster leadership suggests that health leadership in a disaster should adopt an approach similar to that of professions such as law enforcement, military and freighting. Aim: This paper aims to describe observations pertaining to disaster leadership during a mock disaster scenario. Background: This case study is set in Surkhet, Nepal, a small city prone to disasters such as earthquakes and floods. This case presents a mock disaster scenario of an earthquake set at a nongovernment health facility. Methods: Observations were made of the performance of responders in establishing triage, treatment and command centers. Results: Institutional leaders among the responders struggled to apply the disaster plans in the face of spontaneous disaster leadership. Conclusions: Both the recognised leadership of an organisation, and those who in a disaster may step up as disaster leaders need to be confident in implementing the disaster contingency plans. Leadership in disasters must have a clear distinction between incident controller and ‘clinical leader’ roles. Discussion and recommendations: This paper provides recommendations that may have applicability to leadership in real world disasters.No Full Tex

A Best of Both Worlds Approach to Complex, Efficient, Time Series Data Delivery

Part 5: Architectures, Infrastructures, Platforms and ServicesInternational audiencePoint time series are a key data-type for the description of real or modelled environmental phenomena. Delivering this data in useful ways can be challenging when the data volume is large, when computational work (such as aggregation, subsetting, or re-sampling) needs to be performed, or when complex metadata is needed to place data in context for understanding. Some aspects of these problems are especially relevant to the environmental domain: large sensor networks measuring continuous environmental phenomena sampling frequently over long periods of time generate very large datasets, and rich metadata is often required to understand the context of observations. Nevertheless, timeseries data, and most of these challenges, are prevalent beyond the environmental domain, for example in financial and industrial domains.A review of recent technologies illustrates an emerging trend toward high performance, lightweight, databases specialized for time series data. These databases tend to have non-existent or minimalistic formal metadata capacities. In contrast, the environmental domain boasts standards such as the Sensor Observation Service (SOS) that have mature and comprehensive metadata models but existing implementations have had problems with slow performance.In this paper we describe our hybrid approach to achieve efficient delivery of large time series datasets with complex metadata. We use three subsystems within a single system-of-systems: a proxy (Python), an efficient time series database (InfluxDB) and a SOS implementation (52 North SOS). Together these present a regular SOS interface. The proxy processes standard SOS queries and issues them to the either 52 North SOS or to InfluxDB for processing. Responses are returned directly from 52 North SOS or indirectly from InfluxDB via Python proxy where they are processed into WaterML. This enables the scalability and performance advantages of the time series database to be married with the sophisticated metadata handling of SOS. Testing indicates that a recent version of 52 North SOS configured with a Postgres/PostGIS database performs well but an implementation incorporating InfluxDB and 52 North SOS in a hybrid architecture performs approximately 12 times faster

Sleep loss in male rats contributes more to weight gain during sleep disruption than stress assessed by corticosterone

Sleep disruption (SD) promotes stress which may mediate the effect of SD induced by noise on bodyweight gain and food intake. We determined if the change in bodyweight during SD caused by noise was driven by stress (assessed by corticosterone) and whether the effects of noise on SD, stress and bodyweight were specific to the method of SD or a consequence of SD per se. We isolated stress from SD due to noise by exposing rats to noise during the darkphase to test whether darkphase noise stimulated weight gain, stress and food intake. Male Sprague-Dawley rats slept undisturbed, were exposed to noise during both circadian phases (lightphase vs darkphase) and lightphase gentle handling. Bodyweight, food intake, physical activity, vigilance states, and plasma corticosterone were determined. Darkphase noise did not affect vigilance states. Unlike lightphase noise, darkphase noise and lightphase gentle handling did not stimulate weight gain or food intake. Only gentle handling significantly increased corticosterone levels. Noise during the lightphase increasesed weight gain and food intake by causing SD and these effects were not driven by stress as assessed by corticosterone. These results may have significant implications for developing translational models of insomnia-induced obesity in humans.12 month embargo; available online: 09 November 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Quality indicators for capsule endoscopy and deep enteroscopy

Introduction: Capsule endoscopy (CE) and deep enteroscopy (DE) can be useful for diagnosing and treating suspected small-bowel disease. Guidelines and detailed recommendations exist for the use of CE/DE, but comprehensive quality indicators are lacking. The goal of this task force was to develop quality indicators for appropriate use of CE/DE by using a modified RAND/UCLA Appropriateness Method. Methods: An expert panel of 7 gastroenterologists with diverse practice experience was assembled to identify quality indicators. A literature review was conducted to develop a list of proposed quality indicators applicable to preprocedure, intraprocedure, and postprocedure periods. The panelists reviewed the literature; identified and modified proposed quality indicators; rated them on the basis of scientific evidence, validity, and necessity; and determined proposed performance targets. Agreement and consensus with the proposed indicators were verified using the RAND/UCLA Appropriateness Method. Results: The voting procedure to prioritize metrics emphasized selecting measures to improve quality and overall patient care. Panelists rated indicators on the perceived appropriateness and necessity for clinical practice. After voting and discussion, 2 quality indicators ranked as inappropriate or uncertain were excluded. Each quality indicator was categorized by measure type, performance target, and summary of evidence. The task force identified 13 quality indicators for CE and DE. Discussion: Comprehensive quality indicators have not existed for CE or DE. The task force identified quality indicators that can be incorporated into clinical practice. The panel also addressed existing knowledge gaps and posed research questions to better inform future research and quality guidelines for these procedures