Staying on track - Keeping things running in a high-end scientific imaging core facility

Modern life science research is a collaborative effort. Few research groups can single-handedly support the necessary equipment, expertise and personnel needed for the ever-expanding portfolio of technologies that are required across multiple disciplines in today's life science endeavours. Thus, research institutes are increasingly setting up scientific core facilities to provide access and specialised support for cutting-edge technologies. Maintaining the momentum needed to carry out leading research while ensuring high-quality daily operations is an ongoing challenge, regardless of the resources allocated to establish such facilities. Here, we outline and discuss the range of activities required to keep things running once a scientific imaging core facility has been established. These include managing a wide range of equipment and users, handling repairs and service contracts, planning for equipment upgrades, renewals, or decommissioning, and continuously upskilling while balancing innovation and consolidation.

Correlating Time Series Signals and Event Logs in Embedded Systems

In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems.

Device monitoring in heart failure management: outcomes based on a systematic review and meta-analysis

Implantable devices have been developed for continuous monitoring of heart failure. We investigated the effect of fluids and hemodynamic monitoring, using these devices, on heart failure clinical outcomes. Literature search was performed January 2000 through May 2017 of studies comparing device monitored patients with control group. Random-effects meta-analysis was used to pool outcomes across the studies. A total of 5,454 patients were included from 14 studies. There was no difference in heart failure (HF)-related admissions rate [odds ratio (OR) 1.25, 95% CI: 0.92-1.69, P=0.15], all-cause mortality (OR 1.21, 95% CI: 0.91-1.61, P=0.20) or combined admission rate and all-cause mortality (OR 1.21, 95% CI: 0.89-1.64, P=0.22) between the device monitored and the control group. In a subgroup analysis including only pressure sensors devices, there was no difference in all-cause mortality (OR 1.04, 95% CI: 0.62-1.74, P=0.89), however, there was a lower admissions rate (OR 1.63, 95% CI: 1.10-2.41, P=0.02). In a subgroup of only impedance monitoring devices, there was no difference in all-cause mortality or admissions rate. Pressure monitoring was associated with lower HF admissions rate. No improvement in these outcomes was noted with impedance monitoring.