Janus

Proximity detection is at the core of several mobile and ubiquitous computing applications. These include reactive use cases, e.g., alerting individuals of hazards or interaction opportunities, and others concerned only with logging proximity data, e.g., for offline analysis and modeling. Common approaches rely on Bluetooth Low Energy (BLE) or ultra-wideband (UWB) radios. Nevertheless, these strike opposite tradeoffs between the accuracy of distance estimates quantifying proximity and the energy efficiency affecting system lifetime, effectively forcing a choice between the two and ultimately constraining applicability. Janus reconciles these dimensions in a dual-radio protocol enabling accurate and energy-efficient proximity detection, where the energy-savvy BLE is exploited to discover devices and coordinate their distance measurements, acquired via the energy-hungry UWB. A model supports domain experts in configuring Janus for their use cases with predictable performance. The latency, reliability, and accuracy of Janus are evaluated experimentally, including realistic scenarios endowed with the mm-level ground truth provided by a motion capture system. Energy measurements show that Janus achieves weeks to months of autonomous operation, depending on the use case configuration. Finally, several large-scale campaigns exemplify its practical usefulness in real-world contexts.

Cardiac MRI Based Left Ventricular Global Function Index: Association with Disease Severity in Patients with ICD for Secondary Prevention

Left ventricular (LV) ejection fraction (LVEF) is the most widely used prognostic marker in cardiovascular diseases. LV global function index (LVGFI) is a novel marker which incorporates the total LV structure in the assessment of LV cardiac performance. We evaluated the prognostic significance of LVGFI, measured by cardiovascular magnetic resonance (CMR), in predicting mortality and ICD therapies in a real-world (ICD) population with secondary ICD prevention indication, to detect a high-risk group among these patients. In total, 105 patients with cardiac MRI prior to the ICD implantation were included (mean age 56 ± 16 years old; 76% male). Using the MRI data for each patient LVGFI was determined and a cut-off for the LVGFI value was calculated. Patients were followed up every four to six months in our or clinics in proximity. Data on the occurrence of heart failure symptoms and or mortality, as well as device therapies and other vital parameters, were collected. Follow up duration was 37 months in median. The mean LVGFI was 24.5%, the cut off value for LVGFI 13.5%. According to the LVGFI Index patient were divided into 2 groups, 86 patients in the group with the higher LVGFI und 19 patients in the lower group. The LVGFI correlates significantly with the LVEF (r = 0.642, p < 0.001). In Kaplan–Meier analysis, a lower LVGFI (<13.5%) was associated with a higher rate of mortality and rehospitalization (p = 0.002). In contrast, echocardiographic LVEF ≤ 33% was not associated with a higher rate of mortality or rehospitalization. Multivariate Cox-regression analysis revealed a lower LVGFI (p = 0.025, HR = 0.941; 95%-CI 0.89–0.99) and diabetes mellitus (p = 0.027, HR = 0.33; 95%-CI 0.13–0.88) as an independent predictor for mortality and rehospitalization. There was no association between the combined endpoint and the LVEFMRT, LVEFecho, NYHA > I, the initial device or a medication (each p = n.s.). Further, in Kaplan–Meier analysis no association was evident between the LVGFI and adequate ICD therapy (p = n.s.). In secondary prevention ICD patients reduced LVGFI was shown as an independent predictor for mortality and rehospitalization, but not for ICD therapies. We were able to identify a high-risk collective among these patients, but further investigation is needed to evaluate LVGFI compared to ejection fraction, especially in patients with an elevated risk for adverse cardiac events.

Heuristic Scheduler for Proximity Data Exchange

The data is generated using analytical model for analysis

A Novel Distributed and Federated Digital Contact Tracing Systems

There are several contact tracing solutions, some are for closed user groups, some are for the residents of a country, and some are open solutions with no clear boundary defined. These independent solutions are not adequate to support pre-pandemic global mobility. Ideally, what is needed is a global solution that can support decentralized control over data and at the same time support proximity data exchange among Apps developed by different vendors and for different countries. This paper proposes a family of contact tracing designs with low-risk anonymity that includes a centralized design, a distributed design, and a federated design for global solution.

Differences in social activity increase efficiency of contact tracing

Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace environment and the effectiveness depends strongly on the minimum duration of contact required to initiate quarantine. In a realistic social network, we find that forward contact tracing with immediate isolation can reduce an epidemic by more than 70%. In perspective, our findings highlight the necessity of incorporating social heterogeneity into models of mitigation strategies. Graphic abstract

Insights from the genomes of four diploid Camelina spp.

Plant evolution has been a complex process involving hybridization and polyploidization. As a result, understanding the origin and evolution of a plant’s genome is often challenging even once a published genome is available. The oilseed crop, Camelina sativa from the Brassicaceae, has a fully sequenced allohexaploid genome with potentially three unknown ancestors. To better understand which extant species best represent the ancestral genomes that contributed to C. sativa’s formation, we sequenced and assembled chromosome level draft genomes for four diploid members of Camelina: C. neglecta C. hispida var. hispida, C. hispida var. grandiflora and C. laxa using a combination of long and short read data scaffolded with proximity data. We then conducted phylogenetic analyses on regions of synteny and on genes described for Arabidopsis thaliana, from across each nuclear genome and the fully sequenced chloroplasts in order to examine the evolutionary relationships within Camelina and Camelineae. We conclude that the genome of C. neglecta is closely related to C. sativa’s sub-genome 1 and that C. hispida var. hispida and C. hispida var. grandiflora are most closely related to C. sativa’s sub-genome 3. Further, the abundance and density of transposable elements, specifically Helitrons, suggest that the progenitor genome that contributed C. sativa’s sub-genome 3 was more similar to the genome of C. hispida var. hispida than that of C. hispida var. grandiflora. These diploid genomes show few structural differences when compared to C. sativa’s genome indicating little change to chromosome structure following allopolyploidization. This work also indicates that C. neglecta and C. hispida are important resources for understanding the genetics of C. sativa and potential genetic resources for crop improvement.

Heuristic Scheduler for Proximity Data Exchange

The data is generated using analytical model for analysis

A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices

Spatial and temporal variability characterization in Precision Agriculture (PA) practices is often accomplished by proximity data gathering devices, which acquire data from a wide variety of sensors installed within the vicinity of crops. Proximity data acquisition usually depends on a hardware solution to which some sensors can be coupled, managed by a software that may (or may not) store, process and send acquired data to a back-end using some communication protocol. The sheer number of both proprietary and open hardware solutions, together with the diversity and characteristics of available sensors, is enough to deem the task of designing a data acquisition device complex. Factoring in the harsh operational context, the multiple DIY solutions presented by an active online community, available in-field power approaches and the different communication protocols, each proximity monitoring solution can be regarded as singular. Data acquisition devices should be increasingly flexible, not only by supporting a large number of heterogeneous sensors, but also by being able to resort to different communication protocols, depending on both the operational and functional contexts in which they are deployed. Furthermore, these small and unattended devices need to be sufficiently robust and cost-effective to allow greater in-field measurement granularity 365 days/year. This paper presents a low-cost, flexible and robust data acquisition device that can be deployed in different operational contexts, as it also supports three different communication technologies: IEEE 802.15.4/ZigBee, LoRa/LoRaWAN and GRPS. Software and hardware features, suitable for using heat pulse methods to measure sap flow, leaf wetness sensors and others are embedded. Its power consumption is of only 83 μA during sleep mode and the cost of the basic unit was kept below the EUR 100 limit. In-field continuous evaluation over the past three years prove that the proposed solution—SPWAS’21—is not only reliable but also represents a robust and low-cost data acquisition device capable of gathering different parameters of interest in PA practices.

Scheduling Proximity Data Exchange for Contact Tracing

The data is generated using analytical model for analysis