Chloroplast markers for the Malvaceae and the plastome of Henderson's checkermallow (Sidalcea hendersonii S.Wats.), a rare plant from the Pacific Northwest.

OBJECTIVE: Sidalcea is a genus of flowering plants restricted to the west coast of North America, commonly known as checkermallows. Remarkably, of the ~ 30 recognized species, 16 are of conservation concern (vulnerable, imperilled or critically imperilled). To facilitate biological studies in this genus, and in the wider Malvaceae, we have sequenced the whole plastid genome of Sidalcea hendersonii. This will allow us both to check those regions already developed as general Malvaceae markers in a previous study, and to search for new regions. RESULTS: By comparing the Sidalcea genome to that of Althaea, we have identified a hypervariable circa 1 kb region in the short single copy region. This region shows promise for examining phylogeographic pattern, hybridization and haplotype diversity. Remarkably, considering the conservation of plastome architecture between Sidalcea and Althaea, the former has a 237 bp deletion in the otherwise highly conserved inverted repeat region. Newly designed primers provide a PCR assay to determine presence of this indel across the Malvaceae. Screening of previously designed chloroplast microsatellite markers indicates two markers with variation within S. hendersonii that would be useful in future population conservation genetics.

Threats Detection during Human-Computer Interaction in Driver Monitoring Systems.

This paper presents an approach and a case study for threat detection during human-computer interaction, using the example of driver-vehicle interaction. We analyzed a driver monitoring system and identified two types of users: the driver and the operator. The proposed approach detects possible threats for the driver. We present a method for threat detection during human-system interactions that generalizes potential threats, as well as approaches for their detection. The originality of the method is that we frame the problem of threat detection in a holistic way: we build on the driver-ITS system analysis and generalize existing methods for driver state analysis into a threat detection method covering the identified threats. The developed reference model of the operator-computer interaction interface shows how the driver monitoring process is organized, and what information can be processed automatically, and what information related to the driver behavior has to be processed manually. In addition, the interface reference model includes mechanisms for operator behavior monitoring. We present experiments that included 14 drivers, as a case study. The experiments illustrated how the operator monitors and processes the information from the driver monitoring system. Based on the case study, we clarified that when the driver monitoring system detected the threats in the cabin and notified drivers about them, the number of threats was significantly decreased.

GRAPE: genomic relatedness detection pipeline.

Classifying the degree of relatedness between pairs of individuals has both scientific and commercial applications. As an example, genome-wide association studies (GWAS) may suffer from high rates of false positive results due to unrecognized population structure. This problem becomes especially relevant with recent increases in large-cohort studies. Accurate relationship classification is also required for genetic linkage analysis to identify disease-associated loci. Additionally, DNA relatives matching service is one of the leading drivers for the direct-to-consumer genetic testing market. Despite the availability of scientific and research information on the methods for determining kinship and the accessibility of relevant tools, the assembly of the pipeline, which stably operates on a real-world genotypic data, requires significant research and development resources. Currently, there is no open source end-to-end solution for relatedness detection in genomic data, that is fast, reliable and accurate for both close and distant degrees of kinship, combines all the necessary processing steps to work on a real data, and is ready for production integration. To address this, we developed GRAPE: Genomic RelAtedness detection PipelinE. It combines data preprocessing, identity-by-descent (IBD) segments detection, and accurate relationship estimation. The project uses software development best practices, as well as Global Alliance for Genomics and Health (GA4GH) standards and tools. Pipeline efficiency is demonstrated on both simulated and real-world datasets. GRAPE is available from: https://github.com/genxnetwork/grape.

CanDIG: Federated network across Canada for multi-omic and health data discovery and analysis.

We present the Canadian Distributed Infrastructure for Genomics (CanDIG) platform, which enables federated querying and analysis of human genomics and linked biomedical data. CanDIG leverages the standards and frameworks of the Global Alliance for Genomics and Health (GA4GH) and currently hosts data for five pan-Canadian projects. We describe CanDIG's key design decisions and features as a guide for other federated data systems.

In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors.

This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use.