MOTIVATION: Long-read sequencing (LRS) is increasingly used for human medical research and clinical diagnostics due to its capacity to generate complete genome information. However, there is a lack of robust and easy-to-use pipelines for comprehensive LRS data analysis. RESULTS: Here we present Nallo, a Nextflow pipeline for analysis of PacBio and Oxford Nanopore data, with additional support for rare disease research projects. The pipeline detects a wide range of genetic variants, performs genome assembly, and reports CpG methylation. It also enables annotation and ranking of variants based on their predicted functional consequences. AVAILABILITY AND IMPLEMENTATION: Nallo is available from GitHub: https://github.com/genomic-medicine-sweden/nallo.

Real-time human pose estimation and tracking on monocular videos is a fundamental task in computer vision with a wide range of applications. Recently, benefiting from deep learning-based methods, it has received impressive progress in performance. Although some works have reviewed and summarised the advancements in this field, few have specifically focused on real-time performance and monocular video-based solutions. The goal of this review is to bridge this gap by providing a comprehensive understanding of real-time monocular video-based human pose estimation and tracking, encompassing both 2D and 3D domains, as well as single-person and multi-person scenarios. To achieve this objective, this paper systematically reviews 68 papers published between 2014 and 2024 to answer six research questions. This review brings new insights into computational efficiency measures and hardware configurations of existing methods. Additionally, this review provides a deep discussion on trade-off strategies for accuracy and efficiency in real-time systems. Finally, this review highlights promising directions for future research and provides practical solutions for real-world applications.

Virtual reality allows creating highly immersive visual and auditory experiences, making users feel physically present in the environment. This makes it an ideal platform to simulate dangerous scenarios, including fire evacuation, and study human behaviour without exposing users to harmful elements. However, human perception of the surroundings is based on the integration of multiple sensory cues (visual, auditory, tactile, or/and olfactory) present in the environment. When some of the sensory stimuli are missing in the virtual experience, it can break the illusion of being there in the environment and could lead to actions that deviate from normal behaviour. In this work, we added an olfactory cue in a well-documented historic hotel fire scenario that was recreated in VR, and examined the effects of the olfactory cue on human behaviour. We conducted a between subject study on 40 naive participants. Our results show that the addition of the olfactory cue could increase behavioural realism. We found that 80% of the studied actions for the VR with olfactory cue condition matched the ones performed by the survivors. In comparison, only 40% of the participants’ actions for VR only condition were similar to the survivors.

Physical-layer identification using hardware imperfections, known as radiometric fingerprinting, has existed for some time, but little focus has been put on Bluetooth Low Energy (BLE). This work systematically explores features for physical-layer identification of BLE. We evaluate the fingerprinting performance on different feature sets, and we discuss the potential issues with the robustness that may arise in a practical environment. Accuracy results are achieved in excess of 99%, showing potential for the system to work in security settings to safeguard a Bluetooth network.

BACKGROUND: Exome and genome sequencing is becoming the method of choice for rare disease diagnostics. One of the key challenges remaining is distinguishing the disease causing variants from the benign background variation. After analysis and annotation of the sequencing data there are typically thousands of candidate variants requiring further investigation. One of the most effective and least biased ways to reduce this number is to assess the rarity of a variant in any population. Currently, there are a number of reliable sources of information for major population frequencies when considering single nucleotide variants (SNVs) and small insertion and deletions (INDELs), with gnomAD as the most prominent public resource available. However, local variation or frequencies in sub-populations may be underrepresented in these public resources. In contrast, for structural variation (SV), the background frequency in the general population is more or less unknown mostly due to challenges in calling SVs in a consistent way. Keeping track of local variation is one way to overcome these problems and significantly reduce the number of potential disease causing variants retained for manual inspection, both for SNVs and SVs. RESULTS: Here, we present loqusdb, a tool to solve the challenge of keeping track of any type of variant observations from genome sequencing data. Loqusdb was designed to handle a large flow of samples and unlike other solutions, samples can be added continuously to the database without rebuilding it, facilitating improvements and additions. We assessed the added value of a local observations database using 98 samples annotated with information from a background of 888 unrelated individuals. CONCLUSIONS: We show both how powerful SV analysis can be when filtering for population frequencies and how the number of apparently rare SNVs/INDELs can be reduced by adding local population information even after annotating the data with other large frequency databases, such as gnomAD. In conclusion, we show that a local frequency database is an attractive, and a necessary addition to the publicly available databases that facilitate the analysis of exome and genome data in a clinical setting.

BACKGROUND: DNA damage accumulates over the course of cancer development. The often-substantial amount of somatic mutations in cancer poses a challenge to traditional methods to characterize tumors based on driver mutations. However, advances in machine learning technology can take advantage of this substantial amount of data. RESULTS: We developed a command line interface python package, pyCancerSig, to perform sample profiling by integrating single nucleotide variation (SNV), structural variation (SV) and microsatellite instability (MSI) profiles into a unified profile. It also provides a command to decipher underlying cancer processes, employing an unsupervised learning technique, Non-negative Matrix Factorization, and a command to visualize the results. The package accepts common standard file formats (vcf, bam). The program was evaluated using a cohort of breast- and colorectal cancer from The Cancer Genome Atlas project (TCGA). The result showed that by integrating multiple mutations modes, the tool can correctly identify cases with known clear mutational signatures and can strengthen signatures in cases with unclear signal from an SNV-only profile. The software package is available at https://github.com/jessada/pyCancerSig. CONCLUSIONS: pyCancerSig has demonstrated its capability in identifying known and unknown cancer processes, and at the same time, illuminates the association within and between the mutation modes.

Abstract Different virtual reality (VR) experimental tools and equipment are available for evacuation research, ranging from highly immersive systems such as cave automatic virtual environments (CAVEs) to head‐mounted displays (HMDs). In particular, mobile‐powered HMDs are an extremely cost‐efficient solution, for which the research potential for evacuation studies needs to be assessed. This study compares the results of tunnel evacuation experiments aimed at investigating the design of flashing lights on emergency exit portals using two different VR methods (CAVE vs. mobile‐powered HMD). The experiments were performed by repeating the same case study in a CAVE laboratory and a low‐cost mobile‐powered HMD. The CAVE experiment involved 96 participants, whereas the HMD experiment involved 55 participants. An affordance‐based questionnaire was used to interview participants immersed in a VR road tunnel emergency evacuation scenario and rank different emergency portal designs. Questionnaire results show consistency between the two experimental methods for the variables investigated, thus leading to be in favor of the use of low‐cost mobile‐powered HMD tools in evacuation scenarios with a relatively limited level of complexity.

Virtual reality (VR) has become a popular approach to study human behavior in fire.The present position paper analyses Strengths, Weaknesses, Opportunities, and Threats (SWOT) of VR as a research tool for human behavior in fire.Virtual environments provide a maximum of experimental control, are easy to replicate, have relatively high ecological validity, and allow safe study of occupant behavior in scenarios that otherwise would be too dangerous.Lower ecological validity compared to field studies, ergonomic aspects, and technical limitations are the main weaknesses of the method.Increasingly realistic simulations and other technological advances provide new opportunities for this relatively young method.In this position paper, we argue that VR is a promising complementary laboratory tool in the quest to understand human behavior in fire and to improve fire safety.