Emteq (United Kingdom)

BackgroundThe Human Genome Variation Society (HGVS) Nomenclature is the global standard for describing and communicating variants in DNA, RNA, and protein sequences in clinical and research genomics. This manuscript details recent updates to the HGVS Nomenclature, highlighting improvements in governance, community engagement, website functionality, and underlying implementation of the standard.MethodsThe HGVS Variant Nomenclature Committee (HVNC) now operates under the Human Genome Organization (HUGO), facilitating broader community feedback and collaboration with related standards organizations. The website has been redesigned using modern documentation tools and practices. The specification was updated to include guidance for transcript selection and to align with recent cross-consortia recommendations for the representation of gene fusions. A formal computational grammar was introduced to improve the precision and consistency of variant descriptions.ResultsMajor improvements in HGVS Nomenclature v. 21.1 include a redesigned website with enhanced navigation, search functionality, and mobile responsiveness; a new versioning policy aligned with software management practices; formal mechanisms for community feedback and change proposals; and adoption of Extended Backus-Naur Form (EBNF) for defining syntax. The specification now recommends MANE Select transcripts where appropriate and includes updated guidance for representing adjoined transcripts and gene fusions. All content is freely available under permissive licenses at hgvs-nomenclature.org.ConclusionsThese advancements establish a more sustainable foundation for maintaining and evolving the HGVS Nomenclature while improving its accessibility and utility. The introduction of formal computational grammar marks a crucial step toward unambiguous variant descriptions that can be reliably processed by both humans and machines. Combined with enhanced community engagement mechanisms and improved guidance, these changes position the HGVS Nomenclature to better serve the evolving needs of clinical and research genomics while maintaining the stability that users require.

BackgroundPlexins are large transmembrane receptors for the semaphorin family of signalling proteins. Semaphorin-plexin signalling controls cellular interactions that are critical during development as well as in adult life stages. Nine plexin genes have been identified in humans, but despite the apparent importance of plexins in development, only biallelic PLXND1 and PLXNA1 variants have so far been associated with Mendelian genetic disease.MethodsEight individuals from six families presented with a recessively inherited variable clinical condition, with core features of amelogenesis imperfecta (AI) and sensorineural hearing loss (SNHL), with variable intellectual disability. Probands were investigated by exome or genome sequencing. Common variants and those unlikely to affect function were excluded. Variants consistent with autosomal recessive inheritance were prioritised. Variant segregation analysis was performed by Sanger sequencing. RNA expression analysis was conducted in C57Bl6 mice.ResultsRare biallelic pathogenic variants in plexin B2 (PLXNB2), a large transmembrane semaphorin receptor protein, were found to segregate with disease in all six families. The variants identified include missense, nonsense, splicing changes and a multiexon deletion. Plxnb2 expression was detected in differentiating ameloblasts.ConclusionWe identify rare biallelic pathogenic variants in PLXNB2 as a cause of a new autosomal recessive, phenotypically diverse syndrome with AI and SNHL as core features. Intellectual disability, ocular disease, ear developmental abnormalities and lymphoedema were also present in multiple cases. The variable syndromic human phenotype overlaps with that seen in Plxnb2 knockout mice, and, together with the rarity of human PLXNB2 variants, may explain why pathogenic variants in PLXNB2 have not been reported previously.

Understanding emotional states through facial expressions plays a pivotal role in various domains, from mental health assessments to human-computer interaction. In this study, we present an algorithm tailored for efficient, real-time, on-the-edge monitoring of key facial expressions, utilizing the data from novel optomyography (OMG) based smart glasses - OCOsense™. The expressions targeted in this study include low-and high-intensity variation of: smile, frown, eyebrow raise, and winks, facilitating a comprehensive understanding of key facial expressions essential for emotional assessment. The algorithm, developed and evaluated on a large dataset of 40 participants, prioritizes low power consumption and fast inference times. After performing optimization steps that include feature selection and model hyper-parameter tuning, our final prototype achieved a compact code size of 71.3 kB and an accuracy of 93.8%, all while maintaining power consumption at 0.43 watts.

This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included smiling, frowning, raising the eyebrows, and squeezing the eyes. The statistical analysis demonstrated that: (i) OCO sensors based on the principles of OMG can capture distinct variations in cheek and brow movements with a high degree of accuracy and specificity; (ii) Head movement does not have a significant impact on how well these facial expressions are detected. The collected data were also used to train a machine learning model to recognise the four facial expressions and when the face enters a neutral state. We evaluated this model in conditions intended to simulate real-world use, including variations in expression intensity, head movement and glasses position relative to the face. The model demonstrated an overall accuracy of 93% (0.90 f1-score)—evaluated using a leave-one-subject-out cross-validation technique.

Continuous assessment of affective behaviors could improve the diagnosis, assessment and monitoring of chronic mental health and neurological conditions such as depression. However, there are no technologies well suited to this, limiting potential clinical applications. To test if we could replicate previous evidence of hypo reactivity to emotional salient material using an entirely new sensing technique called optomyography which is well suited to remote monitoring. Thirty-eight depressed and 37 controls (≥18, ≤40 years) who met a research diagnosis of depression and an age-matched non-depressed control group. Changes in facial muscle activity over the brow (corrugator supercilli) and cheek (zygomaticus major) were measured whilst volunteers watched videos varying in emotional salience. Across all participants, videos rated as subjectively positive were associated with activation of muscles in the cheek relative to videos rated as neutral or negative. Videos rated as subjectively negative were associated with brow activation relative to videos judged as neutral or positive. Self-reported arousal was associated with a step increase in facial muscle activation across the brow and cheek. Group differences were significantly reduced activation in facial muscles during videos considered subjectively negative or rated as high arousal in depressed volunteers compared with controls. We demonstrate for the first time that it is possible to detect facial expression hypo-reactivity in adults with depression in response to emotional content using glasses-based optomyography sensing. It is hoped these results may encourage the use of optomyography-based sensing to track facial expressions in the real-world, outside of a specialized testing environment.

Using a novel wearable surface electromyography (sEMG), we investigated induced affective states by measuring the activation of facial muscles traditionally associated with positive (left/right orbicularis and left/right zygomaticus) and negative expressions (the corrugator muscle). In a sample of 38 participants that watched 25 affective videos in a virtual reality environment, we found that each of the three variables examined—subjective valence, subjective arousal, and objective valence measured via the validated video types (positive, neutral, and negative)—sEMG amplitude varied significantly depending on video content. sEMG aptitude from “positive muscles” increased when participants were exposed to positively valenced stimuli compared with stimuli that was negatively valenced. In contrast, activation of “negative muscles” was elevated following exposure to negatively valenced stimuli compared with positively valenced stimuli. High arousal videos increased muscle activations compared to low arousal videos in all the measured muscles except the corrugator muscle. In line with previous research, the relationship between sEMG amplitude as a function of subjective valence was V-shaped.

Faceteq prototype v.05 is a wearable technology for measuring facial expressions and biometric responses for experimental studies in Virtual Reality. Developed by Emteq Ltd laboratory, Faceteq can enable new avenues for virtual reality research through combination of high performance patented dry sensor technologies, proprietary algorithms and real-time data acquisition and streaming. Faceteq project was founded with the aim to provide a human-centred additional tool for emotion expression, affective human-computer interaction and social virtual environments. The proposed poster will exhibit the hardware and its functionality.