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On March 17, 2020, a member of a Skagit County, Washington, choir informed Skagit County Public Health (SCPH) that several members of the 122-member choir had become ill. Three persons, two from Skagit County and one from another area, had test results positive for SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). Another 25 persons had compatible symptoms. SCPH obtained the choir's member list and began an investigation on March 18. Among 61 persons who attended a March 10 choir practice at which one person was known to be symptomatic, 53 cases were identified, including 33 confirmed and 20 probable cases (secondary attack rates of 53.3% among confirmed cases and 86.7% among all cases). Three of the 53 persons who became ill were hospitalized (5.7%), and two died (3.7%). The 2.5-hour singing practice provided several opportunities for droplet and fomite transmission, including members sitting close to one another, sharing snacks, and stacking chairs at the end of the practice. The act of singing, itself, might have contributed to transmission through emission of aerosols, which is affected by loudness of vocalization (1). Certain persons, known as superemitters, who release more aerosol particles during speech than do their peers, might have contributed to this and previously reported COVID-19 superspreading events (2-5). These data demonstrate the high transmissibility of SARS-CoV-2 and the possibility of superemitters contributing to broad transmission in certain unique activities and circumstances. It is recommended that persons avoid face-to-face contact with others, not gather in groups, avoid crowded places, maintain physical distancing of at least 6 feet to reduce transmission, and wear cloth face coverings in public settings where other social distancing measures are difficult to maintain.

There is a skin allergy epidemic in the western world, and the rate of deterioration has increased significantly in the past 5–10 years. It is probable that there are many environmental contributing factors, yet some studies have linked it primarily to the rise in the use of synthetic chemical ingredients in modern cosmetics. Our challenge, therefore, was to find a mechanism to determine the effect these substances have on skin health, and whether they really are a primary cause of long term damage to the skin. The first problem is the lack of any definitive way to measure skin health. Motivated by the overwhelming evidence for a link between deficient gut flora and ill health, we decided to look at whether our skin microbiota could similarly be used as an indicator of skin health. Our research illustrates how microbiota diversity alone can predict whether skin is healthy or not, after we revealed a complete lack of conclusive findings linking the presence or abundance of particular species of microbe to skin problems. This phenomenon is replicated throughout nature, where high biodiversity always leads to healthy ecosystems. ‘Caveman’ skin, untouched by modern civilisation, was far different to “western” skin and displayed unprecedented levels of bacterial diversity. The less exposed communities were to western practices, the higher the skin diversity, which is clear evidence of an environmental factor in the developed world damaging skin. For the first time we propose benchmark values of diversity against which we can measure skin to determine how healthy it is. This gives us the ability to be able to predict which people are more likely to be prone to skin ailments, and start to test whether cosmetic ingredients and products are a main cause of the skin allergy epidemic.

A significant loss of microbial biodiversity on the skin has been linked to an increased prevalence of skin problems in the western world. The primary objective of this study was to obtain a benchmark value for the microbial diversity found on healthy western skin, using the Chao1 index. This benchmark was used to update our 2017 skin health measuring mechanism in line with standardised methodology. It used 50 human participants from Graz in Austria and at a read depth of 6600 sequences, we found the average Chao1 diversity to be ~180, with upper and lower quartiles of ~208 and ~150, respectively. Previous work with a larger sample size was unsatisfactory to use as a benchmark because different diversity indices and evaluation methodologies were used. The Medical University of Graz used the most recent version of the Chao1 index to obtain diversity results. Because of this study, we can transfer other benchmarks of skin microbiome diversity to the methodology used in this work from our 2017 study, such as “unhealthy western skin” and “caveman/perfect skin”. This could aid with the diagnostic assessment of susceptibility to cutaneous conditions or diseases and treatment. We also investigated the effect of sex and age, which are two known skin microbiome affecting factors. Although no statistical significance is seen for sex- and age-related changes in diversity, there appear to be changes related to both. Our preliminary results (10 in each of the five age groups) show adults aged 28–37 have the highest average diversity, and adults aged 48–57 have the lowest average diversity. In future work, this could be improved by obtaining benchmark diversity values from a larger sample size for any age, sex, body site, and area of residence, to which subjects can be compared. These improvements could help to investigate the ultimate question regarding which environmental factors in the western world are the main cause of the huge rise in skin problems. This could lead to future restrictions of certain synthetic chemicals or products found to be particularly harmful to the skin.

Our future designers have much to learn from the complex and highly functional systems found in nature. Creating design products that are not only human-centred but also in tune with the natural world requires our designers to be exposed to natural phenomena and scientific principles. To provide design students with a starting point, we have created BioForm: a bio-inspired design module run as part of the Product Design curricula at the National College of Art and Design (NCAD), Dublin, Ireland. The module is delivered by an interdisciplinary team of designers and scientists who expose students to biologically inspired theory and practice through a series of lectures, workshops and site visits, aimed at encouraging bio-inspiration in their design practice. The students, with their growing understanding of bio-inspiration, are then challenged to design a chair, which allows them to playfully explore form and function, and to consider its impact on their design. We hope that by encouraging bio-inspiration in students’ practice they produce designs that are innovative and more environmentally sustainable. This paper reflects on the BioForm project’s pedagogical approach, its impact on the student’s design practice and proposes further developments for the module.

Turn-taking is richly multimodal. Predictive turn-taking models (PTTMs) facilitate naturalistic human-robot interaction, yet most rely solely on speech. We introduce MM-VAP, a multimodal PTTM which combines speech with visual cues including facial expression, head pose and gaze. We find that it outperforms the state-of-the-art audio-only in videoconferencing interactions (84% vs. 79% hold/shift prediction accuracy). Unlike prior work which aggregates all holds and shifts, we group by duration of silence between turns. This reveals that through the inclusion of visual features, MM-VAP outperforms a state-of-the-art audio-only turn-taking model across all durations of speaker transitions. We conduct a detailed ablation study, which reveals that facial expression features contribute the most to model performance. Thus, our working hypothesis is that when interlocutors can see one another, visual cues are vital for turn-taking and must therefore be included for accurate turn-taking prediction. We additionally validate the suitability of automatic speech alignment for PTTM training using telephone speech. This work represents the first comprehensive analysis of multimodal PTTMs. We discuss implications for future work and make all code publicly available.

Accurate predictive turn-taking models (PTTMs) are essential for naturalistic human-robot interaction. However, little is known about their performance in noise. This study therefore explores PTTM performance in types of noise likely to be encountered once deployed. Our analyses reveal PTTMs are highly sensitive to noise. Hold/shift accuracy drops from 84% in clean speech to just 52% in 10 dB music noise. Training with noisy data enables a multimodal PTTM, which includes visual features to better exploit visual cues, with 72% accuracy in 10 dB music noise. The multimodal PTTM outperforms the audio-only PTTM across all noise types and SNRs, highlighting its ability to exploit visual cues; however, this does not always generalise to new types of noise. Analysis also reveals that successful training relies on accurate transcription, limiting the use of ASR-derived transcriptions to clean conditions. We make code publicly available for future research.

An interview with Samantha Sam Russell, an environmental health specialist II at Skagit County Public Health in WA, is presented. Among other things, she talks about joining the NEHA, why she chooses the environmental health field, and the best professional advice she receives.

Immune signaling needs to be well-regulated to promote clearance of pathogens, while preventing aberrant inflammation. Interferons (IFNs) and antiviral genes are activated by the detection of viral RNA by RIG-I-like receptors (RLRs). Signal transduction downstream of RLRs proceeds through a multi-protein complex organized around the central adaptor protein MAVS. Recent work has shown that protein complex function can be modulated by RNA molecules providing allosteric regulation or acting as molecular guides or scaffolds. Thus, we hypothesized that RNA plays a role in organizing MAVS signaling platforms. Here, we show that MAVS, through its central intrinsically disordered domain, directly interacts with the 3' untranslated regions of cellular mRNAs. Importantly, elimination of RNA by RNase treatment disrupts the MAVS signalosome, including newly identified regulators of RLR signaling, and inhibits phosphorylation of the transcription factor IRF3. This supports the hypothesis that RNA molecules scaffold proteins in the MAVS signalosome to induce IFNs. Together, this work uncovers a function for cellular RNA in promoting signaling through MAVS and highlights a generalizable principle of RNA regulatory control of cytoplasmic immune signaling complexes.