A deep learning method for reflective boundary estimation.

Environment estimation is a challenging task in reverberant settings such as the underwater and indoor acoustic domains. The locations of reflective boundaries, for example, can be estimated using acoustic echoes and leveraged for subsequent, more accurate localization and mapping. Current boundary estimation methods are constrained to high signal-to-noise ratios or are customized to specific environments. Existing methods also often require a correct assignment of echoes to boundaries, which is difficult if spurious echoes are detected. To evade these limitations, a convolutional neural network (NN) method is developed for robust two-dimensional boundary estimation, given known emitter and receiver locations. A Hough transform-inspired algorithm is leveraged to transform echo times of arrival into images, which are amenable to multi-resolution regression by NNs. The same architecture is trained on transform images of different resolutions to obtain diverse NNs, deployed sequentially for increasingly refined boundary estimation. A correct echo labeling solution is not required, and the method is robust to reverberation. The proposed method is tested in simulation and for real data from a water tank, where it outperforms state-of-the-art alternatives. These results are encouraging for the future development of data-driven three-dimensional environment estimation with high practical value in underwater acoustic detection and tracking.

Every fifth published metagenome is not available to science.

Have you ever sought to use metagenomic DNA sequences reported in scientific publications? Were you successful? Here, we reveal that metagenomes from no fewer than 20% of the papers found in our literature search, published between 2016 and 2019, were not deposited in a repository or were simply inaccessible. The proportion of inaccessible data within the literature has been increasing year-on-year. Noncompliance with Open Data is best predicted by the scientific discipline of the journal. The number of citations, journal type (e.g., Open Access or subscription journals), and publisher are not good predictors of data accessibility. However, many publications in high-impact factor journals do display a higher likelihood of accessible metagenomic data sets. Twenty-first century science demands compliance with the ethical standard of data sharing of metagenomes and DNA sequence data more broadly. Data accessibility must become one of the routine and mandatory components of manuscript submissions-a requirement that should be applicable across the increasing number of disciplines using metagenomics. Compliance must be ensured and reinforced by funders, publishers, editors, reviewers, and, ultimately, the authors.

An architecture for passive joint localization and structure learning in reverberant environments.

Passive localization and tracking of a mobile emitter, and joint learning of its reverberant three-dimensional (3D) acoustic environment, where critical structural features are unknown, is a key open problem. Unaccounted-for occluders are potentially present, so that the emitter can lose line-of-sight to the receivers, and can only be observed through its reflected raypaths. The locations of reflective boundaries must therefore be jointly estimated with the emitter's position. A multistage global optimization and tracking architecture is developed to solve this problem with a relatively unconstrained model. Each stage of this architecture establishes domain knowledge such as synchronization and initial environment estimation, which are inputs for the following stages of more refined algorithms. This approach is generalizable to different physical scales and modalities and improves on methods that do not exploit the motion of the emitter. In one stage of this architecture, particle swarm optimization is used to simultaneously estimate the environment and the emitter location. In another stage, a Hough transform-inspired boundary localization algorithm is extended to 3D settings, to establish an initial estimate of the environment. The performance of this holistic approach is analyzed and its reliability is demonstrated in a reverberant watertank testbed, which models the shallow-water underwater acoustic setting.

Citizen Science Surveillance of Triazole-Resistant Aspergillus fumigatus in United Kingdom Residential Garden Soils.

Compost is an ecological niche for Aspergillus fumigatus due to its role as a decomposer of organic matter and its ability to survive the high temperatures associated with the composting process. Subsequently, composting facilities are associated with high levels of A. fumigatus spores that are aerosolized from compost and cause respiratory illness in workers. In the UK, gardening is an activity enjoyed by individuals of all ages, and it is likely that they are being exposed to A. fumigatus spores when handling commercial compost or compost they have produced themselves. In the present study, 246 citizen scientists collected 509 soil samples from locations in their gardens in the UK, from which were cultured 5,174 A. fumigatus isolates. Of these isolates, 736 (14%) were resistant to tebuconazole: the third most-sprayed triazole fungicide in the UK, which confers cross-resistance to the medical triazoles used to treat A. fumigatus lung infections in humans. These isolates were found to contain the common resistance mechanisms in the A. fumigatus cyp51A gene TR34/L98H or TR46/Y121F/T289A, as well as the less common resistance mechanisms TR34, TR53, TR46/Y121F/T289A/S363P/I364V/G448S, and (TR46)2/Y121F/M172I/T289A/G448S. Regression analyses found that soil samples containing compost were significantly more likely to grow tebuconazole-susceptible and tebuconazole-resistant A. fumigatus strains than those that did not and that compost samples grew significantly higher numbers of A. fumigatus than other samples. IMPORTANCE The findings presented here highlight compost as a potential health hazard to individuals with predisposing factors to A. fumigatus lung infections and as a potential health hazard to immunocompetent individuals who could be exposed to sufficiently high numbers of spores to develop infection. Furthermore, we found that 14% of A. fumigatus isolates in garden soils were resistant to an agricultural triazole, which confers cross-resistance to medical triazoles used to treat A. fumigatus lung infections. This raises the question of whether compost bags should carry additional health warnings regarding inhalation of A. fumigatus spores, whether individuals should be advised to wear facemasks while handling compost, or whether commercial producers should be responsible for sterilizing compost before shipping. The findings support increasing public awareness of the hazard posed by compost and investigating measures that can be taken to reduce the exposure risk.

Effects of acoustic nonlinearity on communication performance in soft tissues.

Acoustic communication has been gaining traction as an alternative communication method in nontraditional media, such as underwater or through tissue. Acoustic propagation is known to be a nonlinear phenomenon; nonlinear propagation of acoustic waves in soft tissues at biomedical frequencies and intensities has been widely demonstrated. However, the effects of acoustic nonlinearity on communication performance in biological tissues have not yet been examined. In this work, nonlinear propagation of a communication signal in soft tissues is analyzed. The relationship between communication parameters (signal amplitude, bandwidth, and center frequency) and nonlinear distortion of the communication signal propagating in soft tissues with different acoustic properties is investigated. Simulated experiments revealed that, unlike linear channels, bit error rates increase as signal amplitude and bandwidth increase. Linear and decision feedback equalizers fail to address the increased error rates. When tissue properties and transmission parameters can be estimated, receivers based on maximum likelihood sequence estimation approach the performance of an ideal receiver in an ideal additive white Gaussian noise channel.

Site Specific Relationships between COVID-19 Cases and SARS-CoV-2 Viral Load in Wastewater Treatment Plant Influent.

Wastewater based epidemiology (WBE) has become an important tool during the COVID-19 pandemic, however the relationship between SARS-CoV-2 RNA in wastewater treatment plant influent (WWTP) and cases in the community is not well-defined. We report here the development of a national WBE program across 28 WWTPs serving 50% of the population of Scotland, including large conurbations, as well as low-density rural and remote island communities. For each WWTP catchment area, we quantified spatial and temporal relationships between SARS-CoV-2 RNA in wastewater and COVID-19 cases. Daily WWTP SARS-CoV-2 influent viral RNA load, calculated using daily influent flow rates, had the strongest correlation (ρ > 0.9) with COVID-19 cases within a catchment. As the incidence of COVID-19 cases within a community increased, a linear relationship emerged between cases and influent viral RNA load. There were significant differences between WWTPs in their capacity to predict case numbers based on influent viral RNA load, with the limit of detection ranging from 25 cases for larger plants to a single case in smaller plants. SARS-CoV-2 viral RNA load can be used to predict the number of cases detected in the WWTP catchment area, with a clear statistically significant relationship observed above site-specific case thresholds.

Modeling the effects of dynamic range compression on signals in noise.

Hearing aids use dynamic range compression (DRC), a form of automatic gain control, to make quiet sounds louder and loud sounds quieter. Compression can improve listening comfort, but it can also cause unwanted distortion in noisy environments. It has been widely reported that DRC performs poorly in noise, but there has been little mathematical analysis of these noise-induced distortion effects. This work introduces a mathematical model to study the behavior of DRC in noise. By making simplifying assumptions about the signal envelopes, we define an effective compression function that models the compression applied to one signal in the presence of another. Using the properties of concave functions, we prove results about DRC that have been previously observed experimentally: that the effective compression applied to each sound in a mixture is weaker than it would have been for the signal alone; that uncorrelated signal envelopes become negatively correlated when compressed as a mixture; and that compression can reduce the long-term signal-to-noise ratio in certain conditions. These theoretical results are supported by software experiments using recorded speech signals.

Acoustic effects of medical, cloth, and transparent face masks on speech signals.

Face masks muffle speech and make communication more difficult, especially for people with hearing loss. This study examines the acoustic attenuation caused by different face masks, including medical, cloth, and transparent masks, using a head-shaped loudspeaker and a live human talker. The results suggest that all masks attenuate frequencies above 1 kHz, that attenuation is greatest in front of the talker, and that there is substantial variation between mask types, especially cloth masks with different materials and weaves. Transparent masks have poor acoustic performance compared to both medical and cloth masks. Most masks have little effect on lapel microphones, suggesting that existing sound reinforcement and assistive listening systems may be effective for verbal communication with masks.

Do storm overflows influence AMR in the environment and is this relevant to human health? A UK perspective on a global issue.

Antimicrobial resistance (AMR) is a global public health threat, and the environment has been identified as an important reservoir for resistant microorganisms and genes. Storm overflows (SOs) discharge wastewater and stormwater, and are found throughout many wastewater networks. While there are no data currently showing the impact of SOs on the environment with respect to AMR in the UK, there is a small but growing body of evidence globally highlighting the potential role of SOs on environmental AMR. This review aims to provide an overview of the current state of SOs, describe global data investigating the impact of SOs on environmental AMR, and discuss the implications of SOs regarding AMR and human health. In addition, the complexities of studying the effects of SOs are discussed and a set of priority research questions and policy interventions to tackle a potentially emerging threat to public health are presented.

Comparative assessment of Nanotrap and polyethylene glycol-based virus concentration in wastewater samples.

Wastewater-based epidemiology is now widely used in many countries for the routine monitoring of SARS-CoV-2 and other viruses at a community level. However, efficient sample processing technologies are still under investigation. In this study, we compared the performance of the novel Nanotrap® Microbiome Particles (NMP) concentration method to the commonly used polyethylene glycol (PEG) precipitation method for concentrating viruses from wastewater and their subsequent quantification and sequencing. For this, we first spiked wastewater with SARS-CoV-2, influenza and measles viruses and norovirus and found that the NMP method recovered 0.4%-21% of them depending on virus type, providing consistent and reproducible results. Using the NMP and PEG methods, we monitored SARS-CoV-2, influenza A and B viruses, RSV, enteroviruses and norovirus GI and GII and crAssphage in wastewater using quantitative PCR (qPCR)-based methods and next-generation sequencing. Good viral recoveries were observed for highly abundant viruses using both methods; however, PEG precipitation was more successful in the recovery of low-abundance viruses present in wastewater. Furthermore, samples processed with PEG precipitation were more successfully sequenced for SARS-CoV-2 than those processed with the NMP method. Virus recoveries were enhanced by high sample volumes when PEG precipitation was applied. Overall, our results suggest that the NMP concentration method is a rapid and easy virus concentration method for viral targets that are abundant in wastewater, whereas PEG precipitation may be more suited to the recovery and analysis of low-abundance viruses and for next generation sequencing.

Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission.

This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place.

Complete genome sequence of mcr-9 containing Leclercia adecarboxylata.

Here, we provide the genome sequence of a Leclercia adecarboxylata isolated from a screen of an environmental bacterial isolate library for resistance to the plant flavonoid berberine. We detected the colistin resistance gene mcr-9, located on an IncFII(pECLA) plasmid.

Diurnal changes in pathogenic and indicator virus concentrations in wastewater.

Wastewater-based epidemiology (WBE) has been commonly used for monitoring SARS-CoV-2 outbreaks. As sampling times and methods (i.e. grab vs composite) may vary, diurnal changes of viral concentrations in sewage should be better understood. In this study, we collected untreated wastewater samples hourly for 4 days at two wastewater treatment plants in Wales to establish diurnal patterns in virus concentrations and the physico-chemical properties of the water. Simultaneously, we also trialled three absorbent materials as passive samples as a simple and cost-efficient alternative for the collection of composite samples. Ninety-six percent of all liquid samples (n = 74) and 88% of the passive samplers (n = 59) were positive for SARS-CoV-2, whereas 87% and 97% of the liquid and passive samples were positive for the faecal indicator virus crAssphage, respectively. We found no significant daily variations in the concentration of the target viruses, ammonium and orthophosphate, and the pH and electrical conductivity levels were also stable. Weak positive correlations were found between some physico-chemical properties and viral concentrations. More variation was observed in samples taken from the influent stream as opposed to those taken from the influent tank. Of the absorbent materials trialled as passive samples, we found that tampons provided higher viral recoveries than electronegative filter paper and cotton gauze swabs. For all materials tested, viral recovery was dependent on the virus type. Our results indicate that grab samples may provide representative alternatives to 24-h composite samples if taken from the influent tank, hence reducing the costs of sampling for WBE programmes. Tampons are also viable alternatives for cost-efficient sampling; however, viral recovery should be optimised prior to use.

Citizen science reveals landscape-scale exposures to multiazole-resistant Aspergillus fumigatus bioaerosols.

Using a citizen science approach, we identify a country-wide exposure to aerosolized spores of a human fungal pathogen, Aspergillus fumigatus, that has acquired resistance to the agricultural fungicide tebuconazole and first-line azole clinical antifungal drugs. Genomic analysis shows no distinction between resistant genotypes found in the environment and in patients, indicating that at least 40% of azole-resistant A. fumigatus infections are acquired from environmental exposures. Hotspots and coldspots of aerosolized azole-resistant spores were not stable between seasonal sampling periods. This suggests a high degree of atmospheric mixing resulting in an estimated per capita cumulative annual exposure of 21 days (±2.6). Because of the ubiquity of this measured exposure, it is imperative that we determine sources of azole-resistant A. fumigatus to reduce treatment failure in patients with aspergillosis.

Investigating One Health risks for human colonisation with extended spectrum ß-lactamase-producing Escherichia coli and Klebsiella pneumoniae in Malawian households: a longitudinal cohort study.

BACKGROUND: Low-income countries have high morbidity and mortality from drug-resistant infections, especially from enteric bacteria such as Escherichia coli. In these settings, sanitation infrastructure is of variable and often inadequate quality, creating risks of extended-spectrum ß-lactamase (ESBL)-producing Enterobacterales transmission. We aimed to describe the prevalence, distribution, and risks of ESBL-producing Enterobacterales colonisation in sub-Saharan Africa using a One Health approach. METHODS: Between April 29, 2019, and Dec 3, 2020, we recruited 300 households in Malawi for this longitudinal cohort study: 100 each in urban, peri-urban, and rural settings. All households underwent a baseline visit and 195 were selected for longitudinal follow-up, comprising up to three additional visits over a 6 month period. Data on human health, antibiotic usage, health-seeking behaviours, structural and behavioural environmental health practices, and animal husbandry were captured alongside human, animal, and environmental samples. Microbiological processing determined the presence of ESBL-producing E coli and Klebsiella pneumoniae, and hierarchical logistic regression was performed to evaluate the risks of human ESBL-producing Enterobacterales colonisation. FINDINGS: A paucity of environmental health infrastructure and materials for safe sanitation was identified across all sites. A total of 11 975 samples were cultured, and ESBL-producing Enterobacterales were isolated from 1190 (41·8%) of 2845 samples of human stool, 290 (29·8%) of 973 samples of animal stool, 339 (66·2%) of 512 samples of river water, and 138 (46·0%) of 300 samples of drain water. Multivariable models illustrated that human ESBL-producing E coli colonisation was associated with the wet season (adjusted odds ratio 1·66, 95% credible interval 1·38-2·00), living in urban areas (2·01, 1·26-3·24), advanced age (1·14, 1·05-1·25), and living in households where animals were observed interacting with food (1·62, 1·17-2·28) or kept inside (1·58, 1·00-2·43). Human ESBL-producing K pneumoniae colonisation was associated with the wet season (2·12, 1·63-2·76). INTERPRETATION: There are extremely high levels of ESBL-producing Enterobacterales colonisation in humans and animals and extensive contamination of the wider environment in southern Malawi. Urbanisation and seasonality are key risks for ESBL-producing Enterobacterales colonisation, probably reflecting environmental drivers. Without adequate efforts to improve environmental health, ESBL-producing Enterobacterales transmission is likely to persist in this setting. FUNDING: Medical Research Council, National Institute for Health and Care Research, and Wellcome Trust. TRANSLATION: For the Chichewa translation of the abstract see Supplementary Materials section.

Wastewater monitoring for detection of public health markers during the COVID-19 pandemic: Near-source monitoring of schools in England over an academic year.

BACKGROUND: Schools are high-risk settings for infectious disease transmission. Wastewater monitoring for infectious diseases has been used to identify and mitigate outbreaks in many near-source settings during the COVID-19 pandemic, including universities and hospitals but less is known about the technology when applied for school health protection. This study aimed to implement a wastewater surveillance system to detect SARS-CoV-2 and other public health markers from wastewater in schools in England. METHODS: A total of 855 wastewater samples were collected from 16 schools (10 primary, 5 secondary and 1 post-16 and further education) over 10 months of school term time. Wastewater was analysed for SARS-CoV-2 genomic copies of N1 and E genes by RT-qPCR. A subset of wastewater samples was sent for genomic sequencing, enabling determination of the presence of SARS-CoV-2 and emergence of variant(s) contributing to COVID-19 infections within schools. In total, >280 microbial pathogens and >1200 AMR genes were screened using RT-qPCR and metagenomics to consider the utility of these additional targets to further inform on health threats within the schools. RESULTS: We report on wastewater-based surveillance for COVID-19 within English primary, secondary and further education schools over a full academic year (October 2020 to July 2021). The highest positivity rate (80.4%) was observed in the week commencing 30th November 2020 during the emergence of the Alpha variant, indicating most schools contained people who were shedding the virus. There was high SARS-CoV-2 amplicon concentration (up to 9.2x106 GC/L) detected over the summer term (8th June - 6th July 2021) during Delta variant prevalence. The summer increase of SARS-CoV-2 in school wastewater was reflected in age-specific clinical COVID-19 cases. Alpha variant and Delta variant were identified in the wastewater by sequencing of samples collected from December to March and June to July, respectively. Lead/lag analysis between SARS-CoV-2 concentrations in school and WWTP data sets show a maximum correlation between the two-time series when school data are lagged by two weeks. Furthermore, wastewater sample enrichment coupled with metagenomic sequencing and rapid informatics enabled the detection of other clinically relevant viral and bacterial pathogens and AMR. CONCLUSIONS: Passive wastewater monitoring surveillance in schools can identify cases of COVID-19. Samples can be sequenced to monitor for emerging and current variants of concern at the resolution of school catchments. Wastewater based monitoring for SARS-CoV-2 is a useful tool for SARS-CoV-2 passive surveillance and could be applied for case identification and containment, and mitigation in schools and other congregate settings with high risks of transmission. Wastewater monitoring enables public health authorities to develop targeted prevention and education programmes for hygiene measures within undertested communities across a broad range of use cases.

RNA-Seq of untreated wastewater to assess COVID-19 and emerging and endemic viruses for public health surveillance.

Background: The COVID-19 pandemic showcased the power of genomic sequencing to tackle the emergence and spread of infectious diseases. However, metagenomic sequencing of total microbial RNAs in wastewater has the potential to assess multiple infectious diseases simultaneously and has yet to be explored. Methods: A retrospective RNA-Seq epidemiological survey of 140 untreated composite wastewater samples was performed across urban (n = 112) and rural (n = 28) areas of Nagpur, Central India. Composite wastewater samples were prepared by pooling 422 individual grab samples collected prospectively from sewer lines of urban municipality zones and open drains of rural areas from 3rd February to 3rd April 2021, during the second COVID-19 wave in India. Samples were pre-processed and total RNA was extracted prior to genomic sequencing. Findings: This is the first study that has utilised culture and/or probe-independent unbiased RNA-Seq to examine Indian wastewater samples. Our findings reveal the detection of zoonotic viruses including chikungunya, Jingmen tick and rabies viruses, which have not previously been reported in wastewater. SARS-CoV-2 was detectable in 83 locations (59%), with stark abundance variations observed between sampling sites. Hepatitis C virus was the most frequently detected infectious virus, identified in 113 locations and co-occurring 77 times with SARS-CoV-2; and both were more abundantly detected in rural areas than urban zones. Concurrent identification of segmented virus genomic fragments of influenza A virus, norovirus, and rotavirus was observed. Geographical differences were also observed for astrovirus, saffold virus, husavirus, and aichi virus that were more prevalent in urban samples, while the zoonotic viruses chikungunya and rabies, were more abundant in rural environments. Interpretation: RNA-Seq can effectively detect multiple infectious diseases simultaneously, facilitating geographical and epidemiological surveys of endemic viruses that could help direct healthcare interventions against emergent and pre-existent infectious diseases as well as cost-effectively and qualitatively characterising the health status of the population over time. Funding: UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) grant number H54810, as supported by Research England.

Tracing the transmission of mpox through wastewater surveillance in Southeast Asia.

High population density and tourism in Southeast Asia increase the risk of mpox due to frequent interpersonal contacts. Our wastewater surveillance in six Southeast Asian countries revealed positive signals for Monkeypox virus (MPXV) DNA, indicating local transmission. This alerts clinicians and helps allocate resources like testing, vaccines and therapeutics in resource-limited countries.

COVID-19 monitoring with sparse sampling of sewered and non-sewered wastewater in urban and rural communities.

Equitable SARS-CoV-2 surveillance in low-resource communities lacking centralized sewers is critical as wastewater-based epidemiology (WBE) progresses. However, large-scale studies on SARS-CoV-2 detection in wastewater from low-and middle-income countries is limited because of economic and technical reasons. In this study, wastewater samples were collected twice a month from 186 urban and rural subdistricts in nine provinces of Thailand mostly having decentralized and non-sewered sanitation infrastructure and analyzed for SARS-CoV-2 RNA variants using allele-specific RT-qPCR. Wastewater SARS-CoV-2 RNA concentration was used to estimate the real-time incidence and time-varying effective reproduction number (Re). Results showed an increase in SARS-CoV-2 RNA concentrations in wastewater from urban and rural areas 14-20 days earlier than infected individuals were officially reported. It also showed that community/food markets were "hot spots" for infected people. This approach offers an opportunity for early detection of transmission surges, allowing preparedness and potentially mitigating significant outbreaks at both spatial and temporal scales.