Research needs for optimising wastewater-based epidemiology monitoring for public health protection

This is the final version. Available on open access from IWA Publishing via the DOI in this recordData availability statement: All relevant data are included in the paper or its Supplementary Information.Wastewater-based epidemiology (WBE) is an unobtrusive method used to observe patterns in illicit drug use, poliovirus, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The pandemic and need for surveillance measures have led to the rapid acceleration of WBE research and development globally. With the infrastructure available to monitor SARS-CoV-2 from wastewater in 58 countries globally, there is potential to expand targets and applications for public health protection, such as other viral pathogens, antimicrobial resistance (AMR), pharmaceutical consumption, or exposure to chemical pollutants. Some applications have been explored in academic research but are not used to inform public health decision-making. We reflect on the current knowledge of WBE for these applications and identify barriers and opportunities for expanding beyond SARS-CoV-2. This paper critically reviews the applications of WBE for public health and identifies the important research gaps for WBE to be a useful tool in public health. It considers possible uses for pathogenic viruses, AMR, and chemicals. It summarises the current evidence on the following: (1) the presence of markers in stool and urine; (2) environmental factors influencing persistence of markers in wastewater; (3) methods for sample collection and storage; (4) prospective methods for detection and quantification; (5) reducing uncertainties; and (6) further considerations for public health use.Natural Environment Research Council (NERC)Engineering and Physical Sciences Research Council (EPSRC

Natural vocalizations in the mammalian inferior colliculus are broadly encoded by a small number of independent multi-unit clusters

How complex natural sounds are represented by the main converging center of the auditory midbrain, the central inferior colliculus, is an open question. We applied neural discrimination to determine the variation of detailed encoding of individual vocalizations across the best frequency gradient of the central inferior colliculus. The analysis was based on collective responses from several neurons. These multi-unit spike trains were recorded from guinea pigs exposed to a spectrotemporally rich set of eleven species-specific vocalizations. Spike trains of disparate units from the same recording were combined in order to investigate whether groups of multi-unit clusters represent the whole set of vocalizations more reliably than only one unit, and whether temporal response correlations between them facilitate an unambiguous neural representation of the vocalizations. We found a spatial distribution of the capability to accurately encode groups of vocalizations across the best frequency gradient. Different vocalizations are optimally discriminated at different locations of the best frequency gradient. Furthermore, groups of a few multi-unit clusters yield improved discrimination over only one multi-unit cluster between all tested vocalizations. However, temporal response correlations between units do not yield better discrimination. Our study is based on a large set of units of simultaneously recorded responses from several guinea pigs and electrode insertion positions. Our findings suggest abroadly distributed code for behaviorally relevant vocalizations in the mammalian inferior colliculus.Responses from a few non-interacting units are sufficient to faithfully represent the whole set of studied vocalizations with diverse spectrotemporal properties