Estimating sewage flow rate in Jefferson County, Kentucky, using machine learning for wastewater-based epidemiology applications

Direct measurement of the flow rate in sanitary sewer lines is not always feasible and is an important parameter for the normalization of data used in wastewater-based epidemiology applications. Machine learning to estimate past wastewater influent flow rates supporting public health applications has not been studied. The aim of this study was to assess wastewater treatment plant influent flow rates when compared with weather data and to retrospectively estimate flow rates in Louisville, Kentucky (USA), based on other data types using machine learning. A random forest model was trained using a range of variables, such as feces-related indicators, weather data that could be associated with dilution in sewage systems, and area demographics. The developed algorithm successfully estimated the flow rate with an accuracy of 91.7%, although it did not perform as well with short-term (1-day) high flow rates. This study suggests using variables such as precipitation (mm/day) and population size are more important for wastewater flow estimation. The fecal indicator concentration (cross-assembly phage and pepper mild mottle virus) was less important. Our study challenges currently accepted opinions by showing the important public health potential application of artificial intelligence in wastewater treatment plant flow rate estimation for wastewater-based epidemiological applications

Generative AI as a Tool for Environmental Health Research Translation

One valuable application for generative artificial intelligence (AI) is summarizing research studies for non-academic readers. We submitted five articles to Chat Generative Pre-trained Transformer (ChatGPT) for summarization, and asked the article\u27s author to rate the summaries. Higher ratings were assigned to more insight-oriented activities, such as the production of eighth-grade reading level summaries, and summaries highlighting the most important findings and real-world applications. The general summary request was rated lower. For the field of environmental health science, no-cost AI technology such as ChatGPT holds the promise to improve research translation, but it must continue to be improved (or improve itself) from its current capabilit

Nationwide public perceptions regarding the acceptance of using wastewater for community health monitoring in the United States

To assess the levels of infection across communities during the coronavirus disease 2019 pandemic, researchers have measured severe acute respiratory syndrome coronavirus 2 RNA in feces dissolved in sewer water. This activity is colloquially known as sewer monitoring and is referred to as wastewater-based epidemiology in academic settings. Although global ethical principles have been described, sewer monitoring is unregulated for health privacy protection when used for public health surveillance in the United States. This study used Qualtrics XM, a national research panel provider, to recruit participants to answer an online survey. Respondents (N = 3,083) answered questions about their knowledge, perceptions of what is to be monitored, where monitoring should occur, and privacy concerns related to sewer monitoring as a public health surveillance tool. Furthermore, a privacy attitude questionnaire was used to assess the general privacy boundaries of respondents. Participants were more likely to support monitoring for diseases (92%), environmental toxins (92%), and terrorist threats (88%; e.g., anthrax). Two-third of the respondents endorsed no prohibition on location sampling scale (e.g., monitoring single residence to entire community was acceptable); the most common location category respondents wanted to prohibit sampling was at personal residences. Sewer monitoring is an emerging technology, and our study sheds light on perceptions that could benefit from educational programs in areas where public acceptance is comparatively lower. Respondents clearly communicated guard rails for sewer monitoring, and public opinion should inform future policy, application, and regulation measures

A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing

The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature. We used natural language processing (NLP) methods to automatically extract material property data from the abstracts of polymer literature. As a component of our pipeline, we trained MaterialsBERT, a language model, using 2.4 million materials science abstracts, which outperforms other baseline models in three out of five named entity recognition datasets when used as the encoder for text. Using this pipeline, we obtained ~300,000 material property records from ~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse range of applications such as fuel cells, supercapacitors, and polymer solar cells to recover non-trivial insights. The data extracted through our pipeline is made available through a web platform at https://polymerscholar.org which can be used to locate material property data recorded in abstracts conveniently. This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with a complete set of extracted material property information

Public awareness and support for use of wastewater for SARS-CoV-2 monitoring: A community survey in Louisville, Kentucky

The majority of sewer systems in the United States and other countries, are operated by public utilities. In the absence of any regulation, public perception of monitoring wastewater for population health biomarkers is an important consideration for a public utility commission when allocating resources for this purpose. In August 2021, we conducted a survey as part of an ongoing COVID-19 community prevalence study in Louisville/Jefferson County, KY. The survey comprised of seven questions about awareness of and privacy concerns and was sent to 32,000 households randomly distributed within the county. A total of 1,220 sampled adults participated in the probability sample, and 981 were used in analysis. A total of 2,444 adults additionally responded in the convenience sample, and 1,751 were used in analysis. The samples were weighted to produce estimates representative of all adults in the county. Public awareness of tracking COVID-19 virus in the sewers was low. Opinions about how data from this activity are shared strongly supported public disclosure of monitoring results. Responses showed more support for measuring the largest areas (\u3e30,000 to 50,000 households) typically representing population levels found in a community or regional wastewater treatment plant. Those who had a history of COVID-19 infection were more likely to support highly localized monitoring. Understanding wastewater surveillance strategies and thresholds of privacy concerns requires in-depth, comprehensive analysis of public opinion for continued success and efficacy of public health monitoring

Epithelial reticulon 4B (Nogo-B) is an endogenous regulator of Th2-driven lung inflammation

The reticulon protein Nogo-B is highly expressed in the lungs, and its loss augments lung inflammation in part as a result of decreased expression of the antiinflammatory protein PLUNC

Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance

SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of meta-information to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what meta-information should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting

Changes in appetite, energy intake, body composition and circulating ghrelin constituents during an incremental trekking ascent to high altitude

Purpose Circulating acylated ghrelin concentrations are associated with altitude-induced anorexia in laboratory environments, but have never been measured at terrestrial altitude. This study examined time course changes in appetite, energy intake, body composition, and ghrelin constituents during a high-altitude trek. Methods Twelve participants [age: 28(4) years, BMI 23.0(2.1) kg m−2] completed a 14-day trek in the Himalayas. Energy intake, appetite perceptions, body composition, and circulating acylated, des-acylated, and total ghrelin concentrations were assessed at baseline (113 m, 12 days prior to departure) and at three fixed research camps during the trek (3619 m, day 7; 4600 m, day 10; 5140 m, day 12). Results Relative to baseline, energy intake was lower at 3619 m (P = 0.038) and 5140 m (P = 0.016) and tended to be lower at 4600 m (P = 0.056). Appetite perceptions were lower at 5140 m (P = 0.027) compared with baseline. Acylated ghrelin concentrations were lower at 3619 m (P = 0.046) and 4600 m (P = 0.038), and tended to be lower at 5140 m (P = 0.070), compared with baseline. Des-acylated ghrelin concentrations did not significantly change during the trek (P = 0.177). Total ghrelin concentrations decreased from baseline to 4600 m (P = 0.045). Skinfold thickness was lower at all points during the trek compared with baseline (P ≤ 0.001) and calf girth decreased incrementally during the trek (P = 0.010). Conclusions Changes in plasma acylated and total ghrelin concentrations may contribute to the suppression of appetite and energy intake at altitude, but differences in the time course of these responses suggest that additional factors are also involved. Interventions are required to maintain appetite and energy balance during trekking at terrestrial altitudes

Consensus Recommendations for Clinical Outcome Assessments and Registry Development in Ataxias: Ataxia Global Initiative (AGI) Working Group Expert Guidance

To accelerate and facilitate clinical trials, the Ataxia Global Initiative (AGI) was established as a worldwide research platform for trial readiness in ataxias. One of AGI's major goals is the harmonization and standardization of outcome assessments. Clinical outcome assessments (COAs) that describe or reflect how a patient feels or functions are indispensable for clinical trials, but similarly important for observational studies and in routine patient care. The AGI working group on COAs has defined a set of data including a graded catalog of COAs that are recommended as a standard for future assessment and sharing of clinical data and joint clinical studies. Two datasets were defined: a mandatory dataset (minimal dataset) that can ideally be obtained during a routine clinical consultation and a more demanding extended dataset that is useful for research purposes. In the future, the currently most widely used clinician-reported outcome measure (ClinRO) in ataxia, the scale for the assessment and rating of ataxia (SARA), should be developed into a generally accepted instrument that can be used in upcoming clinical trials. Furthermore, there is an urgent need (i) to obtain more data on ataxia-specific, patient-reported outcome measures (PROs), (ii) to demonstrate and optimize sensitivity to change of many COAs, and (iii) to establish methods and evidence of anchoring change in COAs in patient meaningfulness, e.g., by determining patient-derived minimally meaningful thresholds of change