Milwaukee Metropolitan Sewerage District

Wastewater surveillance gained popularity as a tool supporting public health decision-making during the COVID-19 pandemic. In this study, we monitored four distinct socially vulnerable communities in Cincinnati, Ohio, by monitoring four subsewersheds using 15 upstream locations over two time periods: spring/summer (2023) and fall/winter (2023–2024). The goal of our study was to evaluate the feasibility and effectiveness of monitoring wastewater in socially and economically diverse subsewersheds. A number of 24 h composite samples were collected twice a week and analyzed for SARS-CoV-2 viral loads in the four subsewersheds and two wastewater treatment plants (WWTPs). Wastewater quality parameters (electric conductivity, pH, temperature, ORP) were also measured continuously. During the fall/winter period, increased clinical cases were correlated with high SARS-CoV-2 viral concentrations indicated by both subsewershed and WWTP monitoring. In our study, subsewershed monitoring did not provide early warning of SARS-CoV-2 levels in wastewater and cases compared to WWTP wastewater monitoring during the fall/winter period when outbreaks with higher pathogen levels often occur. This was possibly due to the proximity of the selected subsewersheds to the WWTPs. Although two socially vulnerable subsewersheds had higher SARS-CoV-2 viral concentrations in wastewater, the most vulnerable subsewershed had the lowest wastewater concentrations and the lowest number of reported cases during our study. Therefore, social vulnerability is not always the best predictor of the community COVID-19 burden since other factors may play a role in community infection, including transiency and population age distribution. This study presents some challenges and important findings from subsewershed SARS-CoV-2 wastewater monitoring during two seasons in Ohio.

The majority of sewer systems in the United States and other countries are operated by public utilities. In the absence of any regulation, the public perception of wastewater monitoring for population health biomarkers is an important consideration for a public utility commission when allocating resources for this purpose. We conducted a survey in August 2021 as part of an ongoing COVID-19 community prevalence study in Louisville/Jefferson County, KY, US. The survey comprised seven questions about wastewater awareness and privacy concerns and was sent to approximately 35 000 households randomly distributed within the county. A total of 1220 adults were involved in the probability sample, and data from 981 respondents were used in the analysis. A total of 2444 adults additionally responded to the convenience sample, and data from 1751 respondents were used in the analysis. The samples were weighted to obtain estimates representative of all adults in the county. Public awareness of tracking the virus that causes COVID-19 in sewers was low. Opinions strongly support the public disclosure of monitoring results. Responses showed that people more strongly supported measurements in the largest areas (>50 000 households), typically representing population levels found in a large community wastewater treatment plant. Those with a history of COVID-19 infection were more likely to support highly localized monitoring. Understanding wastewater surveillance strategies and privacy concern thresholds requires an in-depth and comprehensive analysis of public opinion for continued success and effective public health monitoring.

ABSTRACTMicroorganisms must respond to environmental changes to survive, often by controlling transcription initiation. Intermittent aeration during wastewater treatment presents a cyclically changing environment to which microorganisms must react. We used an intermittently aerated bioreactor performing partial nitritation and anammox (PNA) to investigate how the microbiome responds to recurring change. Meta-transcriptomic analysis revealed a dramatic disconnect between the relative DNA abundance and gene expression within the metagenome-assembled genomes (MAGs) of community members, suggesting the importance of transcriptional regulation in this microbiome. To explore how community members responded to cyclic aeration via transcriptional regulation, we searched for homologs of the catabolite repressor protein/fumarate and nitrate reductase regulatory protein (CRP/FNR) family of transcription factors (TFs) within the MAGs. Using phylogenetic analyses, evaluation of sequence conservation in important amino acid residues, and prediction of genes regulated by TFs in the MAGs, we identified homologs of the oxygen-sensing FNR in Nitrosomonas and Rhodocyclaceae, nitrogen-sensing dissimilative nitrate respiration regulator that responds to nitrogen species (DNR) in Rhodocyclaceae, and nitrogen-sensing nitrite and nitric oxide reductase regulator that responds to nitrogen species (NnrR) in Nitrospira MAGs. Our data also predict that CRP/FNR homologs in Ignavibacteria, Flavobacteriales, and Saprospiraceae MAGs sense carbon availability. In addition, a CRP/FNR homolog in a Brocadia MAG was most closely related to CRP TFs known to sense carbon sources in well-studied organisms. However, we predict that in autotrophic Brocadia, this TF most likely regulates a diverse set of functions, including a response to stress during the cyclic aerobic/anoxic conditions. Overall, this analysis allowed us to define a meta-regulon of the PNA microbiome that explains functions and interactions of the most active community members.IMPORTANCE Microbiomes are important contributors to many ecosystems, including ones where nutrient cycling is stimulated by aeration control. Optimizing cyclic aeration helps reduce energy needs and maximize microbiome performance during wastewater treatment; however, little is known about how most microbial community members respond to these alternating conditions. We defined the meta-regulon of a PNA microbiome by combining existing knowledge of how the CRP/FNR family of bacterial TFs respond to stimuli, with metatranscriptomic analyses to characterize gene expression changes during aeration cycles. Our results indicated that, for some members of the community, prior knowledge is sufficient for high-confidence assignments of TF function, whereas other community members have CRP/FNR TFs for which inferences of function are limited by lack of prior knowledge. This study provides a framework to begin elucidating meta-regulons in microbiomes, where pure cultures are not available for traditional transcriptional regulation studies. Defining the meta-regulon can help in optimizing microbiome performance.

Research calls for community health worker (CHW) integration within health systems, yet there is no agreement regarding what CHW integration is or guidance for how it can be achieved. This study examines factors associated with CHW integration in community and health care settings using a qualitative descriptive multiple-embedded case study of CHW teams at the University of Illinois at Chicago. Data were collected via semistructured interviews/document review and analyzed using thematic coding and quantitative content analysis. Factors associated with higher clinical integration included culture, communication, protocols, and training while higher community integration was associated with accessibility, relationships, and empathy.

Large collections of data help evolve deep learning into the state-of-the-art in solving many artificial intelligence problems. However, the requirements engineering (RE) community has yet to adapt to such sweeping changes caused exclusively by data. One reason is that the traditional requirements quality like unambiguity becomes less applicable to data, and so do requirements fault detection techniques like inspections. In this paper, we view deep learning as a class of machines whose effects must be evaluated with direct consideration of inherent data quality attributes: accuracy, consistency, currentness, etc. We substantiate this view by altering stationarity of the multivariate time-series data, and by further analyzing how the stationarity changes affect the behavior of a recurrent neural network in the context of predicting combined sewer overflow. Our work sheds light on the active role RE plays in deep learning.

Durante los meses de agosto de 2019 a mayo de 2020 se desarrolló un Proyecto Piloto con el objetivo de certificar la producción de mieles con el estándar de Cadena de Custodia PEFC Chile en patrimonio de las empresas Forestal Mininco SA y Forestal Arauco SA certificadas bajo el estándar de Gestión Forestal Sostenible CERTFOR/PEFC con apicultores de las comunas de Mulchén, Huépil, Cañete y Los Álamos. Esto con el objetivo adicional de dar mayor valor agregado a una producción sostenible de mieles para su comercialización en el mercado local e internacional de los apicultores de la Región del Bio Bio.

Majority of the world's mine production is made by surface mining facilities for various reasons. Use of explosives is the most economical way in loosening of the main rock where hard rock formations exist. The degree of fragmentation affects the subsequent excavation and loading operations as well as indicates the success of the blasting process. Although the most preferred method for the measurement of fragment size of blasted rock is digital image analysis, the method also includes some limitations and errors. In this study, size distribution of the blasted rock was measured directly using laser scanning-LiDAR technology and a new algorithm. The laser measurement method comes to the forefront with fast and reliable results compared with traditional measurement methods. The acquisition of the point cloud data by means of a terrestrial laser scanner from the blasted rock pile, generation of the three-dimensional model of the pile in the computer environment, surface reconstruction. and identification of the fragments forming the pile with an algorithm developed, and the determination of the size distributions of the fragments constitute the steps of the study. It has been concluded that this approach can be used effectively to determine the size distribution of fragmented rock piles in order to evaluate the blasting results.