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

Relationship between Renal Function, Fibrin Clot Properties and Lipoproteins in Anticoagulated Patients with Atrial Fibrillation

Background: Mechanisms by which chronic kidney disease (CKD) influences fibrin clot properties in atrial fibrillation (AF) remain ill-defined. We aimed to investigate the effects of AF and CKD on fibrin clot properties and lipoproteins, and determine the relationship between these factors. Methods: Prospective cross-sectional study of patients recruited from cardiology services in Liverpool between September 2019 and October 2021. Primary groups consisted of anticoagulated AF patients with and without CKD in a 1:1 ratio. Control group comprised anticoagulated patients without AF or CKD. Fibrin clot properties were analysed using turbidity and permeation assays. Detailed lipoprotein characteristics, including total cholesterol, low-density lipoprotein cholesterol (LDL-C), small dense LDL and oxidised LDL, were measured. Results: Fifty-six anticoagulated patients were enrolled (median age 72.5; 34% female); 46 with AF (23 with CKD and 23 without CKD) and 10 controls. AF was associated with changes in three indices of fibrin clot properties using PTT (T(lag) 314 vs. 358 s, p = 0.047; Abs(peak) 0.153 vs. 0.111 units, p = 0.031; T(lysis50%) 884 vs. 280 s, p = 0.047) and thrombin reagents (T(lag) 170 vs. 132 s, p = 0.031; T(max) 590 vs. 462 s, p = 0.047; T(peak50%) 406 vs. 220 s, p = 0.005) while the concomitant presence of CKD led to changes in fibrin clot properties using kaolin (T(lag) 1072 vs. 1640 s, p = 0.003; T(max) 1458 vs. 1962 s, p = 0.005; T(peak50%) 1294 vs. 2046, p = 0.008) and PPP reagents (T(lag) 566 vs. 748 s, p = 0.044). Neither of these conditions were associated with changes in fibrin clot permeability. Deteriorating eGFR was significantly correlated to the speed of clot formation, and CKD was independently associated with unfavourable clot properties (T(lag) −778, p = 0.002; T(max) −867, p = 0.004; T(peak50%) −853, p = 0.004 with kaolin reagent). AF alone was not associated with changes in lipoprotein distribution while AF patients with CKD had lower total cholesterol, LDL-C and small dense LDL due to the presence of other risk factors. No significant relationship was observed between fibrin clot properties and lipoprotein distribution. Conclusions: There are important changes that occur in fibrin clot properties with AF and CKD that may account for the increased risk of thromboembolic complications. However, these changes in fibrin clot properties were not attributable to alterations in lipoprotein distribution

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

The scalar bi-spectrum during preheating in single field inflationary models

In single field inflationary models, preheating refers to the phase that immediately follows inflation, but precedes the epoch of reheating. During this phase, the inflaton typically oscillates at the bottom of its potential and gradually transfers its energy to radiation. At the same time, the amplitude of the fields coupled to the inflaton may undergo parametric resonance and, as a consequence, explosive particle production can take place. A priori, these phenomena could lead to an amplification of the super-Hubble scale curvature perturbations which, in turn, would modify the standard inflationary predictions. However, remarkably, it has been shown that, although the Mukhanov-Sasaki variable does undergo narrow parametric instability during preheating, the amplitude of the corresponding super-Hubble curvature perturbations remain constant. Therefore, in single field models, metric preheating does not affect the power spectrum of the large scale perturbations. In this article, we investigate the corresponding effect on the scalar bi-spectrum. Using the Maldacena's formalism, we analytically show that, for modes of cosmological interest, the contributions to the scalar bi-spectrum as the curvature perturbations evolve on super-Hubble scales during preheating is completely negligible. Specifically, we illustrate that, certain terms in the third order action governing the curvature perturbations which may naively be expected to contribute significantly are exactly canceled by other contributions to the bi-spectrum. We corroborate selected analytical results by numerical investigations. We conclude with a brief discussion of the results we have obtained.Comment: v1: 15 pages, 4 figures; v2: 15 pages, 4 figures, discussion and references added, to appear in Phys. Rev.