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Abstract As a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long‐short‐term‐memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2‐step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi‐year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one‐dimensional (1‐D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R2) up to 0.6–0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2‐step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short‐ and long‐term water management plans.
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From Bioreactor to Bulk Rheology: Achieving Scalable Production of Highly Concentrated Circular DNA
Abstract DNA serves as a model system in polymer physics due to its ability to be obtained as a uniform polymer with controllable topology and non‐equilibrium behavior. Currently, a major obstacle in the widespread adoption of DNA is obtaining it on a scale and cost basis that accommodates bulk rheology and high‐throughput screening. To address this, recent advancements in bioreactor‐based plasmid DNA production is coupled with anion exchange chromatography to produce a unified approach to generating gram‐scale quantities of monodisperse DNA. With this method, 1.1 grams of DNA is obtained per batch to generate solutions with concentrations up to 116 mg mL−1of uniform supercoiled and relaxed circular plasmid DNA, which is roughly 69 times greater than the overlap concentration. The utility of this method is demonstrated by performing bulk rheology measurements on DNA of different length, topologies, and concentrations at sample volumes up to 1 mL. The measured elastic moduli are orders of magnitude larger than those previously reported for DNA and allowed for the construction of a time‐concentration superposition curve that spans twelve decades of frequency. Ultimately, these results could provide important insights into the dynamics of ring polymers and the nature of highly condensed DNA dynamics. This article is protected by copyright. All rights reserved
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- PAR ID:
- 10519353
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Advanced Materials
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/adma.202405490
