Modeling Virus Transport and Removal during Storage and Recovery in Heterogeneous Aquifers

A quantitative understanding of virus removal during aquifer storage and recovery (ASR) in physically and geochemically heterogeneous aquifers is needed to accurately assess human health risks from viral infections. A two-dimensional axisymmetric numerical model incorporating processes of virus attachment, detachment, and inactivation in aqueous and solid phases was developed to systematically evaluate the virus removal performance of ASR schemes. Physical heterogeneity was considered as either layered or randomly distributed hydraulic conductivities (with selected variance and horizontal correlation length). Geochemical heterogeneity in the aquifer was accounted for using Colloid Filtration Theory to predict the spatial distribution of attachment rate coefficient. Simulation results demonstrate that the combined effects of aquifer physical heterogeneity and spatial variability of attachment rate resulted in higher virus concentrations in the recovered water at the ASR well (i.e. reduced virus removal). While the sticking efficiency of viruses to aquifer sediments was found to significantly influence virus concentration in the recovered water, the solid phase inactivation under realistic field conditions combined with the duration of storage phase had a predominant influence on the overall virus removal. The relative importance of physical heterogeneity increased under physicochemical conditions that reduced virus removal (e.g. lower value of sticking efficiency or solid phase inactivation rate). This study provides valuable insight on site selection of ASR projects and an approach to optimize ASR operational parameters (e.g. storage time) for virus removal and to minimize costs associated with post-recovery treatment

Transport and fate of viruses in sediment and stormwater from a Managed Aquifer Recharge site

© 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (Oct 2017) in accordance with the publisher’s archiving policyEnteric viruses are one of the major concerns in water reclamation and reuse at Managed Aquifer Recharge (MAR) sites. In this study, the transport and fate of bacteriophages MS2, PRD1, and ΦX174 were studied in sediment and stormwater (SW) collected from a MAR site in Parafield, Australia. Column experiments were conducted using SW, stormwater in equilibrium with the aquifer sediment (EQ-SW), and two pore-water velocities (1 and 5 m day−1) to encompass expected behavior at the MAR site. The aquifer sediment removed >92.3% of these viruses under all of the considered MAR conditions. However, much greater virus removal (4.6 logs) occurred at the lower pore-water velocity and in EQ-SW that had a higher ionic strength and Ca2+ concentration. Virus removal was greatest for MS2, followed by PRD1, and then ΦX174 for a given physicochemical condition. The vast majority of the attached viruses were irreversibly attached or inactivated on the solid phase, and injection of Milli-Q water or beef extract at pH = 10 only mobilized a small fraction of attached viruses ( μs > kdet > μl, and katt was several orders of magnitude greater than μl. Therefore, current microbial risk assessment methods in the MAR guideline may be overly conservative in some instances. Interestingly, virus BTCs exhibited blocking behavior and the calculated solid surface area that contributed to the attachment was very small. Additional research is therefore warranted to study the potential influence of blocking on virus transport and potential implications for MAR guidelines

Automated Identification of Breast Cancer Type Using Novel Multipath Transfer Learning and Ensemble of Classifier

Breast cancer, a global health concern, requires innovative diagnostic approaches. The potential of Artificial Intelligence and Machine Learning in breast cancer diagnosis warrants exploration along with conventional methods. Our method partitions breast cancer images into four regions by, employing transfer learning using ResNet50 and VGG16 for feature extraction in each region. The extracted features are consolidated and fed into an Extra Tree Classifier. In addition, an ensemble learning framework combines logistic regression, SVM (Support Vector Machine), Extra Tree Classifier, and Ridge Classifier outputs, harnessing the strengths of each for robust breast cancer image classification. Among the five machine learning classification models (— Extra Tree Classifier, Logistic Regression, Ridge Classifier, SVM, and Voting Classifier) — the goal was to determine the most effective in terms of accuracy. Surprisingly, the Voting Classifier emerged as the top performer, with an impressive accuracy of 96.86% across these carcinoma classes, validating the effectiveness of the approach. The Extra Tree Classifier followed with an accuracy of 89.66%, whereas the Ridge Classifier trailed closely at 88.74%. Additionally, Logistic Regression exhibited a notable accuracy rate of 91.42%, and the SVM model achieved a reasonable accuracy of 91.44%. This approach integrates the feature extraction power of deep learning with the interpretability of the traditional models. The results demonstrate the efficacy of our method in classifying ductal, lobular, and papillary cancers. The proposed method offers a variety of advantages, including early-stage identification, increased precision, customized medical advice, and simplified analysis, by combining feature extraction with ensemble learning. Ongoing research aims to refine these algorithms, leading to earlier detection and improved outcomes. This innovative approach has the potential to revolutionize breast cancer care and fundamentally reshape treatment strategies

Contributions of Nanoscale Roughness to Anomalous Colloid Retention and Stability Behavior

All natural surfaces exhibit nanoscale roughness (NR) and chemical heterogeneity (CH) to some extent. Expressions were developed to determine the mean interaction energy between a colloid and a solid−water interface, as well as for colloid−colloid interactions, when both surfaces contain binary NR and CH. The influence of heterogeneity type, roughness parameters, solution ionic strength (IS), mean zeta potential, and colloid size on predicted interaction energy profiles was then investigated. The role of CH was enhanced on smooth surfaces with larger amounts of CH, especially for smaller colloids and higher IS. However, predicted interaction energy profiles were mainly dominated by NR, which tended to lower the energy barrier height and the magnitudes of both the secondary and primary minima, especially when the roughness fraction was small. This dramatically increased the relative importance of primary to secondary minima interactions on net electrostatically unfavorable surfaces, especially when roughness occurred on both surfaces and for conditions that produced small energy barriers (e.g., higher IS, lower pH, lower magnitudes in the zeta potential, and for smaller colloid sizes) on smooth surfaces. The combined influence of roughness and Born repulsion frequently produced a shallow primary minimum that was susceptible to diffusive removal by random variations in kinetic energy, even under electrostatically favorable conditions. Calculations using measured zeta potentials and hypothetical roughness properties demonstrated that roughness provided a viable alternative explanation for many experimental deviations that have previously been attributed to electrosteric repulsion (e.g., a decrease in colloid retention with an increase in solution IS; reversible colloid retention under favorable conditions; and diminished colloid retention and enhanced colloid stability due to adsorbed surfactants, polymers, and/or humic materials)