Biology subjects

Snead, D.

Publications and source records attributed to Snead, D..

3 recordsLinked to original sources

bioRxiv2026

A Statistical Review of Virus Reduction in Coagulation, Flocculation, and Sedimentation Treatment Processes

Coagulation, flocculation, and sedimentation (CFS) is widely applied as a combined unit process in the treatment of drinking water, wastewater, and recycled water; however, virus reduction through CFS has not been sufficiently characterized to assign pathogen log reduction value (LRV) credits. This study collected data through a systematic review that yielded over 1000 LRVs from 43 manuscripts covering 46 viruses to characterize virus reduction through CFS. The results demonstrate that CFS is effective at reducing viruses, with 68% of virus LRVs greater than 1. A mixed-effects model was used to identify potential mechanisms of virus reduction with ferric and aluminum coagulants, as well as factors associated with variability in performance. Key insights from the model show that virus reduction is: (1) improved at lower pH, similar to natural organic matter (NOM) reduction, (2) lower in secondary effluent than surface water for drinking water treatment, (3) virus-dependent, and (4) dependent on virus enumeration methods, with lower LRVs observed for molecular techniques. These findings demonstrate the potential for CFS to provide consistent and explainable virus reduction, potentially establishing a foundation for regulatory crediting in potable reuse applications. Future crediting frameworks will need to account for the factors impacting performance to accurately quantify and assign credit for virus reduction.

microbiology↗

bioRxiv2023

Kairos infers in situ horizontal gene transfer in longitudinally sampled microbiomes through microdiversity-aware sequence analysis

Horizontal gene transfer (HGT) occurring within microbiomes is linked to complex environmental and ecological dynamics that are challenging to replicate in controlled settings. Consequently, most extant studies of microbiome HGT are either simplistic experimental settings with tenuous relevance to real microbiomes or correlative studies that assume that HGT potential is a function of the relative abundance of mobile genetic elements (MGEs), the vehicles of HGT. Here we introduce Kairos as a bioinformatic tool deployed in nextflow for detecting HGT events "in situ," i.e., within a microbiome, through analysis of time-series metagenomic sequencing data. The in-situ framework proposed here leverages available metagenomic data from a longitudinally sampled microbiome to assess whether the chronological occurrence of potential donors, recipients, and putatively transferred regions could plausibly have arisen due to HGT over a range of defined time periods. The centerpiece of the Kairos workflow is a novel competitive read alignment method that enables discernment of even very similar genomic sequences, such as those produced by MGE-associated recombination. A key advantage of Kairos is its reliance on assemblies rather than metagenome assembled genomes (MAGs), which avoids systematic exclusion of accessory genes associated with the binning process. In an example test-case of real world data, use of assemblies directly produced a 264-fold increase in the number of antibiotic resistance genes included in the analysis of HGT compared to analysis of MAGs with MetaCHIP. Further, in silico evaluation of contig taxonomy was performed to assess the accuracy of classification for both chromosomally- and MGE-derived sequences, indicating a high degree of accuracy even for conjugative plasmids up to the level of class or order. Thus, Kairos enables the analysis of very recent HGT events, making it suitable for studying rapid prokaryotic adaptation in environmental systems without disturbing the ornate ecological dynamics associated with microbiomes. Current versions of the Kairos workflow are available here: https://github.com/clb21565/kairos.

bioinformatics↗

bioRxiv2020

Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling

One of the challenges in the current COVID-19 crisis is the time and cost of performing tests especially for large-scale population surveillance. Since, the probability of testing positive in large population studies is expected to be small (<15%), therefore, most of the test outcomes will be negative. Here, we propose the use of agglomerative sampling which can prune out multiple negative cases in a single test by intelligently combining samples from different individuals. The proposed scheme builds on the assumption that samples from the population may not be independent of each other. Our simulation results show that the proposed sampling strategy can significantly increase testing capacity under resource constraints: on average, a saving of ~40% tests can be expected assuming a positive test probability of 10% across the given samples. The proposed scheme can also be used in conjunction with heuristic or Machine Learning guided clustering for improving the efficiency of large-scale testing further. The code for generating the simulation results for this work is available here: https://github.com/foxtrotmike/AS.

bioinformatics↗