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1. Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies

   https://doi.org/10.1371/journal.pcbi.1010608  

   Gao, Yilin; Sun, Fengzhu (October 2023, PLOS Computational Biology)

   Roy, Sushmita (Ed.)

   Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier’s reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier’s prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches. 

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2. A revisit to universal single-copy genes in bacterial genomes

   https://doi.org/10.1038/s41598-022-18762-z  

   Wang, Saidi; Ventolero, Minerva; Hu, Haiyan; Li, Xiaoman (December 2022, Scientific Reports)

   Abstract Universal single-copy genes (USCGs) are widely used for species classification and taxonomic profiling. Despite many studies on USCGs, our understanding of USCGs in bacterial genomes might be out of date, especially how different the USCGs are in different studies, how well a set of USCGs can distinguish two bacterial species, whether USCGs can separate different strains of a bacterial species, to name a few. To fill the void, we studied USCGs in the most updated complete bacterial genomes. We showed that different USCG sets are quite different while coming from highly similar functional categories. We also found that although USCGs occur once in almost all bacterial genomes, each USCG does occur multiple times in certain genomes. We demonstrated that USCGs are reliable markers to distinguish different species while they cannot distinguish different strains of most bacterial species. Our study sheds new light on the usage and limitations of USCGs, which will facilitate their applications in evolutionary, phylogenomic, and metagenomic studies. 

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3. Fitness tracking reveals task-specific associations between memory, mental health, and physical activity

   https://doi.org/10.1038/s41598-022-17781-0  

   Manning, Jeremy R.; Notaro, Gina M.; Chen, Esme; Fitzpatrick, Paxton C. (August 2022, Scientific Reports)

   Abstract Physical activity can benefit both physical and mental well-being. Different forms of exercise (e.g., aerobic versus anaerobic; running versus walking, swimming, or yoga; high-intensity interval training versus endurance workouts; etc.) impact physical fitness in different ways. For example, running may substantially impact leg and heart strength but only moderately impact arm strength. We hypothesized that the mental benefits of physical activity might be similarly differentiated. We focused specifically on how different intensities of physical activity might relate to different aspects of memory and mental health. To test our hypothesis, we collected (in aggregate) roughly a century’s worth of fitness data. We then asked participants to fill out surveys asking them to self-report on different aspects of their mental health. We also asked participants to engage in a battery of memory tasks that tested their short and long term episodic, semantic, and spatial memory performance. We found that participants with similar physical activity habits and fitness profiles tended to also exhibit similar mental health and task performance profiles. These effects were task-specific in that different physical activity patterns or fitness characteristics varied with different aspects of memory, on different tasks. Taken together, these findings provide foundational work for designing physical activity interventions that target specific components of cognitive performance and mental health by leveraging low-cost fitness tracking devices. 

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4. Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs

   https://doi.org/10.1109/IEEECONF51394.2020.9443496  

   Gupta, Ananya Sen; Zhao, Zhengqiao; Rosen, Gail (November 2020, Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs)

   null (Ed.)

   Viral subtyping can facilitate visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Understanding the virus's evolution spatiotemporally can help forensic strategies. We have identified mutation variation within SARS-CoV-2 sequences via an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Using these ISMs, we show that we can use them for a variety of downstream analyses, such as comparing countries' subtype compositions. We present association graphs as a visualization tool to connect different ISMs based on their co-occurrence across different individuals. In particular, we investigate dominant ISMs for different locations, across different factors such as gender and age. 

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5. Surface-assisted self-assembly of 2D, DNA binary crystals

   https://doi.org/10.1039/d3nr01187a  

   Liu, Longfei; Mao, Dake; Li, Zhe; Zheng, Mengxi; He, Kai; Mao, Chengde (June 2023, Nanoscale)

   Surface-assisted, tile-based DNA self-assembly is a powerful method to construct large, two-dimensional (2D) nanoarrays. To further increase the structural complexity, one idea is to incorporate different types of tiles into one assembly system. However, different tiles have different adsorption strengths to the solid surface. The differential adsorptions make it difficult to control the effective molar ratio between different DNA tile concentrations on the solid surface, leading to assembly failure. Herein, we propose a solution to this problem by engineering the tiles with comparable molecular weights while maintaining their architectures. As a demonstration, we have applied this strategy to successfully assemble binary DNA 2D arrays out of very different tiles. We expect that this strategy would facilitate assembly of other complicated nanostructures as well. 

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