Department of Computer Science & Engineering

INTRODUCTION: In long-term care (LTC) facilities, nurses play a key role in detecting changes in residents health conditions and preventing avoidable emergency transfers and hospitalisations through multidisciplinary collaboration. This study aimed to explore how nurses detect changes that indicate the deterioration in LTC residents conditions. METHODS: Semi-structured interviews were conducted with 23 nurses from 14 LTC facilities. Data from these interviews were qualitatively analysed using coding and constant comparison methods. RESULTS: The three main categories were preparing, assessing and judging. Nurses worked closely with care workers who spent a considerable amount of time with the residents, and by sharing information, the nurses could rapidly respond to changes in the residents conditions. They also evaluated the risk of residents experiencing changes by leveraging their clinical experience. CONCLUSION: This study found that LTC nurses should collaborate with care workers to enhance their health assessment skills, enabling them to detect changes in residents conditions. Findings from this study can be used to promote collaboration between nurses and care workers and to develop effective educational interventions to improve nursing practice in LTC facilities. IMPLICATIONS FOR PRACTICE: This study underscores the necessity of nurses ability to identify early deterioration in LTC residents. The findings reveal key symptoms and warning signs that nurses should prioritize in their assessments. By leveraging clinical experience and close observation, nurses can make timely and effective decisions to address residents health changes, preventing further deterioration and enhancing their quality of life.

Understanding the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the outset of the coronavirus disease 2019 pandemic can provide insight into the circumstances leading to its emergence. Early SARS-CoV-2 genomic diversity has been classified into two distinct viral lineages, denoted "A" and "B," which we hypothesized were separately introduced into humans. Recently published data contain two genomes with a haplotype suggested to be an evolutionary intermediate to these two lineages, known as "T/T." We used a phylodynamic approach to analyze SARS-CoV-2 genomes from early 2020 to determine whether these two T/T genomes represent an evolutionarily intermediate haplotype between lineages A and B, or if they are a later descendent of either of these two lineages. We find that these two recently published T/T genomes do not represent an evolutionarily intermediate haplotype and were, instead, derived from either lineage A or lineage B. However, we cannot conclusively determine from which lineage they were derived. After including additional data from the start of the pandemic, including these two T/T genomes, we again find a discrepancy in the molecular clock when inferring the ancestral haplotype of SARS-CoV-2, corroborating existing evidence for the separate introductions of SARS-CoV-2 lineages A and B into the human population in late 2019.

Untargeted metabolomics often produce large datasets with missing values. These missing values are derived from biological or technical factors and can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression, are commonly used, but their effects vary depending on the type of missing data, e.g., Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation, which affected the interpretation of the results. Our results suggest imputation should be used with extreme caution even with modest levels of missing data and especially when the type of missingness is unknown.

BACKGROUND: Host DNA depletion is a critical tool for accessing the microbiomes of samples that have a small amount of microbial DNA contained in a high host background. Of critical practical importance is the ability to identify microbial DNA sequences in frozen tissue specimens. Here, we compare four existing commercial methods and two newly introduced methods involving chromatin immunoprecipitation (ChIP) on frozen human and pig intestinal biopsies. RESULTS: We find that all methods that rely on differential lysis of host and microbial cells introduce substantial biases as assessed by 16 S rRNA gene amplicon profiling. However, ChIP enables 10-fold enrichment of microbial DNA while introducing less bias, sufficient to make assessment possible against background, in both pigs and humans. CONCLUSIONS: We recommend ChIP in situations where host depletion is important but where minimizing taxonomic bias is essential, and the MolYsis or Zymo kit for situations where host depletion level is more important than taxonomic bias. CONCLUSIONS: We recommend ChIP in situations where host depletion is important but where minimizing taxonomic bias is essential, and the MolYsis or Zymo kit for situations where host depletion level is more important than taxonomic bias.

Oncogene amplification is a key driver of cancer pathogenesis. Both breakage fusion bridge (BFB) cycles and extrachromosomal DNA (ecDNA) can lead to high oncogene copy numbers, but the impact of BFB amplifications on intratumoral heterogeneity, treatment response, and patient survival remains poorly understood due to detection challenges with DNA sequencing. We introduce an algorithm, OM2BFB, designed to detect and reconstruct BFB amplifications using optical genome mapping (OGM). OM2BFB demonstrates high precision (>93%) and recall (92%) in identifying BFB amplifications across cancer cell lines, patient-derived xenograft models, and primary tumors. Comparisons using OGM reveal that BFB detection with our AmpliconSuite toolkit for short-read sequencing also achieves high precision, though with reduced sensitivity. We identify 371 BFB events through whole genome sequencing of 2557 primary tumors and cancer cell lines. BFB amplifications are prevalent in cervical, head and neck, lung, and esophageal cancers, but rare in brain cancers. Genes amplified through BFB exhibit lower expression variance, with limited potential for regulatory adaptation compared to ecDNA-amplified genes. Tumors with BFB amplifications (BFB(+)) show reduced structural heterogeneity in amplicons and delayed resistance onset relative to ecDNA(+) tumors. These findings highlight ecDNA and BFB amplifications as distinct oncogene amplification mechanisms with differing biological characteristics, suggesting distinct avenues for therapeutic intervention.

MOTIVATION: The emergence of the telomere-to-telomere genomics brought the challenge of identifying segmental duplications (SDs) in complete genomes. It further opened a possibility for identifying the differences in SDs across individual human genomes and studying the SD evolution. These newly emerged challenges require algorithms for reconstructing SDs in the most complex genomic regions that evaded all previous attempts to analyze their architecture, such as rapidly-evolving immunoglobulin loci. RESULTS: We describe the GenomeDecoder algorithm for inferring SDs and apply it to analyzing genomic architectures of various loci in primate genomes. Our analysis revealed that multiple duplications/deletions led to a rapid birth/death of immunoglobulin genes within the human population and large changes in genomic architecture of immunoglobulin loci across primate genomes. Comparison of immunoglobulin loci across primate genomes suggests that they are subjected to diversifying selection. AVAILABILITY AND IMPLEMENTATION: GenomeDecoder is available at https://github.com/ZhangZhenmiao/GenomeDecoder. The software version and test data used in this paper is uploaded to https://doi.org/10.5281/zenodo.14753844. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Our study harnesses a CRISPR-based method to examine ecDNA biogenesis, uncovering efficient circularization between double-strand breaks. ecDNAs and their corresponding chromosomal scars can form via nonhomologous end joining or microhomology-mediated end joining, but the ecDNA and scar formation processes are distinct. Based on our findings, we establish a mechanistic model of excisional ecDNA formation.

BACKGROUND: Alzheimers disease (AD) is a major neurodegenerative disorder with significant environmental factors, including diet and lifestyle, influencing its onset and progression. Although previous studies have suggested that certain diets may reduce the incidence of AD, the underlying mechanisms remain unclear. METHOD: In this post-hoc analysis of a randomized crossover study of 20 elderly adults, we investigated the effects of a modified Mediterranean ketogenic diet (MMKD) on the plasma lipidome in the context of AD biomarkers, analyzing 784 lipid species across 47 classes using a targeted lipidomics platform. RESULTS: Here we identified substantial changes in response to MMKD intervention, aside from metabolic changes associated with a ketogenic diet, we identified a a global elevation across all plasmanyl and plasmenyl ether lipid species, with many changes linked to clinical and biochemical markers of AD. We further validated our findings by leveraging our prior clinical studies into lipid related changeswith AD (n = 1912), and found that the lipidomic signature with MMKD was inversely associated with the lipidomic signature of prevalent and incident AD. CONCLUSIONS: Intervention with a MMKD was able to alter the plasma lipidome in ways that contrast with AD-associated patterns. Given its low risk and cost, MMKD could be a promising approach for prevention or early symptomatic treatment of AD.

We present Generic Refinement Types: a way to write modular higher-order specifications that abstract invariants over function contracts, while preserving automatic SMT-decidable verification. We show how generic refinements let us write a variety of modular higher-order specifications, including specifications for Rust's traits which abstract over the concrete refinements that hold for different trait implementations. We formalize generic refinements in a core calculus and show how to synthesize the generic instantiations algorithmically at usage sites via a combination of syntactic unification and constraint solving. We give semantics to generic refinements via the intuition that they correspond to ghost parameters, and we formalize this intuition via a type-preserving translation into the polymorphic contract calculus to establish the soundness of generic refinements. Finally, we evaluate generic refinements by implementing them in Flux and using it for two case studies. First, we show how generic refinements let us write modular specifications for Rust's vector indexing API that lets us statically verify the bounds safety of a variety of vector-manipulating benchmarks from the literature. Second, we use generic refinements to refine Rust's Diesel ORM library to track the semantics of the database queries issued by client applications, and hence, statically enforce data-dependent access-control policies in several database-backed web applications.