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.

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.

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.

OBJECTIVE: To study the association between dietary patterns and subgingival microbiota. METHODS: Participants (n = 651) who were enrolled in the Oral Infections, Glucose Intolerance, and Insulin Resistance Study (ORIGINS) with subgingival plaque sampling (n = 890 plaques) and a dietary assessment were included. 16S rRNA gene amplicon sequences of subgingival plaque from sites with either probing depth <4 or ≥4 mm were processed separately and used to obtain α-diversity metrics (Faith, Shannon, Simpson, Observed) and taxa ratios (Red Complex to Corynebacterium [RCLR], Treponema to Corynebacterium [TCLR], and Treponema to Neisseria [TNLR]). Food frequency questionnaires (FFQs) were processed to calculate Alternate Healthy Eating Index (AHEI) and A Priori Diet Quality Score (APDQS) scores. Mixed regression models examined the mean levels of microbial metrics across quartiles of diet quality. Means ± standard errors are reported along with p-values. RESULTS: In multivariable models assessing the association between diet scores and α-diversity metrics, higher AHEI values were significantly associated with lower Faith (p-value = 0.01) and Observed (p-value = 0.04) diversity values; similar findings were observed for APDQS (p-value = 0.01, p-value = 0.04). In multivariable models assessing the association between diet scores (AHEI and APDQS) and taxa ratios (RCLR, TCLR and TNLR), as the AHEI quartile increased, all taxa ratios decreased significantly as follows: -1.06 ± 0.093 in Q1 to -1.34 ± 0.099 in Q4 (RCLR), -0.43 ± 0.077 in Q1 to -0.64 ± 0.083 in Q4 (TCLR) and -0.09 ± 0.083 in Q1 to -0.38 ± 0.089 in Q4 (TNLR), respectively. In contrast, as the APDQS quartiles increased, only TNLR decreased significantly from -0.08 ± 0.085 in Q1 to -0.34 ± 0.091 in Q4. CONCLUSION: Diets rich in fruits, vegetables, whole grains and other nutritionally rich plant foods are associated with lower oral microbial diversity and favourable ratios of pathogenic to commensal microbiota.

Citizen science video games are designed primarily for users already inclined to contribute to science, which severely limits their accessibility for an estimated community of 3 billion gamers worldwide. We created Borderlands Science (BLS), a citizen science activity that is seamlessly integrated within a popular commercial video game played by tens of millions of gamers. This integration is facilitated by a novel game-first design of citizen science games, in which the game design aspect has the highest priority, and a suitable task is then mapped to the game design. BLS crowdsources a multiple alignment task of 1 million 16S ribosomal RNA sequences obtained from human microbiome studies. Since its initial release on 7 April 2020, over 4 million players have solved more than 135 million science puzzles, a task unsolvable by a single individual. Leveraging these results, we show that our multiple sequence alignment simultaneously improves microbial phylogeny estimations and UniFrac effect sizes compared to state-of-the-art computational methods. This achievement demonstrates that hyper-gamified scientific tasks attract massive crowds of contributors and offers invaluable resources to the scientific community.

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial dynamics, facilitates beta-diversity analysis, and enhances reproducibility by transferring learned representations to new data. In simulations, it achieves 90% accuracy in phenotype classification, significantly outperforming existing methods. In real data, TEMPTED identifies vaginal microbial markers linked to term and preterm births, demonstrating robust performance across datasets and sequencing platforms.