MOTIVATION: How individuals with conditions, disabilities or abnormalities were treated gives us valuable insights into past societies. Chromosomal aneuploidies, the presence of an abnormal number of copies of the chromosomes, represent the most common large-scale chromosomal abnormalities in human populations. Chromosomal aneuploidies can affect autosomal chromosomes (e.g. Down syndrome) as well as the sex chromosomes (e.g. Klinefelter syndrome), with physical manifestations ranging from mild to severe. While simple to identify genetically, chromosomal aneuploidies are difficult to diagnose from skeletal remains alone, as they present skeletal pathologies consistent with many other conditions. RESULTS: Here we present ChASM (Chromosomal Aneuploidy Screening Methodology), a statistically rigorous Bayesian method for detecting full autosomal and sex chromosomal aneuploidies. The method leverages chromosome-wise read counts and takes into account differences in sequencing methodology, genetic coverage and condition rarity to produce posterior probability estimates for the screening of small and large databases of sequence data. AVAILABILITY AND IMPLEMENTATION: To facilitate the ease of use, ChASM has been implemented in R as the package RChASM. RChASM is available under MIT license on the Comprehensive R Archive Network.

Patient feedback is necessary to assess the extent to which healthcare delivery aligns with public needs and expectations. Surveys provide structured feedback that is readily analysed; however, they are costly, infrequent, and constrained by predefined questions, limiting a comprehensive understanding of patient experience. In contrast, the unstructured nature of online reviews and social-media posts can reveal unique insights into patient perspectives, yet that very lack of structure presents a challenge for analysis. In this study, we present a methodology for interpretable probabilistic modelling of patient emotions from patient-reported experiences. We employ metadata-network topic modelling to uncover key themes in 13,380 patient-reported experiences from Care Opinion (2012-2022) and reveal insightful relationships between these themes and labelled emotions. Our results show positivity and negativity relate most strongly to aspects of patient experience, such as patient-caregiver interactions, rather than clinical outcomes. Patient educational engagement exhibits strong positivity, whereas dismissal and rejection are linked to suicidality and depression. We develop a context-specific probabilistic emotion recommender system that predicts both multi-label emotions and binary sentiments with a Naïve Bayes classifier using topics as predictors. We assess performance with nDCG and Q-measure and achieve an F1 of 0.921, significantly outperforming standard sentiment lexicons. This methodology offers a cost-effective, timely, and transparent approach to harness unconstrained patient-reported feedback, with the potential to augment traditional patient-reported experience collection. Our R package and interactive dashboard make the approach readily accessible for future research and clinical practice applications, enabling hospitals to integrate emotional insights into surveys and tailor care to patient needs. Overall, this study provides a new avenue for understanding and improving patient experience and the quality of healthcare delivery.

Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the resulting networks is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is highly time consuming. The automatic extraction methods yield comparable results for density, centrality measures and edge weights. Our results provide evidence that automatically-extracted social networks are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. Our findings provide a framework to analyse narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other.

Study of the Internet's high-level structure has for some time intrigued scientists. The AS-graph (showing interconnections between Autonomous Systems) has been measured, studied, modelled and discussed in many papers over the last decade. However, the quality of the measurement data has always been in question. It is by now well known that most measurements of the AS-graph are missing some set of links. Many efforts have been undertaken to correct this, primarily by increasing the set of measurements, but the issue remains: how much is enough? When will we know that we have enough measurements to be sure we can see all (or almost all) of the links. This paper aims to address the problem of estimating how many links are missing from our measurements. We use techniques pioneered in biostatistics and epidemiology for estimating the size of populations (for instance of fish or disease carriers). It is rarely possible to observe entire populations, and so sampling techniques are used. We extend those techniques to the domain of the AS-graph. The key difference between our work and the biological literature is that all links are not the same, and so we build a stratified model and specify an EM algorithm for estimating its parameters. Our estimates suggest that a very significant number of links (many of thousands) are missing from standard route monitor measurements of the AS-graph. Finally, we use the model to derive the number of monitors that would be needed to see a complete AS-graph with high-probability. We estimate that 700 route monitors would see 99.9% of links.