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Purpose: The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union. Methods: Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. Results: The isolated full model correctly classified 75% of outcomes (CI, 65%–82%), whereas the relative full model correctly classified 78% (CI, 69%–86%). Reduced respective models correctly classified 74% (CI, 65%–82%) and 76% (CI, 67%–84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant. Conclusions: Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby. [ABSTRACT FROM AUTHOR]

Despite the growing popularity of women's rugby, there is a lack of research understanding the contribution of place-kicking to match outcomes. This study aims to establish the characteristics and contribution of place-kicking to women's international Rugby Union and evaluate the performance of place-kickers while accounting for factors that contribute to kick difficulty. Data from 674 place-kicks across 80 matches were analysed. A binomial generalised linear mixed model (GLMM) was used to predict the probability of kick success. 60.5% of place-kicks were successful, and they contributed 23.9% of all points scored; conversions accounted for 16.8% and penalties 7.1%. Kick success percentages for conversions (56.9%) and penalties (78.3%) significantly differed (p < 0.01). Kick distance and angle were significant (p < 0.01) predictors of kick success and the GLMM had a prediction accuracy of 73.6%. The performance rankings of kickers changed when comparing observed and expected success, highlighting the need to consider contextual factors contributing to kick difficulty when evaluating performance. The GLMM results provide valuable insights for coaches and players to make informed decisions, for example, whether to attempt a place-kick when a penalty is awarded, by enabling predictions of place-kick success. This could enhance a team's chances of winning matches. [ABSTRACT FROM AUTHOR]

The authors investigated left–right turning preferences of n = 260 juvenile European sea bass (Dicentrarchus labrax) reared in ambient conditions and ocean acidification (OA) conditions or in ambient conditions but tested in OA water. Groups of 10 individuals were observed alone in a circular tank, and individuals' left and right turning during free‐swimming was quantified using trajectory data from the video. The authors showed that near‐future OA levels do not affect the number of turns made, or behavioural lateralization (turning preference), in juvenile D. labrax tested in groups. [ABSTRACT FROM AUTHOR]

Semiconductor quantum dot nanoparticles are in demand as optical biomarkers yet the cellular uptake process is not fully understood; quantification of numbers and the fate of internalized particles are still to be achieved. We have focussed on the characterization of cellular uptake of quantum dots using a combination of analytical electron microscopies because of the spatial resolution available to examine uptake at the nanoparticle level, using both imaging to locate particles and spectroscopy to confirm identity. In this study, commercially available quantum dots, CdSe/ZnS core/shell particles coated in peptides to target cellular uptake by endocytosis, have been investigated in terms of the agglomeration state in typical cell culture media, the traverse of particle agglomerates across U-2 OS cell membranes during endocytosis, the merging of endosomal vesicles during incubation of cells and in the correlation of imaging flow cytometry and transmission electron microscopy to measure the final nanoparticle dose internalized by the U-2 OS cells. We show that a combination of analytical transmission electron microscopy and serial block face scanning electron microscopy can provide a comprehensive description of the internalization of an initial exposure dose of nanoparticles by an endocytically active cell population and how the internalized, membrane bound nanoparticle load is processed by the cells. We present a stochastic model of an endosome merging process and show that this provides a data-driven modelling framework for the prediction of cellular uptake of engineered nanoparticles in general. [ABSTRACT FROM AUTHOR]

The delivery of nanoparticles into cells is important in therapeutic applications and in nanotoxicology. Nanoparticles are generally targeted to receptors on the surfaces of cells and internalized into endosomes by endocytosis, but the kinetics of the process and the way in which cell division redistributes the particles remain unclear. Here we show that the chance of success or failure of nanoparticle uptake and inheritance is random. Statistical analysis of nanoparticle-loaded endosomes indicates that particle capture is described by an over-dispersed Poisson probability distribution that is consistent with heterogeneous adsorption and internalization. Partitioning of nanoparticles in cell division is random and asymmetric, following a binomial distribution with mean probability of 0.52-0.72. These results show that cellular targeting of nanoparticles is inherently imprecise due to the randomness of nature at the molecular scale, and the statistical framework offers a way to predict nanoparticle dosage for therapy and for the study of nanotoxins. [ABSTRACT FROM AUTHOR]

Many applications in biomedical research require the long-term identification and tracking of cells over time. In previous work we have demonstrated that by sequentially dosing a cell population with different emission wavelength nanoparticles it is possible to use the random number of nanoparticle loaded vesicles generated by the cells as a barcode for individual cells within the population. In this paper we develop a simple model to describe the number of codes that can be generated using this sequential loading protocol. The methodology is validated by comparison with experiment and subsequently used to predict the effect of varying the number of colors used to encode the cells and also to assess the effect of misreading the cellular code due to errors in imaging the vesicles.

This chapter provides a method for quantitative single cell analysis to track the transition of single cells from G2, indicated by high cyclin B1 levels, to G1 polyploidy phase (G1(p)), indicated by low cyclin B1 levels, in a 4n population. The cell tracking methodology described provides a fluorescence fingerprint suitable for deriving G2/M or G2/G1p transitions. Notably, during late G2 the absolute cyclin B1-eGFP reporter levels obtained were high and the switch-off point identifiable, with destruction rates of a similar order across all cell cycle routing avenues. The three principle parameters extracted were defined as (1) G2-to-G1(p) transition duration (tGFP(off)); (2) rate of sensor destruction (kGFP(off)), and (3) peak sensor expression (GFP(peak)).