To investigate the repeatability in corneal thickness (CT) and epithelial thickness (ET) measurements using spectral domain anterior segment optical coherence tomography (AS-OCT, REVO NX, Optopol) in keratoconus, and examine the effect of corneal crosslinking (CXL) on repeatability.

A cross-sectional study of 259 eyes of 212 patients with keratoconus attending the corneal disease clinic at a university hospital tertiary referral center were enrolled. Two groups were analysed eyes with no prior history of CXL (Group A) and eyes with prior CXL (Group B). Repeatability of measurements was assessed using the intraclass correlation coefficient (ICC) and coefficient of variation (CV).

In Group A, central corneal thickness (CCT) was 472.18 ± 45.41μm, and the ET was found to be the thinnest in the inferior-temporal aspect at 51.79 ± 5.97μm and thickest at the superior-nasal aspect at 56.07 ± 5.70μm. In Group B, CCT was 465.11± 42.28μm, and the ET was the thinnest at the inferior-temporal aspect at 50.63 ± 5.52μm and thickest at the superior aspect at 56.80 ± 6.39μm. When evaluating CT measurements, ICC was above 0.86 and 0.83 for Group A and Group B respectively. When evaluating ET measurements, ICC was above 0.82 for both groups. CXL had no statistically significant impact on the repeatability of measurements.

AS-OCT provides repeatable CT and ET measurements in the central and peripheral cornea in patients with keratoconus. Repeatability is not affected by a history of CXL.

AS-OCT provides repeatable CT and ET measurements in the central and peripheral cornea in patients with keratoconus. Repeatability is not affected by a history of CXL.Influence maximisation, or how to affect the intrinsic opinion dynamics of a social group, is relevant for many applications, such as information campaigns, political competition, or marketing. Previous literature on influence maximisation has mostly explored discrete allocations of influence, i.e. optimally choosing a finite fixed number of nodes to target. Here, we study the generalised problem of continuous influence maximisation where nodes can be targeted with flexible intensity. We focus on optimal influence allocations against a passive opponent and compare the structure of the solutions in the continuous and discrete regimes. We find that, whereas hub allocations play a central role in explaining optimal allocations in the discrete regime, their explanatory power is strongly reduced in the continuous regime. Instead, we find that optimal continuous strategies are very well described by two other patterns (i) targeting the same nodes as the opponent (shadowing) and (ii) targeting direct neighbours of the opponent (shielding). Finally, we investigate the game-theoretic scenario of two active opponents and show that the unique pure Nash equilibrium is to target all nodes equally. These results expose fundamental differences in the solutions to discrete and continuous regimes and provide novel effective heuristics for continuous influence maximisation.This study examined involuntary capture of attention, overt attention, and stimulus valence and arousal ratings, all factors that can contribute to potential attentional biases to face and train objects in children with and without autism spectrum disorder (ASD). In the visual domain, faces are particularly captivating, and are thought to have a ‘special status’ in the attentional system. Infigratinib Research suggests that similar attentional biases may exist for other objects of expertise (e.g. birds for bird experts), providing support for the role of exposure in attention prioritization. Autistic individuals often have circumscribed interests around certain classes of objects, such as trains, that are related to vehicles and mechanical systems. This research aimed to determine whether this propensity in autistic individuals leads to stronger attention capture by trains, and perhaps weaker attention capture by faces, than what would be expected in non-autistic children. In Experiment 1, autistic children (6-14 years old) and age- and IQ-matched non-autistic children performed a visual search task where they manually indicated whether a target butterfly appeared amongst an array of face, train, and neutral distractors while their eye-movements were tracked. Autistic children were no less susceptible to attention capture by faces than non-autistic children. Overall, for both groups, trains captured attention more strongly than face stimuli and, trains had a larger effect on overt attention to the target stimuli, relative to face distractors. In Experiment 2, a new group of children (autistic and non-autistic) rated train stimuli as more interesting and exciting than the face stimuli, with no differences between groups. These results suggest that (1) other objects (trains) can capture attention in a similar manner as faces, in both autistic and non-autistic children (2) attention capture is driven partly by voluntary attentional processes related to personal interest or affective responses to the stimuli.Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns. Probability density curves and overimputation diagnostics were used to explore the effects of multiple imputation. The results showed that the Data Augmentation (DA) algorithm had the characteristics of high operation efficiency and good imputation effect, but the algorithm was not suitable for data imputation when there was a high data missing rate. The EMB algorithm effectively restored the distribution of datasets with different data missing rates, and was less affected by the missing position; the EMB algorithm could obtain a good imputation effect even when there was a high data missing rate. Overimputation diagnostics could not only reflect the data imputation effect, but also show the correlation between different datasets, which was of great importance for deep data mining and imputation effect improvement. The Expectation-Maximization with Bootstrap (EMB) algorithm had a poor estimation effect on extreme data and failed to reflect the dataset’s variability characteristics.