Emotional salience modulates the forward flow of memory

Conditional response probability (CRP) analyses applied to free recall data indicate that recall occurs for contiguous items with forward-directionality, thought to reflect the shared encoding context of nearby items. We hypothesized that a context disruption, produced by presenting infrequent oddballs, would modulate CRP curves, increasing the forward-flow of recall due to strong binding of items presented after these oddballs to the new encoding context. Seventy young, healthy male and female participants encoded word lists containing either emotional or perceptual oddballs at varying stimulus onset asynchronies (SOA) followed by free recall. Serial recall transitions from emotional, but not perceptual, oddballs were enhanced in the forward direction except at the shortest SOA (1s). The present results provide empirical evidence of CRP modulation selectively by emotional salience and suggest that recall patterns after presenting emotional and perceptual oddballs are mediated by different mechanisms.

Spatial Resolution of Suprachoroidal–Transretinal Stimulation Estimated by Recording Single-Unit Activity From the Cat Lateral Geniculate Nucleus

Retinal prostheses are devices used to restore visual sensation in patients suffering from photoreceptor degeneration, such as retinitis pigmentosa. Suprachoroidal–transretinal stimulation (STS) is a prosthesis with retinal electrodes located in the sclera. STS has the advantage that it is safer than epiretinal or subretinal prostheses, as the implant is not directly attached to the retinal tissue. We have previously reported feasibility of STS with animal experiments and clinical trials. However, functional evaluation with neurophysiological experiments is still largely missing. To estimate the spatial resolution of STS, single-unit activities in response to STS were recorded from relay cells in the dorsal lateral geniculate nucleus of cats, and the response probability of the units was analyzed in relation to the distance between the stimulus location and the receptive field of each recorded unit. A platinum electrode was attached to the sclera after lamellar resection, and the return electrode was placed in the vitreous. The stimulating current, which ranged from 50 to 500 μA, was applied between these electrodes, and the probability of spike responses occurring just after retinal stimulation was measured. The distance at half-maximum of response was determined from the collected response probabilities as a function of stimulus intensity for all units characterized by their distances from the receptive field center to the stimulation point. As the stimulation became weaker, this distance decreased to 1.8° at 150 and 100 μA. As another estimation, the radius of 25% response probability was 1.4° at 100 μA. The diameter of the stimulated cat retinal area, 3.6° or 2.8°, corresponds to human visual acuity of 0.005 or 0.007, or finger counting. Considering the lower hazard to the retina of STS and its potentially large visual field coverage, STS is an attractive method for retinal prosthetic device development.

Individual Scores for Associative Learning in a Differential Appetitive Olfactory Paradigm Using Binary Logistic Regression Analysis

Numerous invertebrates have contributed to our understanding of the biology of learning and memory. In most cases, learning performance is documented for groups of individuals, and nearly always based on a single, typically binary, behavioural metric for a conditioned response. This is unfortunate for several reasons. Foremost, it has become increasingly apparent that invertebrates exhibit inter-individual differences in many aspects of their behaviour, and also that the conditioned response probability for an animal group does not adequately represent the behaviour of individuals in classical conditioning. Furthermore, a binary response character cannot yield a graded score for each individual. We also hypothesise that due to the complexity of a conditioned response, a single metric need not reveal an individual's full learning potential. In this paper, we report individual learning scores for freely moving adult male crickets (Gryllus bimaculatus) based on a multi-factorial analysis of a conditioned response. First, in an absolute conditioning paradigm, we video-tracked the odour responses of animals that, in previous training, received either odour plus reward (sugar water), reward alone, or odour alone to identify behavioural predictors of a conditioned response. Measures of these predictors were then analysed using binary regression analysis to construct a variety of mathematical models that give a probability for each individual that it exhibited a conditioned response (Presp). Using standard procedures to compare model accuracy, we identified the strongest model which could reliably discriminate between the different odour responses. Finally, in a differential appetitive olfactory paradigm, we employed the model after training to calculate the Presp of animals to a conditioned, and to an unconditioned odour, and from the difference a learning index for each animal. Comparing the results from our multi-factor model with a single metric analysis (head bobbing in response to a conditioned odour), revealed advantageous aspects of the model. A broad distribution of model-learning scores, with modes at low and high values, support the notion of a high degree of variation in learning capacity, which we discuss.

Environment dependent benefits of individual differences in the recruitment behaviour of honey bees

Inter-individual differences in behaviour within the members of a social group can affect the group's productivity. In eusocial insects, individual differences amongst workers in a colony play a central role division of labour and task allocation. Extensive empirical and theoretical work has highlighted variation in response thresholds as a proximate mechanism underlying individual behavioural differences and hence division of labour. However, other response parameters, like response probability and intensity, can affect these differences. In this study, we first extended a previously published agent-based model on honey bee foraging to understand the relative importance of response (dance) probability and response (dance) intensity in the task of recruitment. Comparing variation obtained from the simulations with previously published empirical data, we found that response intensity plays a more important role than probability in producing consistent inter-individual differences in recruitment behaviour. We then explored the benefits provided by this individual variation in recruitment behaviour to the colony's collective foraging effort under different environmental conditions. Our results revealed that individual variation leads to a greater energetic yield per forager, but only when food is abundant. Our study highlights the need to consider all response parameters while studying division of labour and adds to the growing body of evidence linking individual variation in behavioural responses to the success of social groups.

Goal-oriented adaptive sampling under random field modelling of response probability distributions

In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space. We consider cases where the spatial variation of these response distributions does not only concern their mean and/or variance but also other features including for instance shape or uni-modality versus multi-modality. Our contributions build upon a non-parametric Bayesian approach to modelling the thereby induced fields of probability distributions, and in particular to a spatial extension of the logistic Gaussian model. The considered models deliver probabilistic predictions of response distributions at candidate points, allowing for instance to perform (approximate) posterior simulations of probability density functions, to jointly predict multiple moments and other functionals of target distributions, as well as to quantify the impact of collecting new samples on the state of knowledge of the distribution field of interest. In particular, we introduce adaptive sampling strategies leveraging the potential of the considered random distribution field models to guide system evaluations in a goal-oriented way, with a view towards parsimoniously addressing calibration and related problems from non-linear (stochastic) inversion and global optimisation.

The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B

Although hepatitis B is widespread, it is hard to cure. This paper presents a new and more accurate model for the diagnosis and treatment of hepatitis B. Based on previous research, the diagnosis and treatment modes were combined into one. By adding more influencing factors and risk factors, the overall diagnosis and treatment model will be further expanded, and a richer and more detailed overall diagnosis and treatment model will be constructed. Reverse logic gates are used in the model to improve the accuracy of the treatment planning. The new unified model is more accurate in subdividing diagnosis results, and it is more flexible and accurate in providing dynamic treatment plans. The prediction process and the static diagnosis process of the model are symmetric, and the related sub-graph is symmetric in structure. In addition, an algorithm for predicting the response probability of treatment scheme is developed, so as to predict the subsequent treatment effects of the current treatment scheme, such as the probability of drug resistance. The results show that this method is more accurate than other available systems, and it has encouraging diagnostic accuracy and effectiveness, which provides a promising help for doctors in diagnosing hepatitis B.

External Validation of UDCA Response Score in Slovak and Croatian Patients with Primary Biliary Cholangitis

Background. Ursodeoxycholic acid response score (URS) is a prognostic model that estimates the baseline probability of treatment response after 12 months of ursodeoxycholic acid (UDCA) therapy in patients with primary biliary cholangitis (PBC). Aim. To independently evaluate the predictive performance of the URS model. Methods. We used a cohort of Slovak and Croatian treatment-naïve PBC patients to quantify the discrimination ability using the area under receiver operating characteristic curve (AUROC) and its 95% confidence interval (CI). Furthermore, we evaluated the calibration using calibration belts. The primary outcome was treatment response after 12 months of UDCA therapy defined as values of alkaline phosphatase ≤1.67 × upper limit of normal. Results. One hundred and ninety-four patients were included. Median pretreatment age was 56 years (interquartile range 49–62). Treatment response was achieved in 79.38% of patients. AUROC of the URS was 0.81 (95% CI 0.73–0.88) and the calibration belt revealed that response rates were correctly estimated by predicted probabilities. Conclusion. Our results confirm that the URS can be used in treatment-naïve PBC patients for estimating the treatment response probability after 12 months of UDCA therapy.