Summary: Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time‐consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad‐scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured withinc.5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in ‘real‐world’ environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

Machine learning has recently been introduced in the context of droplet microfluidics to simplify the process of droplet formation, which is usually controlled by a variety of parameters. However, the studies introduced so far have mainly focused on droplet size control using water and mineral oil in microfluidic devices fabricated using soft lithography or rapid prototyping. This approach negated the applicability of machine learning results to other types of fluids more relevant to biomedical applications, while also preventing users that do not have access to microfluidic fabrication facilities to take advantage of previous findings. There are a number of different algorithms that could be used as part of a data driven approach, and no clear comparison has been previously offered among multiple machine learning architectures with respect to the predictions of flow rate values and generation rate. We here employed machine learning to predict the experimental parameters required for droplet generation in three commercialized microfluidic flow-focusing devices using phosphate buffer saline and biocompatible fluorinated oil as dispersed and continuous liquid phases, respectively. We compared three different machine learning architectures and established the one leading to more accurate predictions. We also compared the predictions with a new set of experiments performed at a different day to account for experimental variability. Finally, we provided a proof of concept related to algae encapsulation and designed a simple app that can be used to generate accurate predictions for a given droplet size and generation rate across the three commercial devices.

According to Conway's view, Autobiographical memory (AM) construction is accompanied by control processes. These processes range from filtering out relevant memories according to the current context, to generating or elaborating appropriate retrieval cues. These processes can be conceptualised as metacognition, the ability to control and monitor cognitive processes. Experimentally, little has been carried out to support the idea that metacognition is involved in AM. To assess this, we designed a task, the Feeling of Retrieval. Participants had to predict whether cue words would facilitate AM access (i.e., fluent access cues) or not (i.e., limited access cues) in a limited time (either 1 (Exp. 2) or 2 (Exp. 1) s). Later, they retrieved memories in response to both types of cues. Results show that cues judged as fluent access led to better AM generation, as illustrated by AM retrieval latency and a subjective measure of the ease with which the AMs were retrieved. These rapid predictions may rely on epistemic feelings and / or other mnemonic cues such as a partial retrieval of information. This metacognitive access to the earliest stages of AM retrieval illustrates the ability to monitor AM processes as proposed by Conway (2005).

Background/Objectives: Vulnerability to sound distraction is commonly reported in older adults with dementia and tends to be associated with adverse impacts on daily activity. However, study outcome heterogeneity is increasingly evident, with preserved resistance to distraction also evident. Contributory factors may include individual differences in distractibility in older adulthood per se, and failure to consider the influence of how difficult a person found the test. Methods: We therefore measured distractibility in a group of older adults by comparing the performance of a primary visual task (Swansea Test of Attentional Control), which includes an adaptive algorithm to take into account how difficult a person finds the test under both no-sound and sound conditions. Results: Analysis revealed no significant difference in group mean performance between no-sound versus sound conditions [t (33) = 0.181, p = 0.858; Cohen’s effect size d = −0.028], but individual differences in performance both within and between sound and no-sound conditions were evident, indicating that for older adults, distracting sounds can be neutral, detrimental, or advantageous with respect to visual task performance. It was not possible to determine individual thresholds for whether sound versus no-sound conditions affected a person’s actual behaviour. Conclusions: Nevertheless, our findings indicate how variable such effects may be in older adults, which in turn may help to explain outcome heterogeneity in studies including people living with dementia. Furthermore, such within-group heterogeneity highlights the importance of considering a person’s individual performance in order to better understand their behaviour and initiate interventions as required.