Despite the increased awareness and action towards Equality, Diversity and Inclusion (EDI), the glaciological community still experiences and perpetuates examples of exclusionary and discriminatory behavior. We here discuss the challenges and visions from a group predominantly composed of early-career researchers from the 2023 edition of the Karthaus Summer School on Ice Sheets and Glaciers in the Climate System. This paper presents the results of an EDI-focused workshop that the 36 students and 12 lecturers who attended the summer school actively participated in. We identify common threads from participant responses and distill them into collective visions for the future of the glaciological research community, built on actionable steps toward change. In this paper, we address the following questions that guided the workshop: What do we see as current EDI challenges in the glaciology research community and which improvements would we like to see in the next fifty years? Contributions have been sorted into three main challenges we want and need to face: making glaciology (1) more accessible, (2) more equitable and (3) more responsible.

Does low mood have intentional content? If so, what is it? Philosophers have tried to answer both questions by appealing to low mood’s phenomenal character. However, appeals to phenomenology have not settled this debate. Thus, I take a different approach: I tackle both questions by examining low mood’s complex functional role in cognition. I argue that if we take this role into account, we have excellent reason to believe that low mood a) has content, and b) has the following indicative-imperative content: Good events are, on average, less likely to occur than bad events & Limit [the subject’s] resource expenditure!

Pathogenic CACNA1A mutations can result in paroxysmal attacks of encephalopathy, hemiplegia and cerebral edema. We report two patients with CACNA1A-associated encephalopathy, hemiplegia and contralateral hemispheric cerebral edema treated successfully with intravenous magnesium sulfate and dexamethasone. One patient met the clinical criteria for familial hemiplegic migraine. There is a paucity of guidance in the literature on how to manage these patients. Despite some discrepancies in the treatment protocols in our two cases, they indicate that magnesium and dexamethasone could be part of the treatment algorithm for these patients. Further research to delineate appropriate dosing and duration of therapy is needed.

Adsorption of uranyl to SWy-1 montmorillonite was evaluated experimentally and results were modeled to identify likely surface complexation reactions responsible for removal of uranyl from solution. Uranyl was contacted with SWy-1 montmorillonite in a NaCIO4 electrolyte solution at three ionic strengths (I = 0.001, 0.01, 0.1), at pH 4 to 8.5, in a N2(g) atmosphere. At low ionic strength, adsorption decreased from 95% at pH 4 to 75% at pH 6.8. At higher ionic strength, adsorption increased with pH from initial values less than 75%; adsorption edges for all ionic strengths coalesced above a pH of 7. A site-binding model was applied that treated SWy-1 as an aggregate of fixed-charge sites and edge sites analogous to gibbsite and silica. The concentration of fixed-charge sites was estimated as the cation exchange capacity, and non-preference exchange was assumed in calculating the contribution of fixed-charge sites to total uranyl adsorption. The concentration of edge sites was estimated by image analysis of transmission electron photomicrographs. Adsorption constants for uranyl binding to gibbsite and silica were determined by fitting to experimental data, and these adsorption constants were then used to simulate SWy-1 adsorption results. The best simulations were obtained with an ionization model in which AlOH2+ was the dominant aluminol surface species throughout the experimental range in pH. The pH-dependent aqueous speciation of uranyl was an important factor determining the magnitude of uranyl adsorption. At low ionic strength and low pH, adsorption by fixed-charge sites was predominant. The decrease in adsorption with increasing pH was caused by the formation of monovalent aqueous uranyl species, which were weakly bound to fixed-charge sites. At higher ionic strengths, competition with Na+ decreased the adsorption of UO22+ to fixed-charge sites. At higher pH, the most significant adsorption reactions were the binding of UO22+ to AlOH and of (UO2)3(OH)5+ to SiOH edge sites. Near-saturation of AlOH sites by UO22+ allowed significant contributions of SiOH sites to uranyl adsorption.

To investigate the symptoms of SARS-CoV-2 infection, their dynamics and their discriminatory power for the disease using longitudinally, prospectively collected information reported at the time of their occurrence. We have analysed data from a large phase 3 clinical UK COVID-19 vaccine trial. The alpha variant was the predominant strain. Participants were assessed for SARS-CoV-2 infection via nasal/throat PCR at recruitment, vaccination appointments, and when symptomatic. Statistical techniques were implemented to infer estimates representative of the UK population, accounting for multiple symptomatic episodes associated with one individual. An optimal diagnostic model for SARS-CoV-2 infection was derived. The 4-month prevalence of SARS-CoV-2 was 2.1%; increasing to 19.4% (16.0%–22.7%) in participants reporting loss of appetite and 31.9% (27.1%–36.8%) in those with anosmia/ageusia. The model identified anosmia and/or ageusia, fever, congestion, and cough to be significantly associated with SARS-CoV-2 infection. Symptoms’ dynamics were vastly different in the two groups; after a slow start peaking later and lasting longer in PCR+ participants, whilst exhibiting a consistent decline in PCR- participants, with, on average, fewer than 3 days of symptoms reported. Anosmia/ageusia peaked late in confirmed SARS-CoV-2 infection (day 12), indicating a low discrimination power for early disease diagnosis.

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.

Social learning is a critical adaptation for dealing with different forms of variability. Uncertainty is a severe form of variability where the space of possible decisions or probabilities of associated outcomes are unknown. We identified four theoretically important sources of uncertainty: temporal environmental variability; payoff ambiguity; selection-set size; and effective lifespan. When these combine, it is nearly impossible to fully learn about the environment. We develop an evolutionary agent-based model to test how each form of uncertainty affects the evolution of social learning. Agents perform one of several behaviours, modelled as a multi-armed bandit, to acquire payoffs. All agents learn about behavioural payoffs individually through an adaptive behaviour-choice model that uses a softmax decision rule. Use of vertical and oblique payoff-biased social learning evolved to serve as a scaffold for adaptive individual learning – they are not opposite strategies. Different types of uncertainty had varying effects. Temporal environmental variability suppressed social learning, whereas larger selection-set size promoted social learning, even when the environment changed frequently. Payoff ambiguity and lifespan interacted with other uncertainty parameters. This study begins to explain how social learning can predominate despite highly variable real-world environments when effective individual learning helps individuals recover from learning outdated social information.