Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI-assisted technologies in scientific research and education.

The emergence of large language models (LLMs) and assisted artificial intelligence (AI) technologies have revolutionized the way in which we interact with technology. A recent symposium at the Walter and Eliza Hall Institute explored the current practical applications of LLMs in medical research and canvassed the emerging ethical, legal and social implications for the use of AI-assisted technologies in the sciences. This paper provides an overview of the symposium's key themes and discussions delivered by diverse speakers, including early career researchers, group leaders, educators and policy-makers highlighting the opportunities and challenges that lie ahead for scientific researchers and educators as we continue to explore the potential of this cutting-edge and emerging technology.

Live-cell fluorescence imaging of microgametogenesis in the human malaria parasite Plasmodium falciparum.

Formation of gametes in the malaria parasite occurs in the midgut of the mosquito and is critical to onward parasite transmission. Transformation of the male gametocyte into microgametes, called microgametogenesis, is an explosive cellular event and one of the fastest eukaryotic DNA replication events known. The transformation of one microgametocyte into eight flagellated microgametes requires reorganisation of the parasite cytoskeleton, replication of the 22.

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks.

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving.

Risk surveillance and mitigation: autoantibodies as triggers and inhibitors of severe reactions to SARS-CoV-2 infection.

COVID-19 clinical presentation differs considerably between individuals, ranging from asymptomatic, mild/moderate and severe disease which in some cases are fatal or result in long-term effects. Identifying immune mechanisms behind severe disease development informs screening strategies to predict who are at greater risk of developing life-threatening complications. However, to date clear prognostic indicators of individual risk of severe or long COVID remain elusive.

Batch equalization with a generative adversarial network.

Motivation: Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-batch variation (i.e.

A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens.

Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction.

Cellular dissection of malaria parasite invasion of human erythrocytes using viable Plasmodium knowlesi merozoites.

Plasmodium knowlesi, a zoonotic parasite causing severe-to-lethal malaria disease in humans, has only recently been adapted to continuous culture with human red blood cells (RBCs). In comparison with the most virulent human malaria, Plasmodium falciparum, there are, however, few cellular tools available to study its biology, in particular direct investigation of RBC invasion by blood-stage P. knowlesi merozoites.

Spatio-temporal image correlation spectroscopy and super-resolution microscopy to quantify molecular dynamics in T cells.

Many cellular processes are regulated by the spatio-temporal organisation of signalling complexes, cytoskeletal components and membranes. One such example is at the T cell immunological synapse where the retrograde flow of cortical filamentous (F)-actin from the synapse periphery drives signalling protein microclusters towards the synapse centre. The density of this mesh however, makes visualisation and analysis of individual actin fibres difficult due to the resolution limit of conventional microscopy.

Quantification of fibrous spatial point patterns from single-molecule localization microscopy (SMLM) data.

Motivation: Unlike conventional microscopy which produces pixelated images, SMLM produces data in the form of a list of localization coordinates-a spatial point pattern (SPP). Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular clustering within, for example, the plasma membrane. While SMLM cluster analysis is now well developed, techniques for analyzing fibrous structures remain poorly explored.

Molecular flow quantified beyond the diffraction limit by spatiotemporal image correlation of structured illumination microscopy data.

We combine total internal reflection fluorescence structured illumination microscopy with spatiotemporal image correlation spectroscopy to quantify the flow velocities and directionality of filamentous-actin at the T cell immunological synapse. These techniques demonstrate it is possible to image retrograde flow of filamentous-actin at superresolution and provide flow quantification in the form of velocity histograms and flow vector maps. The flow was found to be retrograde and radially directed throughout the periphery of T-cells during synapse formation.