Sebocytes are lipid - secreting cells whose differentiation is marked by the accumulation of intracellular lipid droplets, making their quantification a key readout in sebocyte biology. Manual counting is labor - intensive and subjective, motivating automated solutions. Here, we introduce a simple attention - based multiple instance learning (MIL) framework for sebocyte image analysis. Nile Red - stained sebocyte images were annotated into 14 classes according to droplet counts, expanded via data augmentation to ab out 50,000 cells. Two models were benchmarked: a baseline multi - layer perceptron (MLP) trained on aggregated patch - level counts, and an attention - based MIL model leveraging precomputed ResNet - 50 feature embeddings with trainable instance weighting. Experiments using five - fold cross - validation showed that the baseline MLP achieved more stable performance (mean MAE = 5.6) compared with the attention - based MIL, which was less consistent (mean MAE = 10.7) but occasionally superior in specific folds. The se findings indicate that simple bag - level aggregation provides a robust baseline for slide - level droplet counting, while attention - based MIL requires task - aligned pooling and regularization to fully realize its potential in sebocyte image analysis.

In the era of big data in scientific research, there is a necessity to leverage techniques which reduce human effort in labeling and categorizing large datasets by involving sophisticated machine tools. To combat this problem, we present a novel, general purpose model for 3D segmentation that leverages patch-wise adversariality and Long Short-Term Memory to encode sequential information. Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers. We leverage the patch-wise discriminator in our model to provide an estimate of which slices within these image cubes have poorly generalized feature representations, and correspondingly poor machine performance. These images with corresponding machine proposals would be presented to volunteers on Zooniverse for correction, leading to a drastic reduction in the volunteer effort on citizen science projects. We trained our model on ~2300 liver tissue 3D electron micrographs. Lipid droplets were segmented within these images through human annotation via the `Etch A Cell - Fat Checker' citizen science project, hosted on the Zooniverse platform. In this work, we demonstrate this framework and the selection methodology which resulted in a measured reduction in volunteer effort by more than 60%. We envision this type of joint human-machine partnership will be of great use on future Zooniverse projects.

Drosophila that represents one of the models of neurodegeneration used in the lab to screen for things (both chemically and genetically) that regulate mitophagy. A new study, published in the journal PLOS Biology, suggests that the language used by researchers in describing their results can be utilized to uncover new treatments for Parkinson's disease. The study, led by Angus McQuibban of the University of Toronto in Canada, utilized AI to find an existing anti-cholesterol medication that has the capability to enhance the disposal of mitochondria, which are cellular components responsible for energy production and are affected in Parkinson's disease. The full pathogenic pathway leading to Parkinson's disease (PD) is unknown, but one clear contributor is mitochondrial dysfunction and the inability to dispose of defective mitochondria, a process called mitophagy. At least five genes implicated in PD are linked to impaired mitophagy, either directly or indirectly, and so the authors sought compounds that could enhance the mitophagy process.

Author Summary Recently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components.