Cancer tissues reflect a greater number of pathological characteristics of cancer compared to cancer cells, so the evaluation of cancer tissues can be effective in determining cancer treatment strategies. Mass spectrometry imaging (MSI) can evaluate cancer tissues and even identify molecules while preserving spatial information. Cluster analysis of cancer tissues’ MSI data is currently used to evaluate the phenotype heterogeneity of the tissues. Interestingly, it has been reported that phenotype heterogeneity does not always coincide with genotype heterogeneity in HER2-positive breast cancer. We thus investigated the phenotype heterogeneity of luminal breast cancer, which is generally known to have few gene mutations. As a result, we identified phenotype heterogeneity based on lipidomics in luminal breast cancer tissues. Clusters were composed of phosphatidylcholine (PC), triglycerides (TG), phosphatidylethanolamine, sphingomyelin, and ceramide. It was found that mainly the proportion of PC and TG correlated with the proportion of cancer and stroma on HE images. Furthermore, the number of carbons in these lipid class varied from cluster to cluster. This was consistent with the fact that enzymes that synthesize long-chain fatty acids are increased through cancer metabolism. It was then thought that clusters containing PCs with high carbon counts might reflect high malignancy. These results indicate that lipidomics-based phenotype heterogeneity could potentially be used to classify cancer for which genetic analysis alone is insufficient for classification.

In local and global disaster scenes, rapid recognition of victims’ breathing is vital. It is unclear whether the footage transmitted from small drones can enable medical providers to detect breathing. This study investigated the ability of small drones to evaluate breathing correctly after landing on victims’ bodies and hovering over them. We enrolled 46 medical workers in this prospective, randomized, crossover study. The participants were provided with envelopes, from which they were asked to pull four notes sequentially and follow the written instructions (“breathing” and “no breathing”). After they lied on the ground in the supine position, a drone was landed on their abdomen, subsequently hovering over them. Two evaluators were asked to determine whether the participant had followed the “breathing” or “no breathing” instruction based on the real-time footage transmitted from the drone camera. The same experiment was performed while the participant was in the prone position. If both evaluators were able to determine the participant’s breathing status correctly, the results were tagged as “correct.” All experiments were successfully performed. Breathing was correctly determined in all 46 participants (100%) when the drone was landed on the abdomen and in 19 participants when the drone hovered over them while they were in the supine position (p < 0.01). In the prone position, breathing was correctly determined in 44 participants when the drone was landed on the abdomen and in 10 participants when it was kept hovering over them (p < 0.01). Notably, breathing status was misinterpreted as “no breathing” in 8 out of 27 (29.6%) participants lying in the supine position and 13 out of 36 (36.1%) participants lying in the prone position when the drone was kept hovering over them. The landing points seemed wider laterally when the participants were in the supine position than when they were in the prone position. Breathing status was more reliably determined when a small drone was landed on an individual’s body than when it hovered over them.

The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents’ brain sections. CNN produced classification models with high accuracy and low loss rate in any test data sets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS and of mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. On the basis of the extracted mass spectra pattern features, the histologically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging generated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no significant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment.

Division of labor is a prominent feature of social insect societies, where different castes engage in different specialized tasks. As brain differences are associated with behavioral differences, brain anatomy may be linked to caste polymorphism. Here, we show that termite brain morphology changes markedly with caste differentiation and age in the termite, Reticulitermes speratus. Brain morphology was shown to be associated with reproductive division of labor, with reproductive individuals (alates and neotenic reproductives) having larger brains than nonreproductives (workers and soldiers). Micro‐computed tomography (CT) imaging and dissection observations showed that the king's brain morphology changed markedly with shrinkage of the optic lobes during their long life in the dark. Behavioral experiments showed that mature primary kings lose visual function as a result of optic lobe shrinkage. These results suggested that termites restructure their nervous systems to perform necessary tasks as they undergo caste differentiation, and that they also show flexible changes in brain morphology even after the final molt. This study showed that brain morphology in social insects is linked to caste and aging, and that the evolution of the division of labor is underpinned by the development of diverse neural systems for specialized tasks.