Multiple animal positioning system shows that socially-reared mice influence the social proximity of isolation-reared cagemates.

Social relationships are a key determinant of social behaviour, and disruption of social behaviour is a major symptom of several psychiatric disorders. However, few studies have analysed social relationships among multiple individuals in a group or how social relationships within a group influence the behaviour of members with impaired socialisation. Here, we developed a video-analysis-based system, the Multiple-Animal Positioning System (MAPS), to automatically and separately analyse the social behaviour of multiple individuals in group housing. Using MAPS, we show that social isolation of male mice during adolescence leads to impaired social proximity in adulthood. The phenotype of these socially isolated mice was partially rescued by cohabitation with group-housed (socially-reared) mice, indicating that both individual behavioural traits and those of cagemates influence social proximity. Furthermore, we demonstrate that low reactive behaviour of other cagemates also influence individual social proximity in male mice.

Mice lacking a functional NMDA receptor exhibit social subordination in a group-housed environment.

Social dominance, in which an individual asserts control over others or benefits most after social conflict, has an influence on social behaviour. However, the mechanisms mediating social dominance remain unclear. Social dominance within social groups determines the distribution of rewards such as food and access to mating partners, which can act as reinforcers. In this study, we used the water competition test to determine whether mice were dominant or subordinate. It has been previously reported that mice heterozygous for a missense mutation in Grin1 (Grin1Rgsc174 ) showed altered social behaviour, with increased locomotor activity, novelty seeking and anxiety. However, social dominance in these mice has not been previously investigated. We subjected Grin1Rgsc174/+ mice to the water competition test using IntelliCage and observed that Grin1 influences competitive dominance. We found that Grin1Rgsc174/+ mice exhibited social subordination characterised by decreased corner visit frequency and occupancy time at the beginning of the task. However, Grin1Rgsc174/+ mice retained increased basal activity and exploring behaviour under a group-housed environment. Our findings suggested that Grin1 plays an important role in determining social dominance.

How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

OBJECT: Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared with administering an additional test. METHODS: Data were collected during annual health check-ups performed in Japan between 2011 and 2013 (inclusive). We prepared training and test datasets from the health check-up data to build prediction models; these were composed of 43,524 and 17,789 persons, respectively. Gradient-boosting decision tree (GBDT), random forest (RF), and logistic regression (LR) approaches were trained using the training dataset and were then used to predict hyperuricemia in the test dataset. Undersampling was applied to build the prediction models to deal with the imbalanced class dataset. RESULTS: The results showed that the RF and GBDT approaches afforded the best performances in terms of sensitivity and specificity, respectively. The area under the curve (AUC) values of the models, which reflected the total discriminative ability of the classification, were 0.796 [95% confidence interval (CI): 0.766-0.825] for the GBDT, 0.784 [95% CI: 0.752-0.815] for the RF, and 0.785 [95% CI: 0.752-0.819] for the LR approaches. No significant differences were observed between pairs of each approach. Small changes occurred in the AUCs after applying undersampling to build the models. CONCLUSIONS: We developed a virtual health check-up that predicted the development of hyperuricemia using machine-learning methods. The GBDT, RF, and LR methods had similar predictive capability. Undersampling did not remarkably improve predictive power.

In utero and lactational dioxin exposure induces Sema3b and Sema3g gene expression in the developing mouse brain.

In the developing mammalian brain, neural network formation is regulated by complex signaling cascades. In utero and lactational dioxin exposure is known to induce higher brain function abnormalities and dendritic growth disruption in rodents. However, it is unclear whether perinatal dioxin exposure affects the expression of genes involved in neural network formation. Therefore, we investigated changes in gene expression in the brain regions of developing mice born to dams administered 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD; dose: 0, 0.6, or 3.0 µg/kg) on gestational day 12.5. Quantitative RT-PCR showed that TCDD exposure induced Ahrr expression in the cerebral cortex, hippocampus, and olfactory bulb of 3-day-old mice. Gene microarray analysis indicated that the mRNA expression levels of Sema3b and Sema3g, which encode proteins that are known to control axonal projections, were elevated in the olfactory bulb of TCDD-exposed mice, and the induction of these genes was observed during a 2-week postnatal period. Increased Sema3g expression was also observed in the brain but not in the kidney, liver, lung, and spleen of TCDD-exposed neonatal mice. These results indicate that the Sema3b and Sema3g genes are sensitive to brain-specific induction by dioxin exposure, which may disrupt neural network formation in the mammalian nervous system, thereby leading to abnormal higher brain function in adulthood.