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The prediction of mobility soon after a stroke should allow proper selection for rehabilitation and suggest the long-term prognosis of gait ability. Stable gait is related to midline body orientation and equilibrium mechanisms. We proposed that the sitting balance during the 1st, 2nd, or 3rd weeks after a hemiplegia could be a prognostic indicator for gait at 6 and 12 months. Sitting equilibrium measured in hospital was correlated with gait at the time of discharge and after 6 and 12 months, assessed by standing up, walking, and climbing stairs. The power in the affected limbs in hospital was also correlated with gait at those times. In the 134 patients followed up at 6 months, the correlation of equilibrium with gait at 6 months was r = 0.675 (p < 0.0001), and that of arm power with gait was r = 0.551 (p < 0.0001). Correlations with gait at 12 months were smaller and less meaningful. Assessment of sitting balance, even before the patient can stand, forms an important part of early management of the stroke patient.

Individual animals are adept at making decisions and have cognitive abilities, such as memory, which allow them to hone their decisions. Social animals can also share information. This allows social animals to make adaptive grouplevel decisions. Both individual and collective decision-making systems also have drawbacks and limitations, and while both are well studied, the interaction between them is still poorly understood. Here, we study how individual and collective decision-making interact during ant foraging. We first gathered empirical data on memory-based foraging persistence in the ant Lasius niger. We used these data to create an agent-based model where ants may use social information (trail pheromones), private information (memories) or both to make foraging decisions. The combined use of social and private information by individuals results in greater efficiency at the group level than when either information source was used alone. The modelled ants couple consensus decision-making, allowing them to quickly exploit high-quality food sources, and combined decision-making, allowing different individuals to specialize in exploiting different resource patches. Such a composite collective decision-making system reaps the benefits of both its constituent parts. Exploiting such insights into composite collective decision-making may lead to improved decision-making algorithms.

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