A unified theoretical framework underlying the regulation of motivated behavior.

To orchestrate behaviors for survival, multiple psychological components have evolved. The current theories do not clearly distinguish the distinct components. In this article, we provide a unified theoretical framework. To optimize survival, there should be four components; (1) "need", an alarm based on a predicted deficiency. (2) "motivation", a direct behavior driver. (3) "pleasure", a teacher based on immediate outcomes. (4) "utility", a teacher based on final delayed outcomes. For behavior stability, need should be accumulated into motivation to drive behavior. Based on the immediate outcome of the behavior, the pleasure should teach whether to continue the current behavior. Based on the final delay outcome, the utility should teach whether to increase future behavior by reshaping the other three components. We provide several neural substrate candidates in the food context. The proposed theoretical framework, in combination with appropriate experiments, will unravel the neural components responsible for each theoretical component.

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems.

Lateral hypothalamic leptin receptor neurons drive hunger-gated food-seeking and consummatory behaviours in male mice.

For survival, it is crucial for eating behaviours to be sequenced through two distinct seeking and consummatory phases. Heterogeneous lateral hypothalamus (LH) neurons are known to regulate motivated behaviours, yet which subpopulation drives food seeking and consummatory behaviours have not been fully addressed. Here, in male mice, fibre photometry recordings demonstrated that LH leptin receptor (LepR) neurons are correlated explicitly in both voluntary seeking and consummatory behaviours. Further, micro-endoscope recording of the LHLepR neurons demonstrated that one subpopulation is time-locked to seeking behaviours and the other subpopulation time-locked to consummatory behaviours. Seeking or consummatory phase specific paradigm revealed that activation of LHLepR neurons promotes seeking or consummatory behaviours and inhibition of LHLepR neurons reduces consummatory behaviours. The activity of LHLepR neurons was increased via Neuropeptide Y (NPY) which acted as a tonic permissive gate signal. Our results identify neural populations that mediate seeking and consummatory behaviours and may lead to therapeutic targets for maladaptive food seeking and consummatory behaviours.