Approximating Optimal Behavioural Strategies Down to Rules-of-Thumb: Energy Reserve Changes in Pairs of Social Foragers

Functional explanations of behaviour often propose optimal strategies for organisms to follow. These ‘best’ strategies could be difficult to perform given biological constraints such as neural architecture and physiological constraints. Instead, simple heuristics or ‘rules-of-thumb’ that approximate these optimal strategies may instead be performed. From a modelling perspective, rules-of-thumb are also useful tools for considering how group behaviour is shaped by the behaviours of individuals. Using simple rules-of-thumb reduces the complexity of these models, but care needs to be taken to use rules that are biologically relevant. Here, we investigate the similarity between the outputs of a two-player dynamic foraging game (which generated optimal but complex solutions) and a computational simulation of the behaviours of the two members of a foraging pair, who instead followed a rule-of-thumb approximation of the game's output. The original game generated complex results, and we demonstrate here that the simulations following the much-simplified rules-of-thumb also generate complex results, suggesting that the rule-of-thumb was sufficient to make some of the model outcomes unpredictable. There was some agreement between both modelling techniques, but some differences arose – particularly when pair members were not identical in how they gained and lost energy. We argue that exploring how rules-of-thumb perform in comparison to their optimal counterparts is an important exercise for biologically validating the output of agent-based models of group behaviour

Diversity of dimethylsulfide‐degrading methanogens and sulfate‐reducing bacteria in anoxic sediments along the Medway Estuary, UK

Methane is a powerful greenhouse gas but the microbial diversity mediating methylotrophic methanogenesis is not well-characterized. One overlooked route to methane is via the degradation of dimethylsulfide (DMS), an abundant organosulfur compound in the environment. Methanogens and sulfate-reducing bacteria (SRB) can degrade DMS in anoxic sediments depending on sulfate availability. However, we know little about the underlying microbial community and how sulfate availability affects DMS degradation in anoxic sediments. We studied DMS-dependent methane production along the salinity gradient of the Medway Estuary (UK) and characterized, for the first time, the DMS-degrading methanogens and SRB using cultivation-independent tools. DMS metabolism resulted in high methane yield (39%–42% of the theoretical methane yield) in anoxic sediments regardless of their sulfate content. Methanomethylovorans, Methanolobus and Methanococcoides were dominant methanogens in freshwater, brackish and marine incubations respectively, suggesting niche-partitioning of the methanogens likely driven by DMS amendment and sulfate concentrations. Adding DMS also led to significant changes in SRB composition and abundance in the sediments. Increases in the abundance of Sulfurimonas and SRB suggest cryptic sulfur cycling coupled to DMS degradation. Our study highlights a potentially important pathway to methane production in sediments with contrasting sulfate content and sheds light on the diversity of DMS degraders

Irreversible Electroporation (IRE) in Renal Tumors

Small renal masses (SRMs) have been traditionally managed with surgical resection. Minimally invasive nephron-sparing treatment methods are preferred to avoid harmful consequences of renal insufficiency, with partial nephrectomy (PN) considered the gold standard. With increase in the incidence of the SRMs and evolution of ablative technologies, percutaneous ablation is now considered a viable treatment alternative to surgical resection with comparable oncologic outcomes and better nephron-sparing property. Traditional thermal ablative techniques suffer from unique set of challenges in treating tumors near vessels or critical structures. Irreversible electroporation (IRE), with its non-thermal nature and connective tissue-sparing properties, has shown utility where traditional ablative techniques face challenges. This review presents the role of IRE in renal tumors based on the most relevant published literature on the IRE technology, animal studies, and human experience

Algorithms for survival: a comparative perspective on emotions

The nature and neural implementation of emotions is the subject of vigorous debate. Here, we use Bayesian decision theory to address key complexities in this field and conceptualize emotions in terms of their relationship to survival-relevant behavioural choices. Decision theory indicates which behaviours are optimal in a given situation; however, the calculations required are radically intractable. We therefore conjecture that the brain uses a range of pre-programmed algorithms that provide approximate solutions. These solutions seem to produce specific behavioural manifestations of emotions and can also be associated with core affective dimensions. We identify principles according to which these algorithms are implemented in the brain and illustrate our approach by considering decision making in the face of proximal threat