Leo Chi U Seak’s research while affiliated with University of Cambridge and other places

Realistic, everyday rewards contain multiple components, such as taste and size. However, our reward valuations and the associated neural reward signals are single dimensional (vector to scalar transformation). Here, we present a protocol to identify these single-dimensional neural responses for multi-component choice options in humans and monkeys using concept-based behavioral choice experiments. We describe the use of stringent economic concepts to develop and implement behavioral tasks. We detail regional neuroimaging in humans and fine-grained neurophysiology in monkeys and describe approaches for data analysis. For complete details on the use and execution of this protocol, please refer to our work on humans Seak et al.¹ and Pastor-Bernier et al.² and monkeys Pastor-Bernier et al. ³, Pastor-Bernier et al.⁴, and Pastor-Bernier et al.⁵.

The past decades have seen tremendous progress in fundamental studies on economic choice in humans. However, elucidation of the underlying neuronal processes requires invasive neurophysiological studies that are met with difficulties in humans. Monkeys as evolutionary closest relatives offer a solution. The animals display sophisticated and well-controllable behavior that allows to implement key constructs of proven economic choice theories. However, the similarity of economic choice between the two species has never been systematically investigated. We investigated compliance with the independence axiom (IA) of expected utility theory as one of the most demanding choice tests and compared IA violations between humans and monkeys. Using generalized linear modeling and cumulative prospect theory (CPT), we found that humans and monkeys made comparable risky choices, although their subjective values (utilities) differed. These results suggest similar fundamental choice mechanism across these primate species and encourage to study their underlying neurophysiological mechanisms.

Unlabelled: Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms. Supplementary information: The online version contains supplementary material available at 10.1007/s11166-022-09388-7.

Decisions can be risky or riskless, depending on the outcomes of the choice. Expected utility theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the option’s value and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of two macaque monkeys in risky and riskless decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for risky and riskless choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

Endocrine Disrupting Chemicals (EDCs) are a group of molecules that can influence hormonal balance, causing disturbance of the reproductive system and other health problems. Despite the efforts to eliminate EDC in the environment, all current approaches are inefficient and expensive. In previous research, studies revealed that laccase-producing microorganisms may be a potential candidate for EDC degradation, as laccases have been found to be able to degrade many kinds of EDCs effectively and steadily. Here, we created two recombinant laccases, each fused with secretion peptide, Novel Signal Peptide 4 (NSP4), and expressed them in Escherichia coli (E. coli, BL21), together with one laccase without secretion peptide. We first optimized the culture condition of expressing these laccases. Then, we test the activity of the recombinant laccases of decolorizing of a synthetic dye, indigo carmine. Finally, we confirmed the secreted can degrade one of the EDCs, β-estradiol, showing the potential of using the laccase secretion system to degrade toxic compounds.

Current fire retardants are known to be toxic to humans and our environment. As environmental-friendly flame retardants (FRs), protein-based flame retardants have been studied extensively recently, even though they are not durable. In this study, we designed, synthesized and tested a durable protein-based FR through the fusion of the adhesion domain from either mussel foot protein-5 (mfp-5) or cellulose-binding domain (CBD) with flame retardant protein (SR protein and alpha casein). We first verified the expression of the recombinant proteins in Escherichia coli using Western blot. Then, we coated the fusion protein (carrying cell lysates) to cotton fabrics and wood and verified with Infrared (IR) spectroscopy. Using a vertical burning test and wood flammability test, we confirmed the flame retardancy of the materials after the protein coating. In the vertical burning test, the SR protein and alpha casein flame retardant proteins with the CBD adhesion domain showed a 50.0% and 43.3% increase in flame retardancy. The data is also consistent in the wood flame retardancy test. Confocal imaging experiments also suggested these new fire retardants can be preserved on the materials well even after washing. Overall, our results showed that flame-retardant proteins with adhesion domains are high potential candidates of green alternative flame retardants.

Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The independence axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA in thousands of stochastic choice over several months using a large variety of binary option sets. Three monkeys showed few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (87%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms.

Artificial neural network (ANN) is nowadays one of the most used and researched computational methods. In the field of biocomputing, however, synthetic biologists are still using logic gate technologies to design genetic circuits. We here propose and computationally validate a novel method to mimic ANN with genetic circuits. We first describe the flow and the mathematical expression of this genetic circuit design and then we provide in silico proof to support the functionality of our method in regression and classification analyses. We believe that this de novo genetic circuit design method would have wide applications in biotechnology.