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Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.

 

Incorporation of secondary redox-inactive cations into heterobimetallic complexes is an attractive strategy for modulation of metal-centered redox chemistry, but quantification of the consequences of incorporating strongly Lewis acidic trivalent cations has received little attention. Here, a family of seven heterobimetallic complexes that pair a redox-active nickel center with La3+, Y3+, Lu3+, Sr2+, Ca2+, K+, and Na+ (in the form of their triflate salts) have been prepared on a heteroditopic ligand platform to understand how chemical behavior varies across the comprehensive series. Structural data from X-ray diffraction analysis demonstrate that the positions adopted by the secondary cations in the crown-ether-like site of the ligand relative to nickel are dependent primarily on the secondary cations’ ionic radii and that the triflate counteranions are bound to the cations in all cases. Electrochemical data, in concert with electron paramagnetic resonance studies, show that nickel(II)/nickel(I) redox is modulated by the secondary metals; the heterogeneous electron-transfer rate is diminished for the derivatives incorporating trivalent metals, an effect that is dependent on steric crowding about the nickel metal center and that was quantified here with a topographical free-volume analysis. As related analyses carried out here on previously reported systems bear out similar relationships, we conclude that the placement and identity of both the secondary metal cations and their associated counteranions can afford unique changes in the (electro)chemical behavior of heterobimetallic species. 

We present an optical photometric and spectroscopic analysis of the fast-declining hydrogen-rich Type II supernova (SN) 2019nyk. The light curve properties of SN 2019nyk align well with those of other fast-declining Type II SNe, such as SNe 2013by and 2014G. SN 2019nyk exhibits a peak absolute magnitude of −18.09 ± 0.17 mag in theVband, followed by a rapid decline at 2.84  ±  0.03 mag (100 d)⁻¹during the recombination phase. The early spectra of SN 2019nyk exhibit high-ionisation emission features as well as narrow H Balmer lines, persisting until 4.1 d since explosion, indicating the presence of circumstellar material (CSM) in close proximity. A comparison of these features with other Type II SNe displaying an early interaction reveals similarities between these features and those observed in SNe 2014G and 2023ixf. We also compared the early spectra to literature models, estimating a mass-loss rate of the order of 10⁻³M_⊙yr⁻¹. Radiation hydrodynamical modelling of the light curve also suggests the mass loss from the progenitor within a short period prior to explosion, totalling 0.16M_⊙of material within 2900R_⊙of the progenitor. Furthermore, light curve modelling infers a zero-age main sequence mass of 15M_⊙for the progenitor, a progenitor radius of 1031R_⊙, and an explosion energy of 1.1 × 10⁵¹erg.

 

Experiential purchases (focused ondoingrather thanhaving) provide more satisfaction than material goods. Here, we examine a different downstream consequence of spending money on experiences: fostering social connection. Consumers reported feeling more kinship with someone who had made a similar experiential purchase than someone who had made a similar material purchase—a result tied to the greater centrality of experiences to one's identity. This greater sense of connection that experiences provide applied even when someone else had made a similar, but superior purchase. Participants also reported feeling more connected to others in general, not just those who have made the same purchase, when reflecting on experiential consumption—and these feelings of connection were expressed in a greater desire to engage in social activities when participants considered their experiential purchases than when they considered their material purchases. Together, these results demonstrate that experiential consumption enhances people's social connection quite broadly.

 

Conversational AIs such as Alexa and ChatGPT are increasingly ubiquitous in young people’s lives, but these young users are often not afforded the opportunity to learn about the inner workings of these technologies. One of the most powerful ways to foster this learning is to empower youth to create AI that is personally and socially meaningful to them. We have built a novel development environment, AMBY–‘‘AI Made By You’’–for youth to create conversational agents. AMBY was iteratively designed with and for youth aged 12–13 through contextual inquiry and usability studies. AMBY is designed to foster AI learning with features that enable users to generate training datasets and visualize conversational flow. We report on results from a two-week summer camp deployment, and contribute design implications for conversational AI authoring tools that empower AI learning for youth.