The paper reports a study of the kinetics of the reaction between phosphoenolpyruvate, ADP and Mg2+ catalysed by rabbit muscle pyruvate kinase. The experimental results indicate that the reaction mechanism is equilibrium random-order in type, that the substrates and products are phosphoenolpyruvate, ADP, Mg2+, pyruvate and MgATP, and that dead-end complexes, between pyruvate, ADP and Mg2+, form randomly and exist in equilibrium with themselves and other substrate complexes. Values were determined for the Michaelis, dissociation and inhibition constants of the reaction and are compared with values ascertained by previous workers.

The paper reports a study of the kinetics of the reaction between phosphoenolpyruvate, ADP and Mg(2+) catalysed by yeast pyruvate kinase when activated by fructose 1,6-diphosphate and K(+). The experimental results indicate that the reaction mechanism is of the Ordered Tri Bi type with the substrates binding in the order phosphoenolpyruvate, ADP and Mg(2+). Direct phosphoryl transfer takes place in the quaternary complex, with pyruvate released before MgATP. A dead-end enzyme-pyruvate complex is also indicated. Values have been determined for the Michaelis, dissociation and inhibition constants of the reaction. Several of the rate constants involved have also been evaluated.

The purification of NADP‐linked isocitrate dehydrogenase from ox heart mitochondria is described. The molecular weight from gel filtration, sedimentation equilibrium and gel electrophoresis is 90000 ± 4000, and there are two subunits in the molecule each of which binds NADPH with enhancement of the coenzyme fluorescence. The amino‐acid composition is reported, and the absorption coefficient, A2801% estimated from dry weight measurements is 11.8 cm−1.

The hallmark of the information age is the ease with which information is stored, accessed, and shared throughout the globe. This is enabled, in large part, by the simplicity of duplicating digital information without error. Unfortunately, an ever-growing consequence is the global threat to security and privacy enabled by our digital reliance. Specifically, modern secure communications and authentication suffer from formidable threats arising from the potential for copying of secret keys stored in digital media. With relatively little transfer of information, an attacker can impersonate a legitimate user, publish malicious software that is automatically accepted as safe by millions of computers, or eavesdrop on countless digital exchanges. To address this vulnerability, a new class of cryptographic devices known as physical unclonable functions (PUFs) are being developed. PUFs are modern realizations of an ancient concept, the physical key, and offer an attractive alternative for digital key storage. A user derives a digital key from the PUF’s physical behavior, which is sensitive to physical idiosyncrasies that are beyond fabrication tolerances. Thus, unlike conventional physical keys, a PUF cannot be duplicated and only the holder can extract the digital key. However, emerging machine learning (ML) methods are remarkably adept at learning behavior via training, and if such algorithms can learn to emulate a PUF, then the security is compromised. Unfortunately, such attacks are highly successful against conventional electronic PUFs. Here, we investigate ML attacks against a nonlinear silicon photonic PUF, a novel design that leverages nonlinear optical interactions in chaotic silicon microcavities. First, we investigate these devices’ resistance to cloning during fabrication and demonstrate their use as a source of large volumes of cryptographic key material. Next, we demonstrate that silicon photonic PUFs exhibit resistance to state-of-the-art ML attacks due to their nonlinearity and finally validate this resistance in an encryption scenario.

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