On the acidity and reactivity of HNO in aqueous solution and biological systems

The gas phase and aqueous thermochemistry and reactivity of nitroxyl (nitrosyl hydride, HNO) were elucidated with multiconfigurational self-consistent field and hybrid density functional theory calculations and continuum solvation methods. The pKa of HNO is predicted to be 7.2 ± 1.0, considerably different from the value of 4.7 reported from pulse radiolysis experiments. The ground-state triplet nature of NO− affects the rates of acid-base chemistry of the HNO/NO− couple. HNO is highly reactive toward dimerization and addition of soft nucleophiles but is predicted to undergo negligible hydration (Keq = 6.9 × 10−5). HNO is predicted to exist as a discrete species in solution and is a viable participant in the chemical biology of nitric oxide and derivatives.

Selenoprotein oxidoreductase with specificity for thioredoxin and glutathione systems

Thioredoxin (Trx) and glutathione (GSH) systems are considered to be two major redox systems in animal cells. They are reduced by NADPH via Trx reductase (TR) or oxidized GSH (GSSG) reductase and further supply electrons for deoxyribonucleotide synthesis, antioxidant defense, and redox regulation of signal transduction, transcription, cell growth, and apoptosis. We cloned and characterized a pyridine nucleotide disulfide oxidoreductase, Trx and GSSG reductase (TGR), that exhibits specificity for both redox systems. This enzyme contains a selenocysteine residue encoded by the TGA codon. TGR can reduce Trx, GSSG, and a GSH-linked disulfide in in vitro assays. This unusual substrate specificity is achieved by an evolutionary conserved fusion of the TR and glutaredoxin domains. These observations, together with the biochemical probing and molecular modeling of the TGR structure, suggest a mechanism whereby the C-terminal selenotetrapeptide serves a role of a protein-linked GSSG and shuttles electrons from the disulfide center within the TR domain to either the glutaredoxin domain or Trx.

On the controllability of distributed systems

To “control” a system is to make it behave (hopefully) according to our “wishes,” in a way compatible with safety and ethics, at the least possible cost. The systems considered here are distributed—i.e., governed (modeled) by partial differential equations (PDEs) of evolution. Our “wish” is to drive the system in a given time, by an adequate choice of the controls, from a given initial state to a final given state, which is the target. If this can be achieved (respectively, if we can reach any “neighborhood” of the target) the system, with the controls at our disposal, is exactly (respectively, approximately) controllable. A very general (and fuzzy) idea is that the more a system is “unstable” (chaotic, turbulent) the “simplest,” or the “cheapest,” it is to achieve exact or approximate controllability. When the PDEs are the Navier–Stokes equations, it leads to conjectures, which are presented and explained. Recent results, reported in this expository paper, essentially prove the conjectures in two space dimensions. In three space dimensions, a large number of new questions arise, some new results support (without proving) the conjectures, such as generic controllability and cases of decrease of cost of control when the instability increases. Short comments are made on models arising in climatology, thermoelasticity, non-Newtonian fluids, and molecular chemistry. The Introduction of the paper and the first part of all sections are not technical. Many open questions are mentioned in the text.

Memory systems in the brain and localization of a memory

It is now clear that there are a number of different forms or aspects of learning and memory that involve different brain systems. Broadly, memory phenomena have been categorized as explicit or implicit. Thus, explicit memories for experience involve the hippocampus–medial temporal lobe system and implicit basic associative learning and memory involves the cerebellum, amygdala, and other systems. Under normal conditions, however, many of these brain–memory systems are engaged to some degree in learning situations. But each of these brain systems is learning something different about the situation. The cerebellum is necessary for classical conditioning of discrete behavioral responses (eyeblink, limb flexion) under all conditions; however, in the “trace” procedure where a period of no stimuli intervenes between the conditioned stimulus and the unconditioned stimulus the hippocampus plays a critical role. Trace conditioning appears to provide a simple model of explicit memory where analysis of brain substrates is feasible. Analysis of the role of the cerebellum in basic delay conditioning (stimuli overlap) indicates that the memories are formed and stored in the cerebellum. The phenomenon of cerebellar long-term depression is considered as a putative mechanism of memory storage.