On the prediction by density functional theory of entropies in solution within implicit solvation models.

Context: Entropies are fundamental contributions to Gibbs energies that carry important chemical information, in particular when investigating reaction mechanisms. However, evaluating them in solution is far from being straightforward. In this paper, we focus on its evaluation within the framework of implicit solvation models.

Dramatic changes in mitochondrial subcellular location and morphology accompany activation of the CO concentrating mechanism.

Dynamic changes in intracellular ultrastructure can be critical for the ability of organisms to acclimate to environmental conditions. Microalgae, which are responsible for ~50% of global photosynthesis, compartmentalize their Ribulose 1,5 Bisphosphate Carboxylase/Oxygenase (Rubisco) into a specialized structure known as the pyrenoid when the cells experience limiting CO conditions; this compartmentalization is a component of the CO Concentrating Mechanism (CCM), which facilitates photosynthetic CO fixation as environmental levels of inorganic carbon (Ci) decline. Changes in the spatial distribution of mitochondria in green algae have also been observed under CO limitation, although a role for this reorganization in CCM function remains unclear.

Illuminating microbial mat assembly: Cyanobacteria and Chloroflexota cooperate to structure light-responsive biofilms.

Microbial mats are stratified communities often dominated by unicellular and filamentous phototrophs within an exopolymer matrix. It is challenging to quantify the dynamic responses of community members in situ as they experience steep gradients and rapid fluctuations of light. To address this, we developed a binary consortium using two representative isolates from hot spring mats: the unicellular oxygenic phototrophic cyanobacterium Synechococcus OS-B' (Syn OS-B') and the filamentous anoxygenic phototroph Chloroflexus MS-CIW-1 (Chfl MS-1).

Predicting redox potentials by graph-based machine learning methods.

The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoretical chemistry.