Bibliometric analysis of the global scientific production on machine learning applied to different cancer types.

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Quantum molecular simulations of micro-hydrated halogen anions.

We report the results of a detailed and accurate investigation focused on structures and energetics of poly-hydrated halides employing first-principles polarizable halide-water potentials to describe the underlying forces. Following a bottom-up data-driven potential approach, we initially looked into the classical behavior of higher-order X-(H2O)N clusters. We have located several low-lying energies, such as global and local minima, structures for each cluster, with various water molecules (up to N = 8) surrounding the halide anion (X- = F-, Cl-, Br-, I-), employing an evolutionary programming method. It is found that the F--water clusters exhibit different structural configurations than the heavier halides, however independently of the halide anion, all clusters show in general a selective growth with the anion preferring to be connected to the outer shell of the water molecule arrangements. In turn, path-integral molecular dynamics simulations are performed to incorporate explicitly nuclear quantum and thermal effects in describing the nature of halide ion microsolvation in such prototypical model systems. Our data reveal that at low finite temperatures, nuclear quantum effects affect certain structural properties, such as weakening hydrogen bonding between the halide anion and water molecules, with minor distortions in the water network beyond the first hydration shell, indicating local structure rearrangements. Such structural characteristics and the promising cluster size trends observed in the single-ion solvation energies motivated us to draw connections of small size cluster data to the limits of continuum bulk values, toward the investigation of the challenging computational modeling of bulk single ion hydration.

Translucency of Graphene to van der Waals Forces Applies to Atoms/Molecules with Different Polar Character.

Graphene has been proposed to be either fully transparent to van der Waals interactions to the extent of allowing switching between hydrophobic and hydrophilic behavior, or partially transparent (translucent), yet there has been considerable debate on this topic, which is still ongoing. In a combined experimental and theoretical study we investigate the effects of different metal substrates on the adsorption energy of atomic (argon) and molecular (carbon monoxide) adsorbates on high-quality epitaxial graphene. We demonstrate that while the adsorption energy is certainly affected by the chemical composition of the supporting substrate and by the corrugation of the carbon lattice, the van der Waals interactions between adsorbates and the metal surfaces are partially screened by graphene. Our results indicate that the concept of graphene translucency, already introduced in the case of water droplets, is found to hold more generally also in the case of single polar molecules and atoms, which are apolar.