Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis.

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset-namely CMTD-of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.

A Statistical Analysis of Risk Factors and Biological Behavior in Canine Mammary Tumors: A Multicenter Study.

Canine mammary tumors (CMTs) represent a serious issue in worldwide veterinary practice and several risk factors are variably implicated in the biology of CMTs. The present study examines the relationship between risk factors and histological diagnosis of a large CMT dataset from three academic institutions by classical statistical analysis and supervised machine learning methods. Epidemiological, clinical, and histopathological data of 1866 CMTs were included. Dogs with malignant tumors were significantly older than dogs with benign tumors (9.6 versus 8.7 years, P < 0.001). Malignant tumors were significantly larger than benign counterparts (2.69 versus 1.7 cm, P < 0.001). Interestingly, 18% of malignant tumors were smaller than 1 cm in diameter, providing compelling evidence that the size of the tumor should be reconsidered during the assessment of the TNM-WHO clinical staging. The application of the logistic regression and the machine learning model identified the age and the tumor's size as the best predictors with an overall diagnostic accuracy of 0.63, suggesting that these risk factors are sufficient but not exhaustive indicators of the malignancy of CMTs. This multicenter study increases the general knowledge of the main epidemiologica-clinical risk factors involved in the onset of CMTs and paves the way for further investigations of these factors in association with CMTs and in the application of machine learning technology.

Molecular QCA embedding in microporous materials.

We propose an environment for information encoding and transmission via a nanoconfined molecular Quantum Dot Cellular Automata (QCA) wire, composed of a single row of head-to-tail interacting 2-dots molecular switches. While most of the research in the field refers to dots-bearing molecules bound on some type of surface, forming a bidimensional array of square cells capable of performing QCA typical functions, we propose here to embed the information bearing elements within the channels of a microporous matrix. In this way molecules would self-assemble in a row as a consequence of adsorption inside the pores of the material, forming an encased wire, with the crystalline environment giving stability and protection to the structure. DFT calculations on a diferrocenyl carborane, previously proposed and synthesized [J. A. Christie, R. P. Forrest, S. A. Corcelli, N. A. Wasio, R. C. Quardokus, R. Brown, S. A. Kandel, Y. Lu, C. S. Lent and K. W. Henderson, Angew. Chem., Int. Ed., 2015, 54, 15448], were performed both in vacuum and inside the channels of zeolite ITQ-51, indicating that information encoding and transmission is possible within the nanoconfined environment.

Local free energies for the coarse-graining of adsorption phenomena: The interacting pair approximation.

We investigate the coarse-graining of host-guest systems under the perspective of the local distribution of pore occupancies, along with the physical meaning and actual computability of the coarse-interaction terms. We show that the widely accepted approach, in which the contributions to the free energy given by the molecules located in two neighboring pores are estimated through Monte Carlo simulations where the two pores are kept separated from the rest of the system, leads to inaccurate results at high sorbate densities. In the coarse-graining strategy that we propose, which is based on the Bethe-Peierls approximation, density-independent interaction terms are instead computed according to local effective potentials that take into account the correlations between the pore pair and its surroundings by means of mean-field correction terms without the need for simulating the pore pair separately. Use of the interaction parameters obtained this way allows the coarse-grained system to reproduce more closely the equilibrium properties of the original one. Results are shown for lattice-gases where the local free energy can be computed exactly and for a system of Lennard-Jones particles under the effect of a static confining field.

Partial Charges in Periodic Systems: Improving Electrostatic Potential (ESP) Fitting via Total Dipole Fluctuations and Multiframe Approaches.

Two major improvements to the state-of-the-art Repeating Electrostatic Potential Extracted Atomic (REPEAT) method, for generating accurate partial charges for molecular simulations of periodic structures, are here developed. The first, D-REPEAT, consists in the simultaneous fit of the electrostatic potential (ESP), together with the total dipole fluctuations (TDF) of the framework. The second, M-REPEAT, allows the fit of multiple ESP configurations at once. When both techniques are fused into one, DM-REPEAT method, the resulting charges become remarkably stable over a large set of fitting regions, giving a robust and physically sound solution to the buried atoms problem. The method capabilities are extensively studied in ZIF-8 framework, and subsequently applied to IRMOF-1 and ITQ-29 crystal structures. To our knowledge, this is the first time that this approach is proposed in the context of periodic systems.

A coarse-grained method based on the analysis of short molecular dynamics trajectories for the simulation of non-Markovian dynamics of molecules adsorbed in microporous materials.

We developed a coarse-grained model suitable for the study of adsorbed molecules in microporous materials. A partition of the space available to the motion of adsorbed molecules was carried out, which allows to formulate the dynamics in terms of jumps between discrete regions. The probabilities of observing given pairs of successive jumps were calculated from Molecular Dynamics (MD) simulations, performed on small systems, and used to drive the motion of molecules in a lattice-gas model. Dynamics is thus reformulated in terms of event-space dynamics and this allows to treat the system despite its inherent non markovity. Despite the assumptions enforced in the algorithm, results show that it can be applied to various spherical molecules adsorbed in the all-silica zeolite ITQ-29, establishing a suitable direct bridge between MD simulation results and coarse-grained models.

Speeding up simulation of diffusion in zeolites by a parallel synchronous kinetic Monte Carlo algorithm.

Understanding the behaviors of molecules in tight confinement is a challenging task. Standard simulation tools like kinetic Monte Carlo have proven to be very effective in the study of adsorption and diffusion phenomena in microporous materials, but they turn out to be very inefficient when simulation time and length scales are extended. In this paper we have explored the possibility of application of a discrete version of the synchronous parallel kinetic Monte Carlo algorithm introduced by Martínez et al. [J. Comput. Phys. 227, 3804 (2008)] to the study of aromatic hydrocarbons diffusion in zeolites. The efficiency of this algorithm is investigated as a function of the number of processors and domain size. We show that with an accurate choice of domains size it is possible to achieve very good efficiencies thus permitting us to effectively extend space and time scales of the simulated system.