The stability of S-States of Unit-Charge Coulomb three-body systems: From H- TO

High accuracy non-relativistic quantum chemical calculations of the ground state energies and wavefunctions of symmetric three-particle Coulomb systems of the form {m ± 1 m ± 2 m ∓ 3 } , m 1 = m 2, are calculated using an efficient and effective series solution method in a triple orthogonal Laguerre basis set. These energies are used to determine an accurate lower bound to the stability zone of unit-charge three-particle Coulomb systems using an expression for the width of the stability band in terms of g, the fractional additional binding due to a third particle. The results are presented in the form of a reciprocal mass fraction ternary diagram and the energies used to derive a parameterised function g(a 3), where a 3 =m −1 3 /(m −1 1 +m −1 2 +m −1 3 ) is the reciprocal mass of the uniquely charged particle. It is found that the function is not minimal at a 3 = 0 which corresponds to ∞H− nor is it minimal at the positronium negative ion (Ps−) the system with the least absolute energetic gain by association with a third particle; the function g(a 3) is minimal at m 1/m 3 = 0.49, and a possible physical interpretation in terms of the transition from atomic-like to molecular-like is provided

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.</p

Multilevel Modeling in Psychosomatic Medicine Research

The primary purpose of this manuscript is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The manuscript begins with a general introduction to multilevel modeling. Multilevel regression modeling at two-levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated datasets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including: communication of model specification, parameter interpretation, sample size and power, and missing data. Input and key output files from Mplus and SAS are provided. A cluster randomized trial with repeated measures (i.e., three-level regression model) is then briefly presented with simulated data based on some core ideas from a cognitive behavioral stress management intervention in prostate cancer