Abstract: An optical system (LS) includes lenses (L22,L23) satisfying the following conditional expressions, ?dLZ<35.0, and 0.702<?gFLZ+(0.00316×?dLZ), where ?dLZ: Abbe number of the lens with reference to d-line, and ?gFLZ: a partial dispersion ratio of the lens, wherein ?gFLZ is defined by the following expression, ?gFLZ=(ngLZ?nFLZ)/(nFLZ?nCLZ). wherein a refractive index of the lens with reference to g-line is ngLZ, a refractive index of the lens with reference to F-line is nFLZ, and a refractive index of the lens with reference to C-line is nCLZ.

Abstract: An optical system (LS) has an aperture stop (S), and a negative lens (L73) disposed closer to the image side than the aperture stop (S) and satisfying the following conditional expressions. ?0.010<ndN2?(2.015?0.0068×?dN2), 50.00<?dN2<65.00, 0.545<?gFN2, ?0.010<?gFN2?(0.6418?0.00168×?dN2) where ndN2 is the refractive index to the d line of the negative lens, ?dN2 is the Abbe number with respect to the d line of the negative lens, and ?gFN2 is the partial dispersion ratio of the negative lens.

Abstract: There is provided a therapy selection support device that generates a discriminator (identifier) as a diagnostic marker or a classifier as a stratification marker through machine learning on the basis of measurement data on brain activities and that uses the discriminator or the classifier as a biomarker. A therapy selection support system 300a, 300b, 500 includes a clustering device 300b that executes stratification in which the results of measurement of brain functional connectivity correlation values acquired from a plurality of second subjects are divided into a plurality of clusters through a clustering process.

Abstract: A brain functional connectivity correlation value clustering device for clustering subjects having a prescribed attribute on the basis of brain measurement data obtained from a plurality of facilities, wherein a plurality of MRI devices capture resting state fMRI image data of a healthy cohort and a patient cohort; a computing system 300 performs generation of an identifier as ensemble learning of “supervised learning” between harmonized component values of correlation matrixes and disease labels of each of the subjects, selects, during the ensemble learning, features for clustering in accordance with importance from the features specified for generating an identifier for a disease label, and performs multiple co-clustering by “unsupervised learning.

Abstract: A harmonization system for a brain activity classifier harmonizing brain measurement data obtained at a plurality of sites to realize a discrimination process based on brain functional imaging: obtains data, for a plurality of traveling subjects as common objects of measurements at each of the plurality of measurement sites, resulting from measurements of brain activities of a predetermined plurality of brain regions of each of the traveling subjects; calculates, for each of the traveling subjects, prescribed elements of a brain functional connectivity matrix representing the temporal correlation of brain activities of a set of the plurality of brain regions; using a generalized linear mixed model, calculates measurement bias data 3108 for each element of the brain functional connectivity matrix, as a fixed effect at each measurement site with respect to an average of the corresponding element across the plurality of measurement sites and across the plurality of traveling subjects; and thereby executes a harm