Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients.

Objective: Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations.

Methods: We evaluated linear machine learning (ML) methods' ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma.

Traceable Reference Full Metrology Chain for Innovative Aspheric and Freeform Optical Surfaces Accurate at the Nanometer Level.

The design of innovative reference aspheric and freeform optical elements was investigated with the aim of calibration and verification of ultra-high accurate measurement systems. The verification is dedicated to form error analysis of aspherical and freeform optical surfaces based on minimum zone fitting. Two thermo-invariant material measures were designed, manufactured using a magnetorheological finishing process and selected for the evaluation of a number of ultra-high-precision measurement machines.

Healthcare attendance styles among long-term unemployed people with substance-related and mood disorders.

Background: Both increased and decreased health service usage and unmet care needs are more prevalent among unemployed people than in the general population.

Study Design: This study investigates the associations of substance-related and mood disorders among long-term unemployed people with styles of healthcare attendance in Finland.

Methods: The study material consisted of the health register information on 498 long-term unemployed people in a project screening for work disabilities.

Identification of Depression and Screening for Work Disabilities among Long-Term Unemployed People.

The study explores whether clinical screening targeted at work disabilities among long-term unemployed people reveals eligible individuals for a disability pension and the importance of depression in granting the disability pensions. A total of 364 participants of the screening project were considered as eligible to apply for disability pension. Among them, 188 were diagnosed as clinically depressed.

Spectral Reflectance Estimation Using Gaussian Processes and Combination Kernels.

This paper explores hyperspectral reflectance factor estimation using Gaussian process regression with multispectral- and trichromatic measurements. Estimations are performed in visible- (400-700 nm) or visible-near infrared (400-980 nm) wavelength ranges using the learning-based approach, where sensor and light spectral characteristics are not required. We first construct new estimation models via Gaussian processes, show connection to previous kernel-based models, and then evaluate new models by using marginal likelihood optimization within the probabilistic interpretation.