Chemical features of melanoma tumor resident TRG CDR3s associated with better survival probabilities.

We assessed the T-cell receptor gamma (TRG) recombination reads from the cancer genome atlas melanoma tumor exome files and the TRG recombination reads from an independent, melanoma exome file dataset, from the Moffitt Cancer Center. TRG complementarity determining region-3 (CDR3) amino acid (AA) sequences were assessed for chemical complementarity to cancer testis antigens, with such complementarity for FAM133A and CRISP2 associated with better survival probabilities for both datasets. These results, along with related TRG CDR3 AA chemical feature assessments provided in this report, have indicated opportunities for melanoma patient stratifications based on the recovery of TRG recombination reads from both tumor and blood samples, and the results may point towards novel, effective melanoma antigens.

Direct Constraints on the Ultralight Boson Mass from Searches of Continuous Gravitational Waves.

Superradiance can trigger the formation of an ultralight boson cloud around a spinning black hole. Once formed, the boson cloud is expected to emit a nearly periodic, long-duration, gravitational-wave signal. For boson masses in the range (10^{-13}-10^{-11}) eV, and stellar mass black holes, such signals are potentially detectable by gravitational-wave detectors, like Advanced LIGO and Virgo. In this Letter, we present full band upper limits for a generic all-sky search for periodic gravitational waves in LIGO O2 data, and use them to derive-for the first time-direct constraints on the ultralight scalar boson field mass.

Automated voxel-by-voxel tissue classification for hippocampal segmentation: methods and validation.

The hippocampus is an important structural biomarker for Alzheimer's disease (AD) and has a primary role in the pathogenesis of other neurological and psychiatric diseases. This study presents a fully automated pattern recognition system for an accurate and reproducible segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI). The method was validated on a mixed cohort of 56 T1-weighted structural brain images, and consists of three processing levels: (a) Linear registration: all brain images were registered to a standard template and an automated method was applied to capture the global shape of the hippocampus. (b) Feature extraction: all voxels included in the previously selected volume were characterized by 315 features computed from local information. (c) Voxel classification: a Random Forest algorithm was used to classify voxels as belonging or not belonging to the hippocampus. In order to improve the classification performance, an adaptive learning method based on the use of the Pearson's correlation coefficient was developed. The segmentation results (Dice similarity index = 0.81 ± 0.03) compare well with other state-of-the art approaches. A validation study was conducted on an independent dataset of 100 T1-weighted brain images, achieving significantly better results than those obtained with FreeSurfer.

Generating a minimal set of templates for the hippocampal region in MR neuroimages.

OBJECTIVES: We detail a procedure for generating a set of templates for the hippocampal region in magnetic resonance (MR) images, representative of the clinical conditions of the population under investigation. METHODS: The first step is robust standardization of the intensity scale of brain MR images, belonging to patients with different degrees of neuropathology (Alzheimer's disease). So similar tissues have similar intensities, even across images coming from different sources. After the automatic extraction of the hippocampal region from a large dataset of images, we address template generation, choosing by clusterization methods a small number of the extracted regions. RESULTS: We assess that template generation is largely independent on the clusterization method and on the number and the clinical condition of the patients. The templates are chosen as the most representative images in a population. The estimation of the "minimum" number of templates for the hippocampal region can be proposed, using a metric based on the geometrical position of the extracted regions. CONCLUSIONS: This study describes a simple and easily reproducible procedure to generate templates for the hippocampal region. It can be generalized and applied to other brain regions, which may be relevant for neuroimaging studies.