Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Purpose:To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods:Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. Results:The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R2 = 0.49, P < 0.0001) and FDT visual field testing (R2 = 0.48, P < 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R2 = 0.58, P < 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively). Conclusions:A computational approach can identify structural features that improve glaucoma detection and progression prediction

Hund's rule and metallic ferromagnetism

We study tight-binding models of itinerant electrons in two different bands, with effective on-site interactions expressing Coulomb repulsion and Hund's rule. We prove that, for sufficiently large on-site exchange anisotropy, all ground states show metallic ferromagnetism: They exhibit a macroscopic magnetization, a macroscopic fraction of the electrons is spatially delocalized, and there is no energy gap for kinetic excitations.Comment: 17 page

Rigorous proof of Luttinger liquid behavior in the 1d Hubbard model

We give the first rigorous (non perturbative) proof of Luttinger liquid behavior in the one dimensional Hubbard model, for small repulsive interaction and values of the density different from half filling. The analysis is based on the combination of multiscale analysis with Ward identities bases on a hidden and approximate local chiral gauge invariance. No use is done of exact solutions or special integrability properties of the Hubbard model, and the results can be in fact easily generalized to include non local interactions, magnetic fields or interaction with external potential

Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers

Purpose: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. Methods: FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. Results: FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G(1) and G(2) combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G(1) and G(2) the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. Conclusions: VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.open0

Radiative emission of solar features in the Ca II K line: comparison of measurements and models

We study the radiative emission of various types of solar features, such as quiet Sun, enhanced network, plage, and bright plage regions, identified on filtergrams taken in the Ca II K line. We analysed fulldisk images obtained with the PSPT, by using three interference filters that sample the Ca II K line with different bandpasses. We studied the dependence of the radiative emission of disk features on the filter bandpass. We also performed a NLTE spectral synthesis of the Ca II K line integrated over the bandpass of PSPT filters. The synthesis was carried out by utilizing both the PRD and CRD with the most recent set of semi empirical atmosphere models in the literature and some earlier atmosphere models. We measured the CLV of intensity values for various solar features identified on PSPT images and compared the results obtained with those derived from the synthesis. We find that CRD calculations derived using the most recent quiet Sun model, on average, reproduce the measured values of the quiet Sun regions slightly more accurately than PRD computations with the same model. This may reflect that the utilized atmospheric model was computed assuming CRD. Calculations with PRD on earlier quiet Sun model atmospheres reproduce measured quantities with a similar accuracy as to that achieved here by applying CRD to the recent model. We also find that the median contrast values measured for most of the identified bright features, disk positions, and filter widths are, on average, a factor 1.9 lower than those derived from PRD simulations performed using the recent bright feature models. The discrepancy between measured and modeled values decreases by 12% after taking into account straylight effects on PSPT images. PRD computations on either the most recent or the earlier atmosphere models of bright features reproduce measurements from plage and bright plage regions with a similar accuracy.Comment: 14 pages, 18 figures, accepted by A&

FAS-dependent cell death in α-synuclein transgenic oligodendrocyte models of multiple system atrophy

Multiple system atrophy is a parkinsonian neurodegenerative disorder. It is cytopathologically characterized by accumulation of the protein p25α in cell bodies of oligodendrocytes followed by accumulation of aggregated α-synuclein in so-called glial cytoplasmic inclusions. p25α is a stimulator of α-synuclein aggregation, and coexpression of α-synuclein and p25α in the oligodendroglial OLN-t40-AS cell line causes α-synuclein aggregate-dependent toxicity. In this study, we investigated whether the FAS system is involved in α-synuclein aggregate dependent degeneration in oligodendrocytes and may play a role in multiple system atrophy. Using rat oligodendroglial OLN-t40-AS cells we demonstrate that the cytotoxicity caused by coexpressing α-synuclein and p25α relies on stimulation of the death domain receptor FAS and caspase-8 activation. Using primary oligodendrocytes derived from PLP-α-synuclein transgenic mice we demonstrate that they exist in a sensitized state expressing pro-apoptotic FAS receptor, which makes them sensitive to FAS ligand-mediated apoptosis. Immunoblot analysis shows an increase in FAS in brain extracts from multiple system atrophy cases. Immunohistochemical analysis demonstrated enhanced FAS expression in multiple system atrophy brains notably in oligodendrocytes harboring the earliest stages of glial cytoplasmic inclusion formation. Oligodendroglial FAS expression is an early hallmark of oligodendroglial pathology in multiple system atrophy that mechanistically may be coupled to α-synuclein dependent degeneration and thus represent a potential target for protective intervention

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fundus photographs (n = 14,822) from a racially and ethnically diverse group of individuals (over 33% of African descent) was evaluated by expert reviewers and classified as GON or healthy. Several deep learning architectures and the impact of transfer learning were evaluated. The best performing model achieved an overall area under receiver operating characteristic (AUC) of 0.91 in distinguishing GON eyes from healthy eyes. It also achieved an AUC of 0.97 for identifying GON eyes with moderate-to-severe functional loss and 0.89 for GON eyes with mild functional loss. A sensitivity of 88% at a set 95% specificity was achieved in detecting moderate-to-severe GON. In all cases, transfer improved performance and reduced training time. Model visualizations indicate that these deep learning models relied on, in part, anatomical features in the inferior and superior regions of the optic disc, areas commonly used by clinicians to diagnose GON. The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs

Interstellar MHD Turbulence and Star Formation

This chapter reviews the nature of turbulence in the Galactic interstellar medium (ISM) and its connections to the star formation (SF) process. The ISM is turbulent, magnetized, self-gravitating, and is subject to heating and cooling processes that control its thermodynamic behavior. The turbulence in the warm and hot ionized components of the ISM appears to be trans- or subsonic, and thus to behave nearly incompressibly. However, the neutral warm and cold components are highly compressible, as a consequence of both thermal instability in the atomic gas and of moderately-to-strongly supersonic motions in the roughly isothermal cold atomic and molecular components. Within this context, we discuss: i) the production and statistical distribution of turbulent density fluctuations in both isothermal and polytropic media; ii) the nature of the clumps produced by thermal instability, noting that, contrary to classical ideas, they in general accrete mass from their environment; iii) the density-magnetic field correlation (or lack thereof) in turbulent density fluctuations, as a consequence of the superposition of the different wave modes in the turbulent flow; iv) the evolution of the mass-to-magnetic flux ratio (MFR) in density fluctuations as they are built up by dynamic compressions; v) the formation of cold, dense clouds aided by thermal instability; vi) the expectation that star-forming molecular clouds are likely to be undergoing global gravitational contraction, rather than being near equilibrium, and vii) the regulation of the star formation rate (SFR) in such gravitationally contracting clouds by stellar feedback which, rather than keeping the clouds from collapsing, evaporates and diperses them while they collapse.Comment: 43 pages. Invited chapter for the book "Magnetic Fields in Diffuse Media", edited by Elisabete de Gouveia dal Pino and Alex Lazarian. Revised as per referee's recommendation