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

17-AAG Induces Cytoplasmic α-Synuclein Aggregate Clearance by Induction of Autophagy

The accumulation and aggregation of α-synuclein in nerve cells and glia are characteristic features of a number of neurodegenerative diseases termed synucleinopathies. α-Synuclein is a highly soluble protein which in a nucleation dependent process is capable of self-aggregation. The causes underlying aggregate formation are not yet understood, impairment of the proteolytic degradation systems might be involved.Cl the aggregate clearing effects of 17-AAG were abolished and α-synuclein deposits were enlarged. Analysis of LC3-II immunoreactivity, which is an indicator of autophagosome formation, further revealed that 17-AAG led to the recruitment of LC3-II and to the formation of LC3 positive puncta. This effect was also observed in cultured oligodendrocytes derived from the brains of newborn rats. Inhibition of macroautophagy by 3-methyladenine prevented 17-AAG induced occurrence of LC3 positive puncta as well as the removal of α-synuclein aggregates in OLN-A53T cells.Our data demonstrate for the first time that 17-AAG not only causes the upregulation of heat shock proteins, but also is an effective inducer of the autophagic pathway by which α-synuclein can be removed. Hence geldanamycin derivatives may provide a means to modulate autophagy in neural cells, thereby ameliorating pathogenic aggregate formation and protecting the cells during disease and aging

An Atypical Riboflavin Pathway Is Essential for Brucella abortus Virulence

Brucellosis is a worldwide zoonosis that affects livestock and humans and is caused by closely related Brucella spp., which are adapted to intracellular life within cells of a large variety of mammals. Brucella can be considered a furtive pathogen that infects professional and non-professional phagocytes. In these cells Brucella survives in a replicative niche, which is characterized for having a very low oxygen tension and being deprived from nutrients such as amino acids and vitamins. Among these vitamins, we have focused on riboflavin (vitamin B2). Flavin metabolism has been barely implicated in bacterial virulence. We have recently described that Brucella and other Rhizobiales bear an atypical riboflavin metabolic pathway. In the present work we analyze the role of the flavin metabolism on Brucella virulence. Mutants on the two lumazine synthases (LS) isoenzymes RibH1 and RibH2 and a double RibH mutant were generated. These mutants and different complemented strains were tested for viability and virulence in cells and in mice. In this fashion we have established that at least one LS must be present for B. abortus survival and that RibH2 and not RibH1 is essential for intracellular survival due to its LS activity in vivo. In summary, we show that riboflavin biosynthesis is essential for Brucella survival inside cells or in mice. These results highlight the potential use of flavin biosynthetic pathway enzymes as targets for the chemotherapy of brucellosis

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

Nanostructure, osteopontin, and mechanical properties of calcitic avian eggshell

Avian (and formerly dinosaur) eggshells form a hard, protective biomineralized chamber for embryonic growth—an evolutionary strategy that has existed for hundreds of millions of years. We show in the calcitic chicken eggshell how the mineral and organic phases organize hierarchically across different length scales and how variation in nanostructure across the shell thicknessmodifies its hardness, elastic modulus, and dissolution properties.We also show that the nanostructure changes during egg incubation, weakening the shell for chick hatching. Nanostructure and increased hardness were reproduced in synthetic calcite crystals grown in the presence of the prominent eggshell protein osteopontin. These results demonstrate the contribution of nanostructure to avian eggshell formation, mechanical properties, and dissolution.This work was supported by a grant from the Canadian Institutes of Health Research (no. MOP-142330) and the Natural Sciences and Engineering Research Council of Canada (NSERC; no. RGPIN-2016-05031) to M.D.M., an NSERC (no. RGPIN-2016-04410) Discovery grant to M.T.H., a Spanish Government grant (CGL2015-64683-P) to A.B.R.-N., an Emmy Noether research grant from the German Research Foundation (no. WO1712/3-1) to S.E.W., and an NSF grant (NSF BMAT; no. 1507736) to J.J.G. M.D.M. is a member of the Fonds de Recherche Quebec–Sante Network for Oral and Bone Health Research and the McGill Centre for Bone and Periodontal Researc

Dwarf galaxy formation with H2-regulated star formation

We describe cosmological galaxy formation simulations with the adaptive mesh refinement code Enzo that incorporate a star formation prescription regulated by the local abundance of molecular hydrogen. We show that this H2-regulated prescription leads to a suppression of star formation in low mass halos (M_h < ~10^10 M_sun) at z>4, alleviating some of the dwarf galaxy problems faced by theoretical galaxy formation models. H2 regulation modifies the efficiency of star formation of cold gas directly, rather than indirectly reducing the cold gas content with "supernova feedback". We determine the local H2 abundance in our most refined grid cells (76 proper parsec in size at z=4) by applying the model of Krumholz, McKee, & Tumlinson, which is based on idealized 1D radiative transfer calculations of H2 formation-dissociation balance in ~100 pc atomic--molecular complexes. Our H2-regulated simulations are able to reproduce the empirical (albeit lower z) Kennicutt-Schmidt relation, including the low Sigma_gas cutoff due to the transition from atomic to molecular phase and the metallicity dependence thereof, without the use of an explicit density threshold in our star formation prescription. We compare the evolution of the luminosity function, stellar mass density, and star formation rate density from our simulations to recent observational determinations of the same at z=4-8 and find reasonable agreement between the two.Comment: replaced with version published in Ap

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