Volcanostratigraphic Controls on the Occurrence of Massive Sulfide (VMS) Deposits in the Semail Ophiolite, Oman

The Semail ophiolite in Oman is capped by up to 2 km of basaltic-andesitic lavas that host copper-dominant, Cyprus-type, volcanogenic massive sulfide (VMS) deposits. This study identifies multiple volcanostratigraphic horizons on which the deposits are situated, based on characterization of footwall and hanging-wall lavas from 16 deposits or deposit clusters. Comparison of field and petrographic features, compositions of igneous clinopyroxenes, and whole-rock geochemical signatures permits classification of the lavas within a modified version of the established regional volcanostratigraphy. Four extrusive units host deposits: Geotimes (earliest), Lasail, Alley, and Boninitic Alley (latest). The latter was previously known only at few localities, but this study reveals its regional extent and significance as a host for VMS deposits. The Geotimes and Lasail units represent Late Cretaceous, ocean spreading ridge and related off-axis volcanic environments, respectively. The Alley and Boninitic Alley units represent younger, subduction-related volcanism prior to Coniacian-Santonian obduction of the ophiolite. Our results show that VMS deposits occur on or near the Geotimes/Lasail and Geotimes/Alley contacts as well as entirely within the Geotimes, Lasail, Alley, and Boninitic Alley units. Highest Cu grades tend to occur in deposits lying on or within the Geotimes, whereas highest Au grades occur in deposits within the Boninitic Alley. In contrast to earlier studies, we conclude that essentially every horizon marking a hiatus in lava deposition in the Semail ophiolite, i.e., contacts between the four major eruptive units, and umbers and sedimentary chert layers within the units, has exploration potential for Cu-Au VMS deposits.

Prevention of increased abnormal fundus autofluorescence with blue light-filtering intraocular lenses

PURPOSE To observe changes in fundus autofluorescence 2 years after implantation of blue light-filtering (yellow-tinted) and ultraviolet light-filtering (colorless) intraocular lenses (IOLs). SETTING Department of Ophthalmology and Visual Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan, and the Department of Ophthalmology, University of Bern, Bern, Switzerland. DESIGN Prospective comparative observational study. METHODS Patients were enrolled who had cataract surgery with implantation of a yellow-tinted or colorless IOL and for whom images were obtained on which the fundus autofluorescence was measurable using the Heidelberg Retina Angiogram 2 postoperatively. The fundus autofluorescence in the images was classified into 8 abnormal patterns based on the classification of the International Fundus Autofluorescence Classification Group, The presence of normal fundus autofluorescence, geographic atrophy, and wet age-related macular degeneration (AMD) also was recorded. The fundus findings at baseline and 2 years postoperatively were compared. RESULTS Fifty-two eyes with a yellow-tinted IOL and 79 eyes with a colorless IOL were included. Abnormal fundus autofluorescence did not develop or increase in the yellow-tinted IOL group; however, progressive abnormal fundus autofluorescence developed or increased in 12 eyes (15.2%) in the colorless IOL group (P = .0016). New drusen, geographic atrophy, and choroidal neovascularization were observed mainly in the colorless IOL group. The incidence of AMD was statistically significantly higher in the colorless IOL group (P = .042). CONCLUSIONS Two years after cataract surgery, significant differences were seen in the progression of abnormal fundus autofluorescence between the 2 groups. The incidence of AMD was lower in eyes with a yellow-tinted IOL. FINANCIAL DISCLOSURE No author has a financial or proprietary interest in any material or method mentioned.

Pathology: Classification and Immunoprofile

The classification of neuroendocrine neoplasms (NENs) has been evolving steadily over the last decades. Important prognostic factors of NENs are their proliferative activity and presence/absence of necrosis. These factors are reported in NENs of all body sites; however, the terminology as well as the exact rules of classification differ according to the location of the primary tumor. Only in gastroenteropancreatic (GEP) NENs a formal grading is performed. This grading is based on proliferation assessed by the mitotic count and/or Ki-67 proliferation index. In the lung, NEN grading is an intrinsic part of the tumor designation with typical carcinoids corresponding to neuroendocrine tumor (NET) G1 and atypical carcinoids to NET G2; however, the presence or absence of necrotic foci is as important as proliferation for the differentiation between typical and atypical carcinoids. Immunohistochemical markers can be used to demonstrate neuroendocrine differentiation. Synaptophysin and chromogranin A are, to date, the most reliable and most commonly used for this purpose. Beyond this, other markers can be helpful, for example in the situation of a NET metastasis of unknown primary, where a hormonal profile or a panel of transcription factors can give hints to the primary site. Many immunohistochemical markers have been shown to correlate with prognosis but are not used in clinical practice, for example cytokeratin 19 and KIT expression in pancreatic NETs. There is no predictive biomarker in use, with the exception of somatostatin receptor (SSTR) 2 expression for predicting the amenability of a tumor to in vivo SSTR targeting for imaging or therapy.

Analysis of Phenotypic Variation in Childhood Wheezing Disorders

Recurrent wheezing or asthma is a common problem in children that has increased considerably in prevalence in the past few decades. The causes and underlying mechanisms are poorly understood and it is thought that a numb er of distinct diseases causing similar symptoms are involved. Due to the lack of a biologically founded classification system, children are classified according to their observed disease related features (symptoms, signs, measurements) into phenotypes. The objectives of this PhD project were a) to develop tools for analysing phenotypic variation of a disease, and b) to examine phenotypic variability of wheezing among children by applying these tools to existing epidemiological data. A combination of graphical methods (multivariate co rrespondence analysis) and statistical models (latent variables models) was used. In a first phase, a model for discrete variability (latent class model) was applied to data on symptoms and measurements from an epidemiological study to identify distinct phenotypes of wheezing. In a second phase, the modelling framework was expanded to include continuous variability (e.g. along a severity gradient) and combinations of discrete and continuo us variability (factor models and factor mixture models). The third phase focused on validating the methods using simulation studies. The main body of this thesis consists of 5 articles (3 published, 1 submitted and 1 to be submitted) including applications, methodological contributions and a review. The main findings and contributions were: 1) The application of a latent class model to epidemiological data (symptoms and physiological measurements) yielded plausible pheno types of wheezing with distinguishing characteristics that have previously been used as phenotype defining characteristics. 2) A method was proposed for including responses to conditional questions (e.g. questions on severity or triggers of wheezing are asked only to children with wheeze) in multivariate modelling.ii 3) A panel of clinicians was set up to agree on a plausible model for wheezing diseases. The model can be used to generate datasets for testing the modelling approach. 4) A critical review of methods for defining and validating phenotypes of wheeze in children was conducted. 5) The simulation studies showed that a parsimonious parameterisation of the models is required to identify the true underlying structure of the data. The developed approach can deal with some challenges of real-life cohort data such as variables of mixed mode (continuous and categorical), missing data and conditional questions. If carefully applied, the approach can be used to identify whether the underlying phenotypic variation is discrete (classes), continuous (factors) or a combination of these. These methods could help improve precision of research into causes and mechanisms and contribute to the development of a new classification of wheezing disorders in children and other diseases which are difficult to classify.

PDB-Explorer: a web-based interactive map of the protein data bank in shape space

Background The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited. Methods A 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space. Results The 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups. Conclusion A chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.​cheminfo.​org/​pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.