Proper morphisms of ∞\infty-topoi

We characterise proper morphisms of $\infty$-topoi in terms of a relativised notion of compactness: we show that a geometric morphism of $\infty$-topoi is proper if and only if it commutes with colimits indexed by filtered internal $\infty$-categories in the target. In particular, our result implies that for any $\infty$-topos, the global sections functor is proper if and only if it preserves filtered colimits. As an application, we show that every proper and separated map of topological spaces gives rise to a proper morphism between the associated sheaf $\infty$-topoi, generalising a result of Lurie. Along the way, we develop some aspects of the theory of localic higher topoi internal to an $\infty$-topos, which might be of independent interest.Comment: 48 pages, comments very welcom

Network analysis of genes regulated in renal diseases: implications for a molecular-based classification

Abstract Background Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases. Results The analysis revealed three main regularities: (1) Many genes associated with a single disease, and fewer genes associated with many diseases. (2) Unexpected combinations of renal diseases that share relatively large numbers of genes. (3) Uniform concordance in the regulation of all genes in the network. Conclusion The overall results suggest the need to define a molecular-based classification of renal diseases, in addition to hypotheses for the unexpected patterns of shared genes and the uniformity in gene concordance. Furthermore, the results demonstrate the utility of network analyses to rapidly understand complex relationships between diseases and regulated genes.http://deepblue.lib.umich.edu/bitstream/2027.42/112463/1/12859_2009_Article_3354.pd

Comparability of Pulmonary Nodule Size Measurements among Different Scanners and Protocols: Should Diameter Be Favorized over Volume?

BACKGROUND: To assess the impact of the lung cancer screening protocol recommended by the European Society of Thoracic Imaging (ESTI) on nodule diameter, volume, and density throughout different computed tomography (CT) scanners. METHODS: An anthropomorphic chest phantom containing fourteen different-sized (range 3-12 mm) and CT-attenuated (100 HU, -630 HU and -800 HU, termed as solid, GG1 and GG2) pulmonary nodules was imaged on five CT scanners with institute-specific standard protocols (P$_{S}$) and the lung cancer screening protocol recommended by ESTI (ESTI protocol, P$_{E}$). Images were reconstructed with filtered back projection (FBP) and iterative reconstruction (REC). Image noise, nodule density and size (diameter/volume) were measured. Absolute percentage errors (APEs) of measurements were calculated. RESULTS: Using P$_{E}$, dosage variance between different scanners tended to decrease compared to P$_{S}$, and the mean differences were statistically insignificant (p = 0.48). P$_{S}$ and P$_{E(REC)}$ showed significantly less image noise than P$_{E(FBP)}$ (p < 0.001). The smallest size measurement errors were noted with volumetric measurements in P$_{E(REC)}$ and highest with diametric measurements in P$_{E(FBP)}$. Volume performed better than diameter measurements in solid and GG1 nodules (p < 0.001). However, in GG2 nodules, this could not be observed (p = 0.20). Regarding nodule density, REC values were more consistent throughout different scanners and protocols. CONCLUSION: Considering radiation dose, image noise, nodule size, and density measurements, we fully endorse the ESTI screening protocol including the use of REC. For size measurements, volume should be preferred over diameter

A common framework for the evaluation of psychophysiological visual quality assessment

The assessment of perceived quality based on psychophysiological methods recently gained attraction as it potentiallyovercomes certain flaws of psychophysical approaches. Although studies report promising results, it is not possible toarrive at decisive and comparable conclusions that recommend the use of one or another method for a specific applicationor research question. The video quality expert group started a project on psychophysiological quality assessment to studythese novel approaches and to develop a test plan that enables more systematic research. This test plan comprises of a specificallydesigned set of quality annotated video sequences, suggestions for psychophysiological methods to be studied inquality assessment, and recommendations for the documentation and publications of test results. The test plan is presentedin this article.Celtc-Next 5G Perfecta (2018-00735

Lung Cancer Screening with Submillisievert Chest CT: Potential Pitfalls of Pulmonary Findings in Different Readers with Various Experience Levels

Purpose To assess the interreader variability of submillisievert CT for lung cancer screening in radiologists with various experience levels. Method Six radiologists with different degrees of clinical experience in radiology (range, 1-15 years), rated 100 submillisievert CT chest studies as either negative screening finding (no nodules, benign nodules, nodules 10 mm). Each radiologist interpreted scans randomly ordered and reading time was recorded. Interobserver agreement was assessed with ak statistic. Reasons for differences in nodule classification were analysed on a case-by-case basis. Reading time was correlated with reader experience using Pearson correlation (r). Results The overall interobserver agreement between all readers was moderate (k = 0.454; p < 0.001). In 57 patients, all radiologists agreed on the differentiation of negative and indeterminate/positive finding. In 64 cases disagreement between readers led to different nodule classification. In 8 cases some readers rated the nodule as benign, whereas others scored the case as positive. Overall, disagreement in nodule classification was mostly due to failure in identification of target lesion (n = 40), different lesion measurement (n = 44) or different classification (n = 26). Mean overall reading time per scan was of 2 min 2 s (range: 7s-7 min 45 s) and correlated with reader-experience (r =-0.824). Conclusions Our study showed substantial interobserver variability for the detection and classification of pulmonary nodules in submillisievert CT. This highlights the importance for careful standardisation of screening programs with the objective of harmonizing efforts of involved radiologists across different institutions by defining and assuring quality standards

Phantom epistasis in genomic selection: on the predictive ability of epistatic models

Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density (“Phantom Epistasis”). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.Fil: Schrauf, Matías Florián. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Cátedra de Mejoramiento Genético Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martini, Johannes W.R.. Centro Internacional de Mejoramiento de Maíz y Trigo; MéxicoFil: Simianer, Henner. Universität Göttingen; AlemaniaFil: de los Campos, Gustavo. Michigan State University; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Unidad Ejecutora de Investigaciones en Producción Animal. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Unidad Ejecutora de Investigaciones en Producción Animal; ArgentinaFil: Freudenthal, Jan. Universität Würzburg; AlemaniaFil: Korte, Arthur. Universität Würzburg; AlemaniaFil: Munilla Leguizamon, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Cátedra de Mejoramiento Genético Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans

PURPOSE To assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection. METHODS Ninety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans. RESULTS Nodule volumes ranged from 58.5 (28.8-126) mm$^{3}$ for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39-134) mm$^{3}$ for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 - 0.882). CONCLUSIONS The interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed

DEMI : deep video quality estimation model using perceptual video quality dimensions

Existing works in the field of quality assessment focus separately on gaming and non-gaming content. Along with the traditional modeling approaches, deep learning based approaches have been used to develop quality models, due to their high prediction accuracy. In this paper, we present a deep learning based quality estimation model considering both gaming and non-gaming videos. The model is developed in three phases. First, a convolutional neural network (CNN) is trained based on an objective metric which allows the CNN to learn video artifacts such as blurriness and blockiness. Next, the model is fine-tuned based on a small image quality dataset using blockiness and blurriness ratings. Finally, a Random Forest is used to pool frame-level predictions and temporal information of videos in order to predict the overall video quality. The light-weight, low complexity nature of the model makes it suitable for real-time applications considering both gaming and non-gaming content while achieving similar performance to existing state-of-the-art model NDNetGaming. The model implementation for testing is available on GitHub