Population genomics reveals that within-fungus polymorphism is common and maintained in populations of the mycorrhizal fungus Rhizophagus irregularis.

Arbuscular mycorrhizal (AM) fungi are symbionts of most plants, increasing plant growth and diversity. The model AM fungus Rhizophagus irregularis (isolate DAOM 197198) exhibits low within-fungus polymorphism. In contrast, another study reported high within-fungus variability. Experiments with other R. irregularis isolates suggest that within-fungus genetic variation can affect the fungal phenotype and plant growth, highlighting the biological importance of such variation. We investigated whether there is evidence of differing levels of within-fungus polymorphism in an R. irregularis population. We genotyped 20 isolates using restriction site-associated DNA sequencing and developed novel approaches for characterizing polymorphism among haploid nuclei. All isolates exhibited higher within-isolate poly-allelic single-nucleotide polymorphism (SNP) densities than DAOM 197198 in repeated and non-repeated sites mapped to the reference genome. Poly-allelic SNPs were independently confirmed. Allele frequencies within isolates deviated from diploids or tetraploids, or that expected for a strict dikaryote. Phylogeny based on poly-allelic sites was robust and mirrored the standard phylogeny. This indicates that within-fungus genetic variation is maintained in AM fungal populations. Our results predict a heterokaryotic state in the population, considerable differences in copy number variation among isolates and divergence among the copies, or aneuploidy in some isolates. The variation may be a combination of all of these hypotheses. Within-isolate genetic variation in R. irregularis leads to large differences in plant growth. Therefore, characterizing genomic variation within AM fungal populations is of major ecological importance

Measures and Limits of Models of Fixation Selection

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection . We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced

Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual Attention

Visual attention is thought to be driven by the interplay between low-level visual features and task dependent information content of local image regions, as well as by spatial viewing biases. Though dependent on experimental paradigms and model assumptions, this idea has given rise to varying claims that either bottom-up or top-down mechanisms dominate visual attention. To contribute toward a resolution of this discussion, here we quantify the influence of these factors and their relative importance in a set of classification tasks. Our stimuli consist of individual image patches (bubbles). For each bubble we derive three measures: a measure of salience based on low-level stimulus features, a measure of salience based on the task dependent information content derived from our subjects' classification responses and a measure of salience based on spatial viewing biases. Furthermore, we measure the empirical salience of each bubble based on our subjects' measured eye gazes thus characterizing the overt visual attention each bubble receives. A multivariate linear model relates the three salience measures to overt visual attention. It reveals that all three salience measures contribute significantly. The effect of spatial viewing biases is highest and rather constant in different tasks. The contribution of task dependent information is a close runner-up. Specifically, in a standardized task of judging facial expressions it scores highly. The contribution of low-level features is, on average, somewhat lower. However, in a prototypical search task, without an available template, it makes a strong contribution on par with the two other measures. Finally, the contributions of the three factors are only slightly redundant, and the semi-partial correlation coefficients are only slightly lower than the coefficients for full correlations. These data provide evidence that all three measures make significant and independent contributions and that none can be neglected in a model of human overt visual attention

What is the role of the film viewer? The effects of narrative comprehension and viewing task on gaze control in film

Film is ubiquitous, but the processes that guide viewers' attention while viewing film narratives are poorly understood. In fact, many film theorists and practitioners disagree on whether the film stimulus (bottom-up) or the viewer (top-down) is more important in determining how we watch movies. Reading research has shown a strong connection between eye movements and comprehension, and scene perception studies have shown strong effects of viewing tasks on eye movements, but such idiosyncratic top-down control of gaze in film would be anathema to the universal control mainstream filmmakers typically aim for. Thus, in two experiments we tested whether the eye movements and comprehension relationship similarly held in a classic film example, the famous opening scene of Orson Welles' Touch of Evil (Welles & Zugsmith, Touch of Evil, 1958). Comprehension differences were compared with more volitionally controlled task-based effects on eye movements. To investigate the effects of comprehension on eye movements during film viewing, we manipulated viewers' comprehension by starting participants at different points in a film, and then tracked their eyes. Overall, the manipulation created large differences in comprehension, but only produced modest differences in eye movements. To amplify top-down effects on eye movements, a task manipulation was designed to prioritize peripheral scene features: a map task. This task manipulation created large differences in eye movements when compared to participants freely viewing the clip for comprehension. Thus, to allow for strong, volitional top-down control of eye movements in film, task manipulations need to make features that are important to narrative comprehension irrelevant to the viewing task. The evidence provided by this experimental case study suggests that filmmakers' belief in their ability to create systematic gaze behavior across viewers is confirmed, but that this does not indicate universally similar comprehension of the film narrative

Gray zones around diffuse large B cell lymphoma. Conclusions based on the workshop of the XIV meeting of the European Association for Hematopathology and the Society of Hematopathology in Bordeaux, France

The term “gray-zone” lymphoma has been used to denote a group of lymphomas with overlapping histological, biological, and clinical features between various types of lymphomas. It has been used in the context of Hodgkin lymphomas (HL) and non-Hodgkin lymphomas (NHL), including classical HL (CHL), and primary mediastinal large B cell lymphoma, cases with overlapping features between nodular lymphocyte predominant Hodgkin lymphoma and T-cell/histiocyte-rich large B cell lymphoma, CHL, and Epstein–Barr-virus-positive lymphoproliferative disorders, and peripheral T cell lymphomas simulating CHL. A second group of gray-zone lymphomas includes B cell NHL with intermediate features between diffuse large B cell lymphoma and classical Burkitt lymphoma. In order to review controversial issues in gray-zone lymphomas, a joint Workshop of the European Association for Hematopathology and the Society for Hematopathology was held in Bordeaux, France, in September 2008. The panel members reviewed and discussed 145 submitted cases and reached consensus diagnoses. This Workshop summary is focused on the most controversial aspects of gray-zone lymphomas and describes the panel’s proposals regarding diagnostic criteria, terminology, and new prognostic and diagnostic parameters

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Scenes, saliency maps and scanpaths

The aim of this chapter is to review some of the key research investigating how people look at pictures. In particular, my goal is to provide theoretical background for those that are new to the field, while also explaining some of the relevant methods and analyses. I begin by introducing eye movements in the context of natural scene perception. As in other complex tasks, eye movements provide a measure of attention and information processing over time, and they tell us about how the foveated visual system determines what to prioritise. I then describe some of the many measures which have been derived to summarize where people look in complex images. These include global measures, analyses based on regions of interest and comparisons based on heat maps. A particularly popular approach for trying to explain fixation locations is the saliency map approach, and the first half of the chapter is mostly devoted to this topic. A large number of papers and models are built on this approach, but it is also worth spending time on this topic because the methods involved have been used across a wide range of applications. The saliency map approach is based on the fact that the visual system has topographic maps of visual features, that contrast within these features seems to be represented and prioritized, and that a central representation can be used to control attention and eye movements. This approach, and the underlying principles, has led to an increase in the number of researchers using complex natural scenes as stimuli. It is therefore important that those new to the field are familiar with saliency maps, their usage, and their pitfalls. I describe the original implementation of this approach (Itti & Koch, 2000), which uses spatial filtering at different levels of coarseness and combines them in an attempt to identify the regions which stand out from their background. Evaluating this model requires comparing fixation locations to model predictions. Several different experimental and comparison methods have been used, but most recent research shows that bottom-up guidance is rather limited in terms of predicting real eye movements. The second part of the chapter is largely concerned with measuring eye movement scanpaths. Scanpaths are the sequential patterns of fixations and saccades made when looking at something for a period of time. They show regularities which may reflect top-down attention, and some have attempted to link these to memory and an individual’s mental model of what they are looking at. While not all researchers will be testing hypotheses about scanpaths, an understanding of the underlying methods and theory will be of benefit to all. I describe the theories behind analyzing eye movements in this way, and various methods which have been used to represent and compare them. These methods allow one to quantify the similarity between two viewing patterns, and this similarity is linked to both the image and the observer. The last part of the chapter describes some applications of eye movements in image viewing. The methods discussed can be applied to complex images, and therefore these experiments can tell us about perception in art and marketing, as well as about machine vision

Hypergraph Optimization for Salient Region Detection Based on Foreground and Background Queries

Graph-based methods have been widely adopted to detect salient objects in images. However, there are two limitations of these methods. First, only one kind of query is employed for saliency propagation on the graph. Second, these methods only represent pairwise relations between vertices and thus give an incomplete representation of the relationships between image regions. In this paper, we propose a foreground- and background-queries-based hypergraph optimization framework for salient region detection. In this framework, both foreground queries and background queries are explicitly exploited to uniformly highlight the salient foreground and suppress the non-salient background. Furthermore, to include both the pairwise and the higher-order relations among two or more vertices, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among image regions from different views. Extensive experimental results demonstrate the effectiveness of the proposed framework