Differentiation between rebound thymic hyperplasia and thymic relapse after chemotherapy in pediatric Hodgkin lymphoma.

BACKGROUND: Rebound thymic hyperplasia (RTH) is a common phenomenon caused by stress factors such as chemotherapy (CTX) or radiotherapy, with an incidence between 44% and 67.7% in pediatric lymphoma. Misinterpretation of RTH and thymic lymphoma relapse (LR) may lead to unnecessary diagnostic procedures including invasive biopsies or treatment intensification. The aim of this study was to identify parameters that differentiate between RTH and thymic LR in the anterior mediastinum. METHODS: After completion of CTX, we analyzed computed tomographies (CTs) and magnetic resonance images (MRIs) of 291 patients with classical Hodgkin lymphoma (CHL) and adequate imaging available from the European Network for Pediatric Hodgkin lymphoma C1 trial. In all patients with biopsy-proven LR, an additional fluorodeoxyglucose (FDG)-positron emission tomography (PET)-CT was assessed. Structure and morphologic configuration in addition to calcifications and presence of multiple masses in the thymic region and signs of extrathymic LR were evaluated. RESULTS: After CTX, a significant volume increase of new or growing masses in the thymic space occurred in 133 of 291 patients. Without biopsy, only 98 patients could be identified as RTH or LR. No single finding related to thymic regrowth allowed differentiation between RTH and LR. However, the vast majority of cases with thymic LR presented with additional increasing tumor masses (33/34). All RTH patients (64/64) presented with isolated thymic growth. CONCLUSION: Isolated thymic LR is very uncommon. CHL relapse should be suspected when increasing tumor masses are present in distant sites outside of the thymic area. Conversely, if regrowth of lymphoma in other sites can be excluded, isolated thymic mass after CTX likely represents RTH.

Diverse Deep Neural Networks All Predict Human Inferior Temporal Cortex Well, After Training and Fitting.

Deep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual cortex. What remains unclear is how strongly experimental choices, such as network architecture, training, and fitting to brain data, contribute to the observed similarities. Here, we compare a diverse set of nine DNN architectures on their ability to explain the representational geometry of 62 object images in human inferior temporal cortex (hIT), as measured with fMRI. We compare untrained networks to their task-trained counterparts and assess the effect of cross-validated fitting to hIT, by taking a weighted combination of the principal components of features within each layer and, subsequently, a weighted combination of layers. For each combination of training and fitting, we test all models for their correlation with the hIT representational dissimilarity matrix, using independent images and subjects. Trained models outperform untrained models (accounting for 57% more of the explainable variance), suggesting that structured visual features are important for explaining hIT. Model fitting further improves the alignment of DNN and hIT representations (by 124%), suggesting that the relative prevalence of different features in hIT does not readily emerge from the Imagenet object-recognition task used to train the networks. The same models can also explain the disparate representations in primary visual cortex (V1), where stronger weights are given to earlier layers. In each region, all architectures achieved equivalently high performance once trained and fitted. The models' shared properties-deep feedforward hierarchies of spatially restricted nonlinear filters-seem more important than their differences, when modeling human visual representations.

Correction: Inferring exemplar discriminability in brain representations.

[This corrects the article DOI: 10.1371/journal.pone.0232551.].

Inferring exemplar discriminability in brain representations.

Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of brain activity have used condition-rich designs to sample the stimulus space more densely. To test whether brain response patterns discriminate among a set of stimuli (e.g. exemplars within a category) with good sensitivity, we can pool statistical evidence over all pairwise comparisons. Here we describe a wide range of statistical tests of exemplar discriminability and assess the validity (specificity) and power (sensitivity) of each test. The tests include previously used and novel, parametric and nonparametric tests, which treat subject as a random or fixed effect, and are based on different dissimilarity measures, different test statistics, and different inference procedures. We use simulated and real data to determine which tests are valid and which are most sensitive. A popular test statistic reflecting exemplar information is the exemplar discriminability index (EDI), which is defined as the average of the pattern dissimilarity estimates between different exemplars minus the average of the pattern dissimilarity estimates between repetitions of identical exemplars. The popular across-subject t test of the EDI (typically using correlation distance as the pattern dissimilarity measure) requires the assumption that the EDI is 0-mean normal under H0. Although this assumption is not strictly true, our simulations suggest that the test controls the false-positives rate at the nominal level, and is thus valid, in practice. However, test statistics based on average Mahalanobis distances or average linear-discriminant t values (both accounting for the multivariate error covariance among responses) are substantially more powerful for both random- and fixed-effects inference. Unlike average cross-validated distances, the EDI is sensitive to differences between the distributions associated with different exemplars (e.g. greater variability for some exemplars than for others), which complicates its interpretation. We suggest preferred procedures for safely and sensitively detecting subtle pattern differences between exemplars.

Local opposite orientation preferences in V1: fMRI sensitivity to fine-grained pattern information.

The orientation of a visual grating can be decoded from human primary visual cortex (V1) using functional magnetic resonance imaging (fMRI) at conventional resolutions (2-3 mm voxel width, 3T scanner). It is unclear to what extent this information originates from different spatial scales of neuronal selectivity, ranging from orientation columns to global areal maps. According to the global-areal-map account, fMRI orientation decoding relies exclusively on fMRI voxels in V1 exhibiting a radial or vertical preference. Here we show, by contrast, that 2-mm isotropic voxels in a small patch of V1 within a quarterfield representation exhibit reliable opposite selectivities. Sets of voxels with opposite selectivities are locally intermingled and each set can support orientation decoding. This indicates that global areal maps cannot fully account for orientation information in fMRI and demonstrates that fMRI also reflects fine-grained patterns of neuronal selectivity.

Reliability of dissimilarity measures for multi-voxel pattern analysis.

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries.

Evolution of the Grain Dispersal System in Barley.

About 12,000 years ago in the Near East, humans began the transition from hunter-gathering to agriculture-based societies. Barley was a founder crop in this process, and the most important steps in its domestication were mutations in two adjacent, dominant, and complementary genes, through which grains were retained on the inflorescence at maturity, enabling effective harvesting. Independent recessive mutations in each of these genes caused cell wall thickening in a highly specific grain "disarticulation zone," converting the brittle floral axis (the rachis) of the wild-type into a tough, non-brittle form that promoted grain retention. By tracing the evolutionary history of allelic variation in both genes, we conclude that spatially and temporally independent selections of germplasm with a non-brittle rachis were made during the domestication of barley by farmers in the southern and northern regions of the Levant, actions that made a major contribution to the emergence of early agrarian societies.

Genetic diversity and population structure in a legacy collection of spring barley landraces adapted to a wide range of climates.

Global environmental change and increasing human population emphasize the urgent need for higher yielding and better adapted crop plants. One strategy to achieve this aim is to exploit the wealth of so called landraces of crop species, representing diverse traditional domesticated populations of locally adapted genotypes. In this study, we investigated a comprehensive set of 1485 spring barley landraces (Lrc1485) adapted to a wide range of climates, which were selected from one of the largest genebanks worldwide. The landraces originated from 5° to 62.5° N and 16° to 71° E. The whole collection was genotyped using 42 SSR markers to assess the genetic diversity and population structure. With an average allelic richness of 5.74 and 372 alleles, Lrc1485 harbours considerably more genetic diversity than the most polymorphic current GWAS panel for barley. Ten major clusters defined most of the population structure based on geographical origin, row type of the ear and caryopsis type - and were assigned to specific climate zones. The legacy core reference set Lrc648 established in this study will provide a long-lasting resource and a very valuable tool for the scientific community. Lrc648 is best suited for multi-environmental field testing to identify candidate genes underlying quantitative traits but also for allele mining approaches.

Separating the wheat from the chaff - a strategy to utilize plant genetic resources from ex situ genebanks.

The need for higher yielding and better-adapted crop plants for feeding the world's rapidly growing population has raised the question of how to systematically utilize large genebank collections with their wide range of largely untouched genetic diversity. Phenotypic data that has been recorded for decades during various rounds of seed multiplication provides a rich source of information. Their usefulness has remained limited though, due to various biases induced by conservation management over time or changing environmental conditions. Here, we present a powerful procedure that permits an unbiased trait-based selection of plant samples based on such phenotypic data. Applying this technique to the wheat collection of one of the largest genebanks worldwide, we identified groups of plant samples displaying contrasting phenotypes for selected traits. As a proof of concept for our discovery pipeline, we resequenced the entire major but conserved flowering time locus Ppd-D1 in just a few such selected wheat samples - and nearly doubled the number of hitherto known alleles.

A toolbox for representational similarity analysis.

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).

fMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli.

The orientation of a large grating can be decoded from V1 functional magnetic resonance imaging (fMRI) data, even at low resolution (3-mm isotropic voxels). This finding has suggested that columnar-level neuronal information might be accessible to fMRI at 3T. However, orientation decodability might alternatively arise from global orientation-preference maps. Such global maps across V1 could result from bottom-up processing, if the preferences of V1 neurons were biased toward particular orientations (e.g., radial from fixation, or cardinal, i.e., vertical or horizontal). Global maps could also arise from local recurrent or top-down processing, reflecting pre-attentive perceptual grouping, attention spreading, or predictive coding of global form. Here we investigate whether fMRI orientation decoding with 2-mm voxels requires (a) globally coherent orientation stimuli and/or (b) global-scale patterns of V1 activity. We used opposite-orientation gratings (balanced about the cardinal orientations) and spirals (balanced about the radial orientation), along with novel patch-swapped variants of these stimuli. The two stimuli of a patch-swapped pair have opposite orientations everywhere (like their globally coherent parent stimuli). However, the two stimuli appear globally similar, a patchwork of opposite orientations. We find that all stimulus pairs are robustly decodable, demonstrating that fMRI orientation decoding does not require globally coherent orientation stimuli. Furthermore, decoding remained robust after spatial high-pass filtering for all stimuli, showing that fine-grained components of the fMRI patterns reflect visual orientations. Consistent with previous studies, we found evidence for global radial and vertical preference maps in V1. However, these were weak or absent for patch-swapped stimuli, suggesting that global preference maps depend on globally coherent orientations and might arise through recurrent or top-down processes related to the perception of global form.

Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley.

As early farming spread from the Fertile Crescent in the Near East around 10,000 years before the present, domesticated crops encountered considerable ecological and environmental change. Spring-sown crops that flowered without the need for an extended period of cold to promote flowering and day length-insensitive crops able to exploit the longer, cooler days of higher latitudes emerged and became established. To investigate the genetic consequences of adaptation to these new environments, we identified signatures of divergent selection in the highly differentiated modern-day spring and winter barleys. In one genetically divergent region, we identify a natural variant of the barley homolog of Antirrhinum CENTRORADIALIS (HvCEN) as a contributor to successful environmental adaptation. The distribution of HvCEN alleles in a large collection of wild and landrace accessions indicates that this involved selection and enrichment of preexisting genetic variants rather than the acquisition of mutations after domestication.

FOSE: a framework for open science evaluation.

Pre-publication peer review of scientific literature in its present state suffers from a lack of evaluation validity and transparency to the community. Inspired by social networks, we propose a framework for the open exchange of post-publication evaluation to complement the current system. We first formulate a number of necessary conditions that should be met by any design dedicated to perform open scientific evaluation. To introduce our framework, we provide a basic data standard and communication protocol. We argue for the superiority of a provider-independent framework, over a few isolated implementations, which allows the collection and analysis of open evaluation content across a wide range of diverse providers like scientific journals, research institutions, social networks, publishers websites, and more. Furthermore, we describe how its technical implementation can be achieved by using existing web standards and technology. Finally, we illustrate this with a set of examples and discuss further potential.