A report on the workshop on complexity in linguistics: Developmental and evolutionary perspectives

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Review of the 9th international conference on the evolution of language (Evolang9)

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Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer’s disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus

The intellectual influence of economic journals: quality versus quantity

The evaluation of scientific output has a key role in the allocation of research funds and academic positions. Decisions are often based on quality indicators for academic journals, and over the years, a handful of scoring methods have been proposed for this purpose. Discussing the most prominent methods (de facto standards) we show that they do not distinguish quality from quantity at article level. The systematic bias we find is analytically tractable and implies that the methods are manipulable. We introduce modified methods that correct for this bias, and use them to provide rankings of economic journals. Our methodology is transparent; our results are replicable

Multitarget Stool DNA Test Performance in an Average-Risk Colorectal Cancer Screening Population

INTRODUCTION: We set out to evaluate the performance of a multitarget stool DNA (MT-sDNA) in an average-risk colonoscopy-controlled colorectal cancer (CRC) screening population. MT-sDNA stool test results were evaluated against fecal immunochemical test (FIT) results for the detection of different lesions, including molecularly defined high-risk adenomas and several other tumor characteristics. METHODS: Whole stool samples (n = 1,047) were prospectively collected and subjected to an MT-sDNA test, which tests for KRAS mutations, NDRG4 and BMP3 promoter methylation, and hemoglobin. Results for detecting CRC (n = 7), advanced precancerous lesions (advanced adenoma [AA] and advanced serrated polyps; n = 119), and non-AAs (n = 191) were compared with those of FIT alone (thresholds of 50, 75, and 100 hemoglobin/mL). AAs with high risk of progression were defined by the presence of specific DNA copy number events as measured by low-pass whole genome sequencing. RESULTS: The MT-sDNA test was more sensitive than FIT alone in detecting advanced precancerous lesions (46% (55/119) vs 27% (32/119), respectively, P < 0.001). Specificities among individuals with nonadvanced or negative findings (controls) were 89% (791/888) and 93% (828/888) for MT-sDNA and FIT testing, respectively. A positive MT-sDNA test was associated with multiple lesions (P = 0.005), larger lesions (P = 0.03), and lesions with tubulovillous architecture (P = 0.04). The sensitivity of the MT-sDNA test or FIT in detecting individuals with high-risk AAs (n = 19) from individuals with low-risk AAs (n = 52) was not significantly different. DISCUSSION: In an average-risk screening population, the MT-sDNA test has an increased sensitivity for detecting advanced precancerous lesions compared with FIT alone. AAs with a high risk of progression were not detected with significantly higher sensitivity by MT-sDNA or FIT

Diffusion Weighted Image Denoising using overcomplete Local PCA

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. This work has been also partially supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Manjón Herrera, JV.; Coupé, P.; Concha, L.; Buades, A.; Collins, L.; Robles Viejo, M. (2013). Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE. 8(9):1-12. https://doi.org/10.1371/journal.pone.0073021S11289Sundgren, P. C., Dong, Q., Gómez-Hassan, D., Mukherji, S. K., Maly, P., & Welsh, R. (2004). Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology, 46(5), 339-350. doi:10.1007/s00234-003-1114-xJohansen-Berg, H., & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. 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Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

On the Link between Gaussian Homotopy Continuation and Convex Envelopes

Abstract. The continuation method is a popular heuristic in computer vision for nonconvex optimization. The idea is to start from a simpli-fied problem and gradually deform it to the actual task while tracking the solution. It was first used in computer vision under the name of graduated nonconvexity. Since then, it has been utilized explicitly or im-plicitly in various applications. In fact, state-of-the-art optical flow and shape estimation rely on a form of continuation. Despite its empirical success, there is little theoretical understanding of this method. This work provides some novel insights into this technique. Specifically, there are many ways to choose the initial problem and many ways to progres-sively deform it to the original task. However, here we show that when this process is constructed by Gaussian smoothing, it is optimal in a specific sense. In fact, we prove that Gaussian smoothing emerges from the best affine approximation to Vese’s nonlinear PDE. The latter PDE evolves any function to its convex envelope, hence providing the optimal convexification