Sotirios A. Tsaftaris

Dominant β-catenin mutations cause intellectual disability with recognizable syndromic features.

The recent identification of multiple dominant mutations in the gene encoding β-catenin in both humans and mice has enabled exploration of the molecular and cellular basis of β-catenin function in cognitive impairment. In humans, β-catenin mutations that cause a spectrum of neurodevelopmental disorders have been identified. We identified de novo β-catenin mutations in patients with intellectual disability, carefully characterized their phenotypes, and were able to define a recognizable intellectual disability syndrome. In parallel, characterization of a chemically mutagenized mouse line that displays features similar to those of human patients with β-catenin mutations enabled us to investigate the consequences of β-catenin dysfunction through development and into adulthood. The mouse mutant, designated batface (Bfc), carries a Thr653Lys substitution in the C-terminal armadillo repeat of β-catenin and displayed a reduced affinity for membrane-associated cadherins. In association with this decreased cadherin interaction, we found that the mutation results in decreased intrahemispheric connections, with deficits in dendritic branching, long-term potentiation, and cognitive function. Our study provides in vivo evidence that dominant mutations in β-catenin underlie losses in its adhesion-related functions, which leads to severe consequences, including intellectual disability, childhood hypotonia, progressive spasticity of lower limbs, and abnormal craniofacial features in adults.

Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and performance) as a result of genotype differences (i.e., differences in the genetic code) and the environmental conditions to which a plant has been exposed [1]?[3]. According to the Food and Agriculture Organization of the United Nations, large-scale experiments in plant phenotyping are a key factor in meeting the agricultural needs of the future to feed the world and provide biomass for energy, while using less water, land, and fertilizer under a constantly evolving environment due to climate change. Working on model plants (such as Arabidopsis), combined with remarkable advances in genotyping, has revolutionized our understanding of biology but has accelerated the need for precision and automation in phenotyping, favoring approaches that provide quantifiable phenotypic information that could be better used to link and find associations in the genotype [4]. While early on, the collection of phenotypes was manual, currently noninvasive, imaging-based methods are increasingly being utilized [5], [6]. However, the rate at which phenotypes are extracted in the field or in the lab is not matching the speed of genotyping and is creating a bottleneck [1].

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (

Explicit Shift-Invariant Dictionary Learning

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Low-Complexity Tracking-Aware H.264 Video Compression for Transportation Surveillance

>90xa0% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://www.plant-phenotyping.org/datasets) to support future challenges beyond segmentation within this application domain.

Service Clustering for Autonomic Clouds Using Random Forest

Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors learned in an unsupervised fashion to leaf counts using a supervised regression model. To take advantage of the circular and coplanar arrangement of leaves and also to introduce scale and rotation invariance, we learn features in a log-polar representation. Image patches extracted in this log-polar domain are provided to K-means, which builds a codebook in a unsupervised manner. Feature codes are obtained by projecting patches on the codebook using the triangle encoding, introducing both sparsity and specifically designed representation. A global, per-plant image descriptor is obtained by pooling local features in specific regions of the image. Finally, we provide the global descriptors to a support vector regression framework to estimate the number of leaves in a plant. We evaluate our method on datasets of the textit{Leaf Counting Challenge} (LCC), containing images of Arabidopsis and tobacco plants. Experimental results show that on average we reduce absolute counting error by 40% w.r.t. the winner of the 2014 edition of the challenge -a counting via segmentation method. When compared to state-of-the-art density-based approaches to counting, on Arabidopsis image data ~75% less counting errors are observed. Our findings suggest that it is possible to treat leaf counting as a regression problem, requiring as input only the total leaf count per training image.

Active contour model driven by Globally Signed Region Pressure Force

In this letter we give efficient solutions to the construction of structured dictionaries for sparse representations. We study circulant and Toeplitz structures and give fast algorithms based on least squares solutions. We take advantage of explicit circulant structures and we apply the resulting algorithms to shift-invariant learning scenarios. Synthetic experiments and comparisons with state-of-the-art methods show the superiority of the proposed methods.

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

We found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2].