A video-based text and equation editor for LaTeX

Cataloged from PDF version of article.In this paper we present a video based text and equation editor for LaTeX. The system recognizes what is written onto paper and generates the LaTeX code. Text and equations are written on a regular paper using a board marker, and a USB camera attached to a computer is used to capture and record the pen-tip positions in each consecutive image frame. Characters and symbols are represented as separate finite state machines (FSMs). They are written in an isolated manner and they are recognized on-line using the FSMs. In the last step, LaTeX code corresponding to recognized characters and symbols is generated. (c) 2007 Elsevier Ltd. All rights reserved

Bird taxonomic and functional responses to land abandonment in wood-pastures

Wood-pastures are socio-ecological systems covering vast areas in Europe. Although used for grazing and production of various forest goods, wood-pastures harbour a rich biodiversity and are usually considered as High Nature Value Farmlands. However, socio-economic pressures are driving the transformation of these valuable landscapes from multi-functional, heterogeneous habitats to homogeneous areas through either intensification or land abandonment. We investigated how changes in management intensity influence the taxonomic diversity, functional diversity and functional composition of birds in these landscapes using generalized linear models. In contrast to taxonomic diversity, functional diversity decreased significantly towards shrub-dominated and less heterogeneous areas related to the abandonment of grazing and/or understory management practices. Grassland and generalist species, and associated guilds such as granivores, ground-nesters and ground-foragers are almost absent less managed areas. On the other hand, shrub-dominated areas favour forest species, particularly understory/canopy foragers and arboreal nesters, although the forest guild is still well-represented in actively managed, heterogeneous areas. Our results indicate the abandonment of wood-pasture management affects the prevalence of grassland and generalist species, leading to functional diversity loss and potentially reduced ecosystem functioning. We suggest non-intensive, active management is needed to maintain habitat heterogeneity and canopy openness, enhancing trait diversity in wood-pastures.Peer reviewe

LncRNA RP11-19E11 is an E2F1 target required for proliferation and survival of basal breast cancer

Long non-coding RNAs (lncRNAs) play key roles in the regulation of breast cancer initiation and progression. LncRNAs are differentially expressed in breast cancer subtypes. Basal-like breast cancers are generally poorly differentiated tumors, are enriched in embryonic stem cell signatures, lack expression of estrogen receptor, progesterone receptor, and HER2 (triple-negative breast cancer), and show activation of proliferation-associated factors. We hypothesized that lncRNAs are key regulators of basal breast cancers. Using The Cancer Genome Atlas, we identified lncRNAs that are overexpressed in basal tumors compared to other breast cancer subtypes and expressed in at least 10% of patients. Remarkably, we identified lncRNAs whose expression correlated with patient prognosis. We then evaluated the function of a subset of lncRNA candidates in the oncogenic process in vitro. Here, we report the identification and characterization of the chromatin-associated lncRNA, RP11-19E11.1, which is upregulated in 40% of basal primary breast cancers. Gene set enrichment analysis in primary tumors and in cell lines uncovered a correlation between RP11-19E11.1 expression level and the E2F oncogenic pathway. We show that this lncRNA is chromatin-associated and an E2F1 target, and its expression is necessary for cancer cell proliferation and survival. Finally, we used lncRNA expression levels as a tool for drug discovery in vitro, identifying protein kinase C (PKC) as a potential therapeutic target for a subset of basal-like breast cancers. Our findings suggest that lncRNA overexpression is clinically relevant. Understanding deregulated lncRNA expression in basal-like breast cancer may lead to potential prognostic and therapeutic applications

dAUTOMAP:decomposing AUTOMAP to achieve scalability and enhance performance

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.Comment: Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #658

Magnetic Resonance Fingerprinting using Recurrent Neural Networks

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. Standard MRF reconstructs parametric maps using dictionary matching and lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a recurrent neural network, which exploits the time-dependent information of the MRF signal evolution. We evaluate our method on multiparametric synthetic signals and compare it to existing MRF map reconstruction approaches, including those based on neural networks. Our method achieves state-of-the-art estimates of T1 and T2 values. In addition, the reconstruction time is significantly reduced compared to dictionary-matching based approaches.Comment: Accepted for ISBI 201